1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge - Kreuzer, Matthias; Kellermann, Walter (2023 IEEE International Conference on Prognostics and Health Management, ICPHM 2023, 2023)
In this article, we present our contribution to the International Conference on Prognostics and Health Management (ICPHM) 2023 Data Challenge on Industrial Systems' Health Monitoring using Vibration Analysis. For the task of classifying sun gear faults in a gearbox, we propose a residual Convolutive Neural Network (CNN) that operates on raw three-channel time-domain vibration signals. In conjunction with data augmentation and regu-larization techniques, the proposed model yields very good results in a multi-class classification scenario with real-world data despite its relatively small size, i.e., with less than 30,000 trainable parameters. Even when presented with data obtained from multiple operating conditions, the network is still capable to accurately predict the condition of the gearbox under inspection.
CRIS-Team Scopus Importer:2023-09-01
Keywords: Condition Monitoring; Fault Classification; IPCHM 2023 Data Challenge; Planetary Gearbox; Vibration Analysis

1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization Techniques for the ICPHM 2023 Data Challenge - Kreuzer, Matthias; Kellermann, Walter (2023 IEEE International Conference on Prognostics and Health Management (ICPHM), 2023)
In this article, we present our contribution to the International Conference on Prognostics and Health Management (ICPHM) 2023 Data Challenge on Industrial Systems’ Health Monitoring using Vibration Analysis. For the task of classifying sun gear faults in a gearbox, we propose a residual Convolutive Neural Network (CNN) that operates on raw three-channel time-domain vibration signals. In conjunction with data augmentation and regularization techniques, the proposed model yields very good results in a multi-class classification scenario with real-world data despite its relatively small size, i.e., with less than 30,000 trainable parameters. Even when presented with data obtained from multiple operating conditions, the network is still capable to accurately predict the condition of the gearbox under inspectio
Keywords: IPCHM 2023 Data Challenge; Vibration Analysis; Condition Monitoring; Fault Classification; Planetary Gearbox

2D frequency selective extrapolation for spatial error concealment in H.264/AVC video coding - Meisinger, Katrin; Kaup, André (2006 IEEE International Conference on Image Processing, ICIP 2006, 2006)
The frequency selective extrapolation extends an image signal beyond a limited number of known samples. This problem arises in image and video communication in error prone environments where transmission errors may lead to data losses. In order to estimate the lost image areas, the missing pixels are extrapolated from the available correctly received surrounding area which is approximated by a weighted linear combination of basis functions. In this contribution, we integrate the frequency selective extrapolation into the H.264/AVC coder as spatial concealment method. The decoder reference software uses spatial concealment only for I frames. Therefore, we investigate the performance of our concealment scheme for I frames and its impact on following P frames caused by error propagation due to predictive coding. Further, we compare the performance for coded video sequences in TV quality against the non-normative concealment feature of the decoder reference software. The investigations are done for slice patterns causing chequerboard and raster scan losses enabled by Flexible Macroblock Ordering (FMO). ©2006 IEE
Keywords: Error concealment; Signal extrapolation; Video coding; Video communication

2D sound-source localization on the binaural manifold - Deleforge, Antoine; Horaud, Radu (IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2012, 2012)
The problem of 2D sound-source localization based on a robotic binaural setup and audio-motor learning is addressed. We first introduce a methodology to experimentally verify the existence of a locally-linear bijective mapping between sound-source positions and high-dimensional interaural data, using manifold learning. Based on this local linearity assumption, we propose an novel method, namely probabilistic piecewise affine regression, that learns the localization-to-interaural mapping and its inverse. We show that our method outperforms two state-of-the art mapping methods, and allows to achieve accurate 2D localization of natural sounds from real world binaural recording
LMS::1519

3D-deblocking for error concealment in block-based video decoding systems - Friebe, Markus; Kaup, André (25th PCS: Picture Coding Symposium 2006, PCS2006, 2006)
In this contribution we propose a 3D-deblocking method for macroblock loss recovery in block-based video decoding systems. For a lost macroblock, the motion vector is estimated. Using the estimated motion vector a deblocking filter recovers the lost macroblock by the corresponding motion compensated image samples of the previous frame. Macroblock borders are filtered, if block artifacts are estimated by this deblocking filter. It is shown that in case of transmission errors this restoration technique can successfully be used in block-based video decoding system
Keywords: Error concealment; Video coding and transmission; Video restoration

3-D Image models and compression - Synthetic hybrid or natural fit? - Girod, Bernd; Eisert, Peter; Magnor, Marcus; Steinbach, Eckehard; Wiegand, Thomas (Proc. 3rd International Workshop on Synthetic and Natural Hybrid Coding and Three-Dimensional Imaging, 1999)

3-D Image models and compression - Synthetic hybrid or natural fit? - Girod, Bernd; Eisert, Peter; Magnor, Marcus; Steinbach, Eckehard; Wiegand, Thomas (Proceedings - International Conference on Image Processing, ICIP, 1999)

3-D mesh compensated wavelet lifting for 3-D+t medical CT data - Schnurrer, Wolfgang; Richter, Thomas; Seiler, Jürgen; Herglotz, Christian; Kaup, André (IEEE International Conference on Image Processing, 2014)
For scalable coding, a high quality of the lowpass band of a wavelet transform is crucial when it is used as a downscaled version of the original signal. However, blur and motion can lead to disturbing artifacts. By incorporating feasible compensation methods directly into the wavelet transform, the quality of the lowpass band can be improved. The displacement in dynamic medical 3-D+t volumes from Computed Tomography is mainly given by expansion and compression of tissue over time and can be modeled well by mesh-based methods. We extend a 2-D mesh-based compensation method to three dimensions to obtain a volume compensation method that can additionally compensate deforming displacements in the third dimension. We show that a 3-D mesh can obtain a higher quality of the lowpass band by 0.28 dB with less than 40% of the model parameters of a comparable 2-D mesh. Results from lossless coding with JPEG 2000 3D and SPECK3D show that the compensated subbands using a 3-D mesh need about 6% less data compared to using a 2-D mes
Keywords: Computed Tomography; Discrete Wavelet Transforms; Motion Compensation; Scalability; Signal Analysis

3-D Reconstruction of Real World Objects Using Extended Voxels - Steinbach, Eckehard; Eisert, Peter; Betz, A.; Girod, Bernd (ICIP 2000, 2000)
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3D Rendering Framework for Data Augmentation in Optical Character Recognition - Spruck, Andreas; Hawesch, Maximiliane; Maier, Anatol; Rieß, Christian; Seiler, Jürgen; Kaup, André (ISSCS 2021 - International Symposium on Signals, Circuits and Systems, 2021)
In this paper, we propose a data augmentation framework for Optical Character Recognition (OCR). The proposed framework is able to synthesize new viewing angles and illumination scenarios, effectively enriching any available OCR dataset. Its modular structure allows to be modified to match individual user requirements. The framework enables to comfortably scale the enlargement factor of the available dataset. Furthermore, the proposed method is not restricted to single frame OCR but can also be applied to video OCR. We demonstrate the performance of our framework by augmenting a 15% subset of the common Brno Mobile OCR dataset. Our proposed framework is capable of leveraging the performance of OCR applications especially for small datasets. Applying the proposed method, improvements of up to 2.79 percentage points in terms of Character Error Rate (CER), and up to 7.88 percentage points in terms of Word Error Rate (WER) are achieved on the subset. Especially the recognition of challenging text lines can be improved. The CER may be decreased by up to 14.92 percentage points and the WER by up to 18.19 percentage points for this class. Moreover, we are able to achieve smaller error rates when training on the 15% subset augmented with the proposed method than on the original nonaugmented full dataset.
CRIS-Team Scopus Importer:2021-09-17

3-D Shape Reconstruction from Light Fields Using Voxel Back-Projection - Eisert, Peter; Steinbach, Eckehard; Girod, Bernd (Vision, Modeling, and Visualization Workshop 99, 1999)
LMS::288

3MT Competition (EUSIPCO2024): A peek into the black box: Insights into the functionality of complex-valued neural networks for multichannel speech enhancement - Briegleb, Annika (Science Talks, 2025)
Artificial neural networks (ANNs) have become an important part of signal processing research. While ANNs outperform model-based signal processing methods in many applications, their internal processing often remains unclear. In this thesis, a framework for analyzing the signal processing performed by ANN-based filters for multichannel speech enhancement is proposed. By designing specific training and test scenarios that allow to associate each time frame with certain information, e.g., spatial cues, and using low-cost analysis tools such as clustering, interpretable information can be extracted from the hidden features of the ANN. The proposed framework allows to assess whether and where spatial information is represented inside the ANN, answering the question whether these ANNs exploit spatial cues in addition to spectral information. Furthermore, the impact of the choice of training target on the functionality and interpretability of the ANN is considered. By applying the proposed analysis tools to two conceptually different speech enhancement frameworks, it is shown that the amount of spatial information extracted inside the ANN varies depending on the training target and the test scenario. The insights from this thesis help to assess the signal processing capabilities of ANNs and allow to make informed decisions when configuring, training, and deploying ANNs.
CRIS-Team Scopus Importer:2025-02-17
Keywords: DNN interpretability; Explainable AI; Multichannel speech enhancement; Spatial filtering

4-D frequency selective extrapolation for error concealment in multi-view video - Fecker, Ulrich; Seiler, Jürgen; Kaup, André (2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008, 2008)
Practical applications for multi-view video such as Three-Dimensional Television may require to transmit the data over error-prone channels. Even when channel coding is used, it is likely that parts of the image information are lost after decoding. To improve the image quality in this case, an efficient algorithm for multi-view error concealment is presented. Extending and enhancing a previously known method for single-view concealment, the algorithm simultaneously uses information from surrounding image parts, from temporally preceding and succeeding frames and from neighbouring camera views for extrapolating known image samples into the lost area. It is shown that the proposed algorithm leads to convincing results in terms of PSNR as well as to a good subjective quality. Compared to single-view concealment, the result is improved by up to 0.75 dB by adding cross-view information. © 2008 IEE

4D scalable multi-view video coding using disparity compensated view filtering and motion compensated temporal filtering - Garbas, Jens-Uwe; Fecker, Ulrich; Tröger, Tobias; Kaup, André (2006 IEEE 8th Workshop on Multimedia Signal Processing, MMSP 2006, 2006)
In this paper, a novel framework for scalable multiview video coding is described. A well known wavelet based scalable coding scheme for single-view video sequences has been adopted and extended to match the specific needs of scalable multi-view video coding. Motion compensated temporal filtering (MCTF) is applied to each video sequence of each camera. The use of a wavelet lifting structure guarantees perfect invertibility of this step, and as a consequence of its open-loop architecture, SNR and temporal scalability are attained. Correlations between the temporal subbands of adjacent cameras are reduced by a novel disparity compensated view filtering (DCVF), method which is also lifting based and open-loop to enable view scalability. Spatial scalability and entropy coding are achieved by the JPEG2000 spatial wavelet transform and EBCOT coding, respectively. Rate allocation along the temporal-view-filtered subbands is done by means of an RD-optimal algorithm. Experimental results show the high scaling capability in terms of SNR, temporal and view scalability. © 2006 IEE

A 3D acoustic simulation program with graphical frontend for scene input - Kuntz, Achim; Rabenstein, Rudolf (Audio Engineering Society (AES) 120th Convention, 2006)
A program for full three-dimensional simulation of sound propagation in enclosures is presented which interfaces to a graphical interface for intuitive setup of complex simulation scenes. The simulation algorithm is based on the wave digital filtering principle, allowing for arbitrary reflection coeffcients at object boundaries and walls for realistic results. Simulation scenes are defined in an object oriented way. As a graphical user interface to the simulation program a modeler front-end for a raytracing program is used. Simulation setups can thus be built by graphically placing objects in the scene. Being open source, the proposed modeler can easily be customized if required. Simulation results are shown for several example setups demonstrating the possibilities of our approac

A Bayesian network approach to linear and nonlinear acoustic echo cancellation - Hümmer, Christian; Maas, Roland; Hofmann, Christian; Kellermann, Walter (EURASIP Journal on Advances in Signal Processing, 2015)
This article provides a general Bayesian approach to the tasks of linear and nonlinear acoustic echo cancellation (AEC). We introduce a state-space model with latent state vector modeling all relevant information of the unknown system. Based on three cases for defining the state vector (to model a linear or nonlinear echo path) and its mathematical relation to the observation, it is shown that the normalized least mean square algorithm (with fixed and adaptive stepsize), the Hammerstein group model, and a numerical sampling scheme for nonlinear AEC can be derived by applying fundamental techniques for probabilistic graphical models. As a consequence, the major contribution of this Bayesian approach is a unifying graphical-model perspective which may serve as a powerful framework for future work in linear and nonlinear AE
Keywords: Acoustic echo cancellation; Bayesian networks; Graphical models

A Bayesian Network Approach to Selected Problems in Speech Signal Processing - Hümmer, Christian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
The application of machine learning techniques to signal processing tasks has become of increasing interest in recent years. In particular, directed graphical models, named Bayesian networks, have shown to provide a powerful framework for deriving links between existing and new algorithms from a generalized point of view. This motivates to exploit a systematic Bayesian network approach in this thesis which is described as follows. A sequence of real-world observations is modeled to be produced by a set of latent random variables with unknown statistics. The underlying process producing the observations is described by a probabilistic model and its Bayesian network representation. This is the basis for acquiring information about the latent random variables by applying the steps of inference and decision. The described machine learning methodology will be consistently used to address two distinct speech signal processing tasks from a unifying Bayesian network perspective. First, the problem of single-channel Nonlinear Acoustic Echo Cancellation (NAEC) is considered with the goal to remove the acoustic coupling between a loudspeaker and a microphone. This leads to the derivation of the NLMS algorithms with fixed and optimum adaptive stepsize value as special cases of the Kalman filter. Furthermore, the Elitist Particle Filter based on Evolutionary Strategies (EPFES) is introduced as a new algorithm to estimate the parameters of a nonlinear acoustic echo path model. The experimental results for a synthesized scenario and real smartphone recordings illustrate that the EPFES is a promising method for NAEC. As a second application, the task of environmentally-robust Automatic Speech Recognition (ASR) is addressed by modeling acoustic features to be random variables instead of deterministic point estimates. This model is taken into account by modifying the acoustic-model scoring during the recognition phase. To this end, both a well-known and a new uncertainty decoding strategy are derived from a unifying Bayesian network perspective. The experimental evaluation shows that applying the proposed uncertainty decoding concept improves the recognition accuracy achieved by a powerful deep neural network-based ASR syste

A Bayesian network view on linear and nonlinear acoustic echo cancellation - Maas, Roland; Hümmer, Christian; Schwarz, Andreas; Hofmann, Christian; Kellermann, Walter (2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014, 2014)
In this contribution, we provide a new derivation of the normalized least mean square (NLMS) algorithm from a machine learning perspective. By applying the inference rules of Bayesian networks to a linear observation model, the NLMS can be shown to arise as a modification of the Kalman filter equations. Based on a nonlinear observationmodel, we exemplify the benefit of the Bayesian point of view by employing the technique of particle filtering to realize a tractable algorithm for nonlinear acoustic echo cancellation. Experiments carried out on real smartphone recordings reveal the remarkable performance of the new approac
Keywords: adaptive filtering; Bayesian networks; machine learning for signal processing; nonlinear acoustic echo cancellation; system identification

A Bayesian view on acoustic model-based techniques for robust speech recognition - Maas, Roland; Hümmer, Christian; Sehr, Armin; Kellermann, Walter (EURASIP Journal on Advances in Signal Processing, 2015)
This article provides a unifying Bayesian view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By identifying and converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules. We thus summarize the various approaches as approximations or modifications of the same Bayesian decoding rule leading to a unified view on known derivations as well as to new formulations for certain approache
Keywords: Bayesian network; Missing feature; Model adaptation; Robust automatic speech recognition; Uncertainty decoding

A bilinear framework for adaptive speech dereverberation combining beamforming and linear prediction - Yang, Wenxing; Huang, Gongping; Brendel, Andreas; Chen, Jingdong; Benesty, Jacob; Kellermann, Walter; Cohen, Israel (International Workshop in Acoustic Signal Enhancement (IWAENC), 2022)

A Bit Stream Feature-Based Energy Estimator for HEVC Software Encoding - Ramasubbu, Geetha; Kaup, André; Herglotz, Christian (Picture Coding Symposium (PCS 2022), 2022)
The total energy consumption of today's video coding systems is globally significant and emphasizes the need for sustainable video coder applications. To develop such sustainable video coders, the knowledge of the energy consumption of state-of-the-art video coders is necessary. For that purpose, we need a dedicated setup that measures the energy of the encoding and decoding system. However, such measurements are costly and laborious. To this end, this paper presents an energy estimator that uses a subset of bit stream features to accurately estimate the energy consumption of the HEVC software encoding process. The proposed model reaches a mean estimation error of 4.88video coding, energy-efficiency, energy estimator, HEVC, bit stream features% when averaged over presets of the x265 encoder implementation. The results from this work help to identify properties of encoding energy-saving bit streams and, in turn, are useful for developing new energy-efficient video coding algorithms.<br
Keywords: video coding; energy-efficiency; energy estimator; HEVC; bit stream features

A Bitstream Feature Based Model for Video Decoding Energy Estimation - Herglotz, Christian; Wen, Yongjun; Dai, Bowen; Kränzler, Matthias; Kaup, André (Picture Coding Symposium (PCS), 2016)
In this paper we show that a small amount of bit stream features can be used to accurately estimate the energy consumption of state-of-the-art software and hardware accelerated decoder implementations for four different video codecs. By testing the estimation performance on HEVC, H.264, H.263, and VP9 we show that the proposed model can be used for any hybrid video codec. We test our approach on a high amount of different test sequences to prove the general validity. We show that less than 20 features are sufficient to obtain mean estimation errors that are smaller than 8%. Finally, an example will show the performance trade-offs in terms of rate, distortion, and decoding energy for all tested codec

A Blind Watermarking Scheme Based on Structured Codebooks - Eggers, Joachim; Su, Jonathan; Girod, Bernd (IEE Colloquium: SECURE::IMAGES AND IMAGE AUTHENTICATION, 2000)
LMS::141

A calibration method for robust generalized sidelobe cancelling beamformers - Oak, Parijat; Kellermann, Walter (Proc. Intl. Workshop on Acoustic Echo and Noise Control (IWAENC), 2005)
LMS::556

Accelerated Hybrid Image Reconstruction for Non-Regular Sampling Color Sensors - Bätz, Michel; Eichenseer, Andrea; Jonscher, Markus; Seiler, Jürgen; Kaup, André (IEEE International Conference on Visual Communications and Image Processing (VCIP), 2014)
Increasing the spatial resolution is an ongoing research topic in image processing. A recently presented approach applies a non-regular sampling mask on a low resolution sensor and subsequently reconstructs the masked area via an extrapolation algorithm to obtain a high resolution image. This paper introduces an acceleration of this approach for use with full color sensors. Instead of employing the effective, yet computationally expensive extrapolation algorithm on each of the three RGB channels, a color space conversion is performed and only the luminance channel is then reconstructed using this algorithm. As natural images contain much less information in the chrominance channels, a fast linear interpolation technique can here be used to accelerate the whole reconstruction procedure. Simulation results show that an average speed up factor of 2.9 is thus achieved, while the loss in visual quality stays imperceptible. Comparisons of PSNR results confirm thi
Keywords: image reconstruction; non-regular sampling; signal extrapolation; color image sensor; color space conversion

Accelerating Auxiliary Function-based Independent Vector Analysis - Brendel, Andreas; Kellermann, Walter (IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021)

Accuracy Limits for 3-D Rigid Body Motion Estimation under Perspective Projection - Steinbach, Eckehard; Betz, A.; Girod, Bernd (Proc. 10th IEEE Workshop on Image and Multidimensional Signal Processing IMDSP 98, 1998)
LMS::345

Accuracy of time-difference-of-arrival based source localization algorithms under temperature variations - Annibale, Paolo; Rabenstein, Rudolf (4th International Symposium on Communications, Control, and Signal Processing, ISCCSP-2010, 2010)
Time-differences-of-arrival measurements play a relevant role in localizing and tracking of acoustic sources. Though a huge literature has been devoted to this topic the influence of the propagation speed on the localization accuracy has been largely disregarded. The sound speed is usually supposed to be exactly known, but actually this information is far from being available in real situations, where the environment conditions are uncontrolled and might significantly change. Therefore the accuracy of the existing algorithms under temperature variations and the consequent propagation speed deviation is of interest. Two well known closed-form localization methods are reviewed under the hypothesis that the sound speed is wrongly assumed due to unknown temperature variations. After a mathematical treatment, simulation and measurement results are shown. ©2010 IEE

A Channel Model for Desynchronization Attacks on Watermarks - Huber, J.; Eggers, Joachim; Bäuml, R. (Proceedings of SPIE: Electronic Imaging 2002,::Security and Watermarking of Multimedia Contents IV, 2002)
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A Channel Model for Watermarks Subject to Desynchronization Attacks - Huber, J.; Eggers, Joachim; Bäuml, R. (4th International ITG Conference on::Source and Channel Coding, 2002)
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A Combined Approach for Estimating a Feature-Domain Reverberation Model in Non-diffuse Environments - Sehr, Armin; Wen, Jimi Y. C.; Kellermann, Walter; Naylor, Patrick A. (Proc. Int. Workshop on Acoustic Echo and Noise Control (IWAENC), 2008)
A combined approach for estimating a feature-domain reverbera-tion model suitable for the robust distant-talking automatic speech recognition concept REMOS (REverberation MOdeling for Speech recognition) [1] is proposed. Based on a few calibration utterances recorded in the target environment, the combined approach employs ML estimation and blind estimation of the reverberation time to de-termine a two-slope reverberation model. Since measurements of room impulse responses become unnecessary, the effort for training is greatly reduced compared to [1] and compared to training HMMs on artificially reverberated data. Connected digit recognition exper-iments show that the proposed reverberation models in connection with the REMOS concept significantly outperform HMM-based rec-ognizers trained on reverberant dat
LMS::765

A Communications Approach to Image Steganography - Eggers, Joachim; Bäuml, R.; Girod, Bernd (Proceedings of SPIE: Electronic Imaging 2002, Security and Watermarking of Multimedia Contents IV, 2002)
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A Comparative Analysis of the Time and Energy Demand of Versatile Video Coding and High Efficiency Video Coding Reference Decoders - Kränzler, Matthias; Herglotz, Christian; Kaup, André (IEEE International Workshop on Multimedia Signal Processing (MMSP), 2020)
This paper investigates the decoding energy and decoding time demand of VTM-7.0 in relation to HM-16.20. We present the first detailed comparison of two video codecs in terms of software decoder energy consumption. The evaluation shows that the energy demand of the VTM decoder is increased significantly compared to HM and that the increase depends on the coding configuration. For the coding configuration randomaccess, we find that the decoding energy is increased by over 80% at a decoding time increase of over 70%. Furthermore, results indicate that the energy demand increases by up to 207% when Single Instruction Multiple Data (SIMD) instructions are disabled, which corresponds to the HM implementation style. By measurements, it is revealed that the coding tools MIP, AMVR, TPM, LFNST, and MTS increase the energy efficiency of the decoder. Furthermore, we propose a new coding configuration based on our analysis, which reduces the energy demand of the VTM decoder by over 17% on averag
Keywords: VVC; Energy; Analysis; Complexity; SIMD

A Comparison of Nonstaggered Compact FDTD Schemes for the 3D Wave Equation - Kowalczyk, Konrad; van Walstijn, M. (Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2010)
This paper aims at providing a better insight into the 3D approximations of the wave equation using compact finite-difference time-domain (FDTD) schemes in the context of room acoustic simulations. A general family of 3D compact explicit and implicit schemes based on a nonstaggered rectilinear grid is analyzed in terms of stability, numerical error, and accuracy. Various special cases are compared and the most accurate explicit and implicit schemes are identified. Further considerations presented in the paper include the direct relationship with other numerical approaches found in the literature on room acoustic modeling such as the 3D digital waveguide mesh and Yee's staggered grid techniqu
LMS::939
Keywords: Acoustic propagation; acoustic signal processing; architectural acoustics; finite-difference time-domain (FDTD) methods

A Comprehensive Review of Software and Hardware Energy Efficiency of Video Decoders - Kränzler, Matthias; Herglotz, Christian; Kaup, André (Picture Coding Symposium (PCS), 2024)

A compressed domain change detection algorithm for RTP streams in video surveillance applications - Laumer, Marcus; Amon, P.; Hutter, A.; Kaup, André (3rd IEEE International Workshop on Multimedia Signal Processing, MMSP 2011, 2011)
This paper presents a novel change detection algorithm for the compressed domain. Many video surveillance systems in practical use transmit their video data over a network by using the Real-time Transport Protocol (RTP). Therefore, the presented algorithm concentrates on analyzing RTP streams to detect major changes within contained video content. The paper focuses on a reliable preselection for further analysis modules by decreasing the number of events to be investigated. The algorithm is designed to work on scenes with mainly static background, like in indoor video surveillance streams. The extracted stream elements are RTP timestamps and RTP packet sizes. Both values are directly accessible by efficient byte-reading operations without any further decoding of the video content. Hence, the proposed approach is codec-independent, while at the same time its very low complexity enables the use in extensive video surveillance systems. About 40,000 frames per second of a single RTP stream can be processed on an Intel® Core™ 2 Duo CPU at 2 GHz and 2 GB RAM, without decreasing the efficiency of the algorithm. © 2011 IEE

A Content-Dependent Fast DCT for Low Bit-Rate Video Coding - Girod, Bernd; Stuhlmüller, Klaus (Int. Conf. on Imag. Proc., 1998)
LMS::337

A Continuous Frequency Domain Description of Adjustable Boundary Conditions for Multidimensional Transfer Function Models - Schäfer, Maximilian; Rabenstein, Rudolf (20th International Conference on Digital Audio Effects (DAFx-17), 2017)
Physical modeling of string vibrations strongly depends on the conditions at the system boundaries. The more complex the boundary conditions are the more complex is the process of physical modeling. Based on prior works, this contribution derives a general concept for the incorporation of complex boundary conditions into a transfer function model designed with simple boundary conditions. The concept is related to control theory and separates the treatment of the boundary conditions from the design of the string model.

Acoustic Blind Source Separation in Reverberant and Noisy Environments, PhD thesis - Aichner, Robert (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2007)
In the last few years blind source separation techniques have received a great deal of attention in the signal processing community. One reason is that blind source separation approaches only rely on the assumption of mutual statistical independence of the source signals and do not need additional a-priori information, such as the sensor array geometry or the positions of the desired and interfering sources. Moreover, several separated point sources are retrieved simultaneously instead of extracting one desired source as common in beamforming. In this work we focus on acoustic signals and present several important special cases of a generic blind source separation framework which was termed TRINICON ("TRIple-N Independent component analysis for CONvolutive mixtures"). This framework is based on an information-theoretic criterion allowing to incorporate also higher-order statistics into the adaptation algorithms, which is in contrast to beamforming methods solely relying on second-order statistics. Moreover, this framework allows a unified view on convolutive blind source separation algorithms leading to novel algorithms and showing also relationships to popular state-of-the-art algorithms. After explaining the framework, we present some approximations which lead to highly efficient algorithms while still preserving the superior properties of the general framework relative to other known algorithms. A second major contribution is that, beyond existing blind source separation literature, we address the application of blind source separation to reverberant and noisy environments by presenting several pre- and post-processing techniques in a coherent treatment. The algorithms allow to maintain a high separation performance of the blind source separation algorithms also in noisy scenarios and additionally, are capable of suppressing the undesired diffuse background noise which cannot be treated by the convolutive blind source separation model. Experimental results demonstrate the applicability of the proposed approaches in real-world environments such as reverberant rooms, which may also exhibit background noise, and in the particularly significant case of noisy passenger compartment
Keywords: acoustic signal processing; blind source separation; convolutive mixtures; higher-order statistics; second-order statistics

Acoustic Echo Cancellation for Beamforming Microphone Arrays - Kellermann, Walter (Microphone Arrays, 2001)

Acoustic echo cancellation for natural speech communication at the human-machine interface - Kellermann, Walter (Proc. Int. Workshop on Hands-Free Speech Communication (HSC), 2001)
LMS::142

Acoustic Echo Cancellation for Surround Sound Using Perceptually Motivated Convergence Enhancement - Herre, Jürgen; Buchner, Herbert; Kellermann, Walter (2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, 2007)
Acoustic Echo Cancellation (AEC) has become an essential and wellknown enabling technology for hands-free communication and human-machine interfaces. AEC for two or more reproduction channels aims at identifying the echo paths between the microphone and each audio reproduction source in order to cancel the associated echo contribution. A number of preprocessing methods have been proposed to decorrelate stereo audio signals in order to enable an unambiguous identification of each echo path and to thus ensure robustness to changing sound source locations. While several of these methods provide enough decorrelation to achieve proper AEC convergence in the stereo case, considerations of subjective sound quality have frequently not been addressed adequately. This paper compares the performance of several methods in terms of both convergence speed and aspects of sound perception, and proposes a novel signal decorrelation approach with attractive properties. The superior performance of the proposed method is demonstrated for 5.1 surround sound reproduction. © 2007 IEE
herre_papers_spatial_audio_coding_and_3d_audio_coding
Keywords: Acoustic Echo Cancellation; AEC; Multi-channel sound; Perception; Phase modulation; Psychoacoustics; Surround sound

Acoustic Echo Cancellation for Two and More Reproduction Channels - Buchner, Herbert; Kellermann, Walter (Conf. Rec. Int. Workshop on Acoustic Echo and Noise Control (IWAENC), 2001)
LMS::173

Acoustic Echo Control for Humanoid Robots - El-Rayyes, Adel; Löllmann, Heinrich; Hofmann, Christian; Kellermann, Walter (42. Deutsche Jahrestagung für Akustik (DAGA), 2016)

Acoustic quality enhancement in mobile radio communications applications for public emergency services - Liria, A.; Masi, M.; Muriel, J.A.; Palou, M.; Aichner, Robert; Samuelsson, J. (Proc. Int. Conf. Telecom I+D, 2003)
LMS::446

Acoustic reciprocity: An extension to spherical harmonics domain - Samarasinghe, Prasanga; Abhayapala, Thushara D.; Kellermann, Walter (The Journal of the Acoustical Society of America, 2017)
Acoustic reciprocity is a fundamental property of acoustic wavefields that is commonly used to simplify the measurement process of many practical applications. Traditionally, the reciprocity theorem is defined between a monopole point source and a point receiver. Intuitively, it must apply to more complex transducers than monopoles. In this paper, the authors formulate the acoustic reciprocity theory in the spherical harmonics domain for directional sources and directional receivers with higher order directivity pattern

Acoustic Rendering of Buildings - Rabenstein, Rudolf; Schips, O.; Stenger, Alex (Proc. 5th Int. IBPSA Conference Building Simulation, 1997)
LMS::404

Acoustic Self-awareness of Autonomous Systems in a World of Sounds - Schmidt, Alexander; Löllmann, Heinrich; Kellermann, Walter (Proceedings of the IEEE, 2020)
Autonomous systems (ASs) operating in real-world environments are exposed to a plurality and diversity of sounds that carry a wealth of information for perception in cognitive dynamic systems. While the importance of the acoustic modality for humans as ``autonomous systems'' is obvious, it is investigated to what extent current technical ASs operating in scenarios filled with airborne sound exploit their potential for supporting self-awareness. As a first step, the state of the art of relevant generic techniques for acoustic scene analysis is reviewed, i.e., source localization and the various facets of signal enhancement, including spatial filtering, source separation, noise suppression, dereverberation and echo cancellation. Then, a comprehensive overview of current techniques for ego-noise suppression, as a specific additional challenge for ASs, is presented. Generic methods for robust source localization and signal extraction but also specific models and estimation methods for ego-noise based on various learning techniques are discussed. Finally, active sensing is considered with its unique potential for acoustic scene analysis and, thus, for supporting self-awareness of ASs. Thereby, recent techniques for binaural listening exploiting head motion, for active localization and exploration and for active signal enhancement are presented, with humanoid robots as typical platforms. Underlining the multimodal nature of self-awareness, links to other modalities and nonacoustic reference information are pointed out where appropriate. <br
Keywords: Autonomous systems; self-awareness; acoustic scene analysis; ego-noise; exploration; active localization; active enhancement

Acoustic Signal Processing for Distant-Talking Speech Recognition: Nonlinear Echo Cancellation in a Generic Multichannel Interface - Küch, Fabian; Kellermann, Walter; Buchner, Herbert; Herbordt, Wolfgang (Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP'03), 2003)
LMS::435

Acoustic signal processing for multimedia and telecommunication - Kellermann, Walter; Stenger, Alex; Herbordt, Wolfgang (Online Symposium for Electronics Engineers, 2000)
LMS::179

Acoustic Signal Processing for Next-Generation Human/Machine Interfaces - Kellermann, Walter (Proc. of the 8th Int. Conference in Digital Audio Effects, 2005)
LMS::586

Acoustic source detection and localization based on wavefield decomposition using circular microphone arrays - Teutsch, Heinz; Kellermann, Walter (Journal of the Acoustical Society of America, 2006)
This paper is concerned with the problem of detecting and localizing multiple wideband acoustic sources by applying the notion of wavefield decomposition using circular microphone arrays optionally mounted into cylindrical baffles. The decomposed wavefield representation is used to serve as a basis for so-called modal array signal processing algorithms, which have the significant advantage over classical array signal processing algorithms that they inherently support multiple wideband acoustic sources. A rigorous derivation of modal array signal processing algorithms for source detection and localization, as well as performance evaluations, by means of measurements using an actual real-time capable implementation are presented. © 2006 Acoustical Society of Americ

Acoustic source localization and speed estimation based on time-differences-of-arrival under temperature variations - Annibale, Paolo; Rabenstein, Rudolf (18th European Signal Processing Conference, EUSIPCO 2010, 2010)
In the context of acoustic source localization, knowing the actual propagation speed is crucial especially in uncontrolled environments where the temperature is subject to significant changes that influence the sound speed. In a recent paper we showed the effects of the assumed propagation speed on the localization performance of two closed-form localization algorithms based on TDOA measurements. It has been shown that the so-called unconstrained least squares method is not significantly influenced by a wrongly assumed propagation speed, whereas the constrained method, the one statistically more attractive, is impaired even by small speed deviations. In this article we study in more depth the causes of this disparity and we propose a novel technique to estimate the propagation speed and improve the localization performance when the environment conditions, i.e. the air temperature, are not known exactly. © EURASIP, 201

Acoustic Source Localization under Variable Speed of Sound Conditions - Rabenstein, Rudolf; Annibale, Paolo (Wireless Communications & Mobile Computing, 2017)
The rich literature on acoustic source localization mostly relies on the assumption of a constant value for the speed of sound. This hypothesis allows establishing simple relations between range differences and time differences and leads to effective estimation algorithms. However, it must be challenged for certain applications of wireless acoustic sensor networks in multizone buildings and outdoor environments. This article revisits the source localization problem for the more general case of an unknown value for the speed of sound. It reviews the physical foundations for the dependence of the speed of sound on the air temperature and presents the essential approaches to acoustic source localization. On this basis, several methods for source localization under uncertain or variable speed of sound conditions from the literature are discussed. Applications from different fields are shown. They comprise the localization of sources, sensors, and reflecting surfaces, time-difference-of-arrival disambiguation, and the direct determination of the speed of sound or the air temperature from acoustic measurements.
Keywords: acoustic source localization; time of arrival; time difference of arrival; speed of sound; air temperature

Acoustic Source Position Estimation based on Multi-Feature Gaussian Processes - Brendel, Andreas; Altmann, Ingo; Kellermann, Walter (European Signal Processing Conference (EUSIPCO), 2019)

Acoustic space learning for sound-source separation and localization on binaural manifolds - Deleforge, Antoine; Forbes, Florence; Horaud, Radu (International Journal of Neural Systems, 2015)
In this paper we address the problems of modeling the acoustic space generated by a full-spectrum sound source and of using the learned model for the localization and separation of multiple sources that simultaneously emit sparse-spectrum sounds. We lay theoretical and methodological grounds in order to introduce the binaural manifold paradigm. We perform an in-depth study of the latent low-dimensional structure of the high-dimensional interaural spectral data, based on a corpus recorded with a human-like audiomotor robot head. A non-linear dimensionality reduction technique is used to show that these data lie on a two-dimensional (2D) smooth manifold parameterized by the motor states of the listener, or equivalently, the sound source directions. We propose a probabilistic piecewise affine mapping model (PPAM) specifically designed to deal with high-dimensional data exhibiting an intrinsic piecewise linear structure. We derive a closed-form expectation-maximization (EM) procedure for estimating the model parameters, followed by Bayes inversion for obtaining the full posterior density function of a sound source direction. We extend this solution to deal with missing data and redundancy in real world spectrograms, and hence for 2D localization of natural sound sources such as speech. We further generalize the model to the challenging case of multiple sound sources and we propose a variational EM framework. The associated algorithm, referred to as variational EM for source separation and localization (VESSL) yields a Bayesian estimation of the 2D locations and time-frequency masks of all the sources. Comparisons of the proposed approach with several existing methods reveal that the combination of acoustic-space learning with Bayesian inference enables our method to outperform state-of-the-art method
LMS::1521
Keywords: binaural hearing; sound localization; sound-source seperation; manifold learning; mixture of regressors; EM inference

Acoustic System Identification with Partially Time-Varying Models Based on Tensor Decompositions - Huang, Gongping; Benesty, Jacob; Chen, Jingdong; Paleologu, Constantin; Ciochina, Silviu; Kellermann, Walter; Cohen, Israel (International Workshop on Acoustic Signal Enhancement, IWAENC 2022 - Proceedings, 2022)
Acoustic system identification, which aims at estimating the channel impulse response from a source of interest to the microphone position, plays an important role in many applications, e.g., echo cancellation for full-duplex speech communication. Generally, an acoustic channel impulse response is modeled as a linear finite-impulse-response (FIR) filter, so the objective of system identification is to identify it. While much effort has been devoted to this topic over the last five decades, identifying the room FIR filters accurately with only a small number of observation data snapshots remains a significant challenge. This paper studies this problem and proposes to model the acoustic impulse response, i.e., the FIR filter, with a tensor decomposition, which can be expressed as a multidimensional Kronecker product of a series of shorter filters. Then, a partially time-varying model is applied to acoustic system identification, where the global filter is decomposed into two parts: a time-invariant part, which captures the common properties of acoustic channels, and a time-varying part, which, as its name indicates, represents the components of acoustic channels that change with time. During the identification process, the time-invariant filters can be identified or learned in advance, while the time-varying filters are optimized through an iterative procedure. Simulation results demonstrate that the proposed technique can achieve better acoustic system identification performance with a small number of data snapshots.
CRIS-Team Scopus Importer:2022-11-25
Keywords: Acoustic system identification; iterative algorithm; Kronecker product decomposition; tensor decomposition; Wiener filter

A Csound opcode for a triode stage of a vacuum tube amplifier - Fink, Marco; Rabenstein, Rudolf (14th International Conference on Digital Audio Effects, DAFx 2011, 2011)
The Csound audio programming language adheres to the inputoutput paradigm and provides a large number of specialized commands (called opcodes) for processing output signals from input signals. Therefore it is not directly suitable for component modeling of analog circuitry. This contribution describes an attempt to virtual analog modeling and presents a Csound opcode for a triode stage of a vacuum tube amplifier. Externally it communicates with other opcodes via input and output signals at the sample rate. Internally it uses an established wave digital filter model of a standard triode. The opcode is available as library modul
Keywords: Analog circuitry; Analog modeling; Component modeling; Input and outputs; Input-output; Library modules; Opcodes; Output signal; Sample rate; Vacuum-tube amplifiers; Wave digital filters; Amplifiers (electronic); Digital filters; Tubes (components); Vacuum; Triodes

Active Acoustic Source Tracking Exploiting Particle Filtering and Monte Carlo Tree Search - Haubner, Thomas; Schmidt, Alexander; Kellermann, Walter (European Signal Processing Conference (EUSIPCO), 2019)
In this paper, we address the task of active acoustic source tracking as part of robotic path planning. It denotes the planning of sequences of robotic movements to enhance tracking results of acoustic sources, e.g., talking humans, by fusing observations from multiple positions. Essentially, two strategies are possible: short-term planning, which results in greedy behavior, and long-term planning, which considers a sequence of possible future movements of the robot and the source. Here, we focus on the second method as it might improve tracking performance compared to greedy behavior and propose a path planning algorithm which exploits Monte Carlo Tree Search (MCTS) and particle filtering, based on a reward motivated by information-theoretic considerations. By representing the state posterior by weighted particles, we are capable of modelling arbitrary probability density functions (PDF)s and dealing with highly nonlinear state-space models.<br
Keywords: Active source tracking; particle filter; Monte Carlo tree search; path planning

Active listening room compensation for massive multichannel sound reproduction systems using wave-domain adaptive filtering - Spors, Sascha; Buchner, Herbert; Rabenstein, Rudolf; Herbordt, Wolfgang (Journal of the Acoustical Society of America, 2007)
The acoustic theory for multichannel sound reproduction systems usually assumes free-field conditions for the listening environment. However, their performance in real-world listening environments may be impaired by reflections at the walls. This impairment can be reduced by suitable compensation measures. For systems with many channels, active compensation is an option, since the compensating waves can be created by the reproduction loudspeakers. Due to the time-varying nature of room acoustics, the compensation signals have to be determined by an adaptive system. The problems associated with the successful operation of multichannel adaptive systems are addressed in this contribution. First, a method for decoupling the adaptation problem is introduced. It is based on a generalized singular value decomposition and is called eigenspace adaptive filtering. Unfortunately, it cannot be implemented in its pure form, since the continuous adaptation of the generalized singular value decomposition matrices to the variable room acoustics is numerically very demanding. However, a combination of this mathematical technique with the physical description of wave propagation yields a realizable multichannel adaptation method with good decoupling properties. It is called wave domain adaptive filtering and is discussed here in the context of wave field synthesis. © 2007 Acoustical Society of Americ

Active Listening Room Compensation for Spatial Sound Reproduction Systems - Spors, Sascha (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2007)

Adaptation of Acoustic Echo Cancellers: Incorporating a Memoryless Nonlinearity - Stenger, Alex; Kellermann, Walter; Rabenstein, Rudolf (Proc. IEEE Workshop on Acoustic Echo and Noise Control (IWAENC'99), 1999)
LMS::307

Adaptation of a memoryless preprocessor for nonlinear acoustic echo cancelling - Stenger, Alexander; Kellermann, Walter (Signal Processing, 2000)
Acoustic echo cancellers (AECs) in today's hands-free telephones rely on the assumption of a linear echo path. However, low-cost audio equipment or constraints of portable communication systems cause nonlinear distortions in the loudspeaker and its amplifier, which limit the echo reduction of linear AECs. Such an echo path can be modelled by a memoryless nonlinear function preceding the linear FIR filter. Algorithms for joint adaptation of both stages and stepsize normalizations are derived. As examples for the preprocessor a hard-clipping curve and a polynomial are considered. Fast convergence of the latter is achieved with signal orthogonalization or RLS adaptation. Adaptation stepsize control mechanisms are derived using a novel system distance measure. Experiments under adverse conditions and with real hardware demonstrate robust convergence with both models, and an echo reduction improvement by up to 10 dB at amplitude peaks. For a straightforward implementation, computational cost is increased by factor 1.5-4.5 compared to a linear AEC, but ways for complexity reduction are outlined.

Adapting HMMS of distant-talking asr systems using feature-domain reverberation models - Sehr, Armin; Gardill, Markus; Kellermann, Walter (17th European Signal Processing Conference, EUSIPCO 2009, 2009)
To capture the dispersive effect of reverberation by Hidden Markov Model (HMM)-based distant-talking speech recognition systems, adapting the means of the current HMM state based on the means of the preceding states has been suggested in [1]. In this contribution, we propose to incorporate the reverberation models of [2] into the adaptation approach to describe the effect of reverberation with higher accuracy. Connected-digit recognition experiments in three different rooms confirm that the suggested more accurate reverberation representation leads to a significant performance increase in all investigated environments. © EURASIP, 200

Adaptive beamforming for audio signal acquisition - Herbordt, Wolfgang; Kellermann, Walter (Adaptive Signal Processing - Applications to Real-World Problems, 2003)

Adaptive Block-Based Light Field Coding - Magnor, Marcus; Girod, Bernd (Proc. 3rd International Workshop on Synthetic and Natural Hybrid Coding and Three-Dimensional Imaging, 1999)

Adaptive combination of volterra kernels and its application to nonlinear acoustic echo cancellation - Antonio Azpicueta-Ruiz, Luis; Zeller, Marcus; Ramon Figueiras-Vidal, Anibal; Arenas-Garcia, Jeronimo; Kellermann, Walter (IEEE Transactions on Audio Speech and Language Processing, 2011)
The combination of filters concept is a simple and flexible method to circumvent various compromises hampering the operation of adaptive linear filters. Recently, applications which require the identification of not only linear, but also nonlinear systems are widely studied. In this paper, we propose a combination of adaptive Volterra filters as the most versatile nonlinear models with memory. Moreover, we develop a novel approach that shows a similar behavior but significantly reduces the computational load by combining Volterra kernels rather than complete Volterra filters. Following an outline of the basic principles, the second part of the paper focuses on the application to nonlinear acoustic echo cancellation scenarios. As the ratio of the linear to nonlinear echo signal power is, in general, a priori unknown and time-variant, the performance of nonlinear echo cancellers may be inferior to a linear echo canceller if the nonlinear distortion is very low. Therefore, a modified version of the combination of kernels is developed obtaining a robust behavior regardless of the level of nonlinear distortion. Experiments with noise and speech signals demonstrate the desired behavior and the robustness of both the combination of Volterra filters and the combination of kernels approaches in different application scenarios. © 2010 IEE
Keywords: Combination of filters; combination of kernels; nonlinear acoustic echo cancellation; Volterra filters

Adaptive constrained least squares restoration for removal of blocking artifacts in low bit rate video coding - Kaup, André (IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997)
LMS::479

Adaptive fir filters with automatic length optimization by monitoring a normalized combination scheme - Zeller, Marcus; Azpicueta-Ruiz, Luis A.; Kellermann, Walter (2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2009, 2009)
This paper presents a novel strategy of adaptive filtering which provides an automatic self-configuration of the filter structure in terms of memory length. By monitoring the adaptive mixing of a normalized combination of two competing filters with a different number of coefficients, an online estimate of the optimum filter length is obtained and used to dynamically scale the size of the employed filters. Furthermore, a more efficient, simplified version of this approach is proposed and shown to be equally effective while significantly reducing the required complexity. Experimental results for high-order real-world systems as well as stationary noise and speech signals demonstrate the good performance and the robust tracking behaviour of the outlined algorithms in the context of realistic system identification scenarios. ©2009 IEE
Keywords: Acoustic echo cancellation; Adaptive filters; Scalability; Variable tap-length

Adaptive Frequency Prior for Frequency Selective Reconstruction of Images from Non-Regular Subsampling - Seiler, Jürgen; Kaup, André (IEEE Workshop on Multimedia Signal Processing (MMSP), 2016)
Image signals typically are defined on a rectangular two-dimensional grid. However, there exist scenarios where this is not fulfilled and where the image information only is available for a non-regular subset of pixel position. For processing, transmitting or displaying such an image signal, a re-sampling to a regular grid is required. Recently, Frequency Selective Reconstruction (FSR) has been proposed as a very effective sparsity-based algorithm for solving this under-determined problem. For this, FSR iteratively generates a model of the signal in the Fourier-domain. In this context, a fixed frequency prior inspired by the optical transfer function is used for favoring low-frequency content. However, this fixed prior is often too strict and may lead to a reduced reconstruction quality. To resolve this weakness, this paper proposes an adaptive frequency prior which takes the local density of the available samples into account. The proposed adaptive prior allows for a very high reconstruction quality, yielding gains of up to 0.6 dB PSNR over the fixed prior, independently of the density of the available samples. Compared to other state-of-the-art algorithms, visually noticeable gains of several dB are possibl

Adaptive in-loop noise-filtered prediction for high efficiency video coding - Wige, Eugen; Yammine, Gilbert; Amon, P.; Hutter, Andreas; Kaup, André (3rd IEEE International Workshop on Multimedia Signal Processing, MMSP 2011, 2011)
Compression of noisy image sequences is a hard challenge in video coding. Especially for high quality compression the preprocessing of videos is not possible, as it decreases the objective quality of the videos. In order to overcome this problem, this paper presents an in-loop denoising framework for efficient medium to high fidelity compression of noisy video data. It is shown that using low complexity in-loop noise estimation and noise filtering as well as adaptive selection of the denoised inter frame predictors can improve the compression performance. The proposed algorithm for adaptive selection of the denoised predictor is based on the actual HEVC reference model. The different inter frame prediction modes within the current HEVC reference model are exploited for adaptive selection of denoised prediction by transmission of some side information in combination with decoder side estimation for denoised prediction. The simulation results show considerable gains using the proposed in-loop denoising framework with adaptive selection. In addition the theoretical bounds for the compression efficiency, if we could perfectly estimate the adaptive selection of the denoised prediction in the decoder, are shown in the simulation results. © 2011 IEE

Adaptive joint spatio-temporal error concealment for video communication - Seiler, Jürgen; Kaup, André (2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008, 2008)
In the past years, video communication has found its application in an increasing number of environments. Unfortunately, some of them are error-prone and the risk of block losses caused by transmission errors is ubiquitous. To reduce the effects of these block losses, a new spatio-temporal error concealment algorithm is presented. The algorithm uses spatial as well as temporal information for extrapolating the signal into the lost areas. The extrapolation is carried out in two steps, first a preliminary temporal extrapolation is performed which then is used to generate a model of the original signal, using the spatial neighborhood of the lost block. By applying the spatial refinement a significantly higher concealment quality can be achieved resulting in a gain of up to 5.2 dB in PSNR compared to the unrefined underlying pure temporal extrapolation. © 2008 IEE

Adaptive lagrange multiplier selection for intra-frame video coding - Li, Xiang; Oertel, Norbert; Kaup, André (2007 IEEE International Symposium on Circuits and Systems, ISCAS 2007, 2007)
The Lagrangian technique proves to be an effective way in Rate-Distortion optimization for hybrid video coding. In this paper, an new Lagrange multiplier selection method for Intra-Frame coding is presented. Based on Laplacian distribution, the variance of transformed residual coefficients is introduced into the rate and distortion model, so that the proposed method is able to adaptively optimize the encoding process for different types of videos. It outperforms the algorithm used in the reference software of H.264/AVC, especially for computer animations and videos with small movements. According to the simulations, a gain up to 0.3 dB was achieved. © 2007 IEE

Adaptive listening room compensation for spatial audio systems - Spors, Sascha; Buchner, Herbert; Rabenstein, Rudolf (12th European Signal Processing Conference, EUSIPCO 2004, 2004)
Various kinds of spatial audio systems have been developed that go beyond the current 5.1 standard. Although the approaches are different, they are capable of delivering acoustic immersion to a large number of listeners simultaneously without restricting their activities by headphones, trackers, or alike. In particular, the sweet spot limitation well known from current consumer market systems is repealed. However, these attractive properties could only be realized in listening rooms without any noticeable reverberation. Otherwise, measures have to be taken for the digital compensation of wall reflections. This contribution presents an adaptive compensation technique which is based on the decomposition of sound fields into plane waves. Various advantages are discussed, e.g. improved multichannel adaptation by decoupling of the compensation filters. Finally, results for a specific spatial reproduction method (wave field synthesis) are presented.

Adaptive listening room equalization using a scalable filtering structure in the wave domain - Schneider, Martin; Kellermann, Walter (IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012)
Massive multichannel reproduction systems like wave field synthesis (WFS) are potentially well suited to be complemented by listening room equalization (LRE). However, their typically large number of reproduction channels makes this task challenging for both computational and algorithmic reasons. Wave-domain adaptive filtering (WDAF) was proposed earlier and is especially well-suited to adaptive filtering tasks in the context of WFS. In this paper, we propose to generalize the model originally used for WDAF to allow an adaptive LRE for a broader range of reproduction scenarios, while maintaining the advantages of the original approach. The proposed approach is evaluated for filtering structures of varying complexity along with considering the robustness to varying listener positions. © 2012 IEE
Keywords: adaptive filtering; listening room compensation; listening room equalization; wave domain

Adaptive Low-Pass Extrapolation for Object-Based Texture Coding of Moving Video - Kaup, André (Visual Communications and Image Processing, SPIE, 1997)
LMS::478

Adaptive Mode Decision with Residual Motion Compensation for Distributed Video Coding - van Luong, Huynh; Forchhammer, S.; Slowack, J.; De Cock, J.; Van de Walle, R. (Picture Coding Symposium (PCS), 2013)
Distributed video coding (DVC) is a coding paradigm that entails low complexity encoding by exploiting the source statistics at the decoder. To improve the DVC coding efficiency, this paper proposes a novel adaptive technique for mode decision to control and take advantage of skip mode and intra mode in DVC. The adaptive mode decision is not only based on quality of key frames but also the rate of Wyner-Ziv (WZ) frames. To improve noise distribution estimation for a more accurate mode decision, a residual motion compensation is proposed to estimate a current noise residue based on a previously decoded frame. The experimental results show that the proposed adaptive mode decision DVC significantly improves the rate distortion performance without increasing the encoding complexity. For a GOP size of 2 on the set of test sequences, the average bitrate saving of the proposed codec is 35.5% on WZ frames compared with the DISCOVER code
LMS::1543
Keywords: adaptive mode decision; distributed video coding; noise distribution; residual motion compensation

Adaptive Multimedia Messaging: Application Scenario and Technical Challenges - Heuer, J.; Kaup, André; Rauschenbach, U. (Wireless World Research Forum, 2001)
LMS::456

Adaptive Multimedia Messaging Based on MPEG-7 - The M3-Box - Heuer, J.; Casas, J.L.; Kaup, André (Second International Symposium on Mobile Multimedia Systems and Applications, 2000)
LMS::466

Adaptive Polynomial Filters and their Application to Nonlinear Acoustic Echo Cancellation - Küch, Fabian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2005)
In todays telecommunication devices often cheap audio hardware is included which introduces non-negligible nonlinear distortion into the loudspeaker signal. In case of hands-free telephone systems or mobile communication devices, these nonlinear audio components cause nonlinearly distorted acoustic echoes that can not be sufficiently attenuated by purely linear echo cancellers. This application exemplifies the growing demand for nonlinear adaptive signal processing methods. In this work, we address the task to provide nonlinear adaptive algorithms for applications in acoustic echo cancellation in order to overcome performance limitations of linear approaches. Based on a nonlinear discrete-time model for the acoustic echo path, we focus on special types of adaptive polynomial filters which require only little a priori knowledge about the audio hardware actually included in the telecommunication device. If moderately-sized loudspeakers represent the only source of nonlinear distortion, second-order Volterra filters can been used to model their frequency-dependent nonlinear behaviour. Additionally, we consider the case that only the amplifier or the miniaturized loudspeaker of a mobile phone causes the nonlinear distortion in the echo path. For this scenario, an orthogonalized version of adaptive power filters is proposed in order to improve the echo cancellation performance compared to purely linear approaches in case of saturation-type nonlinearities included in the echo path. Experimental results obtained for real loudspeaker systems are presented in order to verify the suitability of the discussed nonlinear adaptive algorithms for nonlinear acoustic echo cancellatio

Adaptive quantization parameter cascading for hierarchical video coding - Li, Xiang; Amon, Peter; Hutter, Andreas; Kaup, André (2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, ISCAS 2010, 2010)
Quantization parameter (QP) cascaded hierarchical prediction structures have been proved as efficient techniques in hybrid video coding. However, the current QP cascading method is empirical and not adaptive. The reason for the higher coding efficiency of this method has not been fully explored so far. In this paper, the rate-distortion performance of QP cascaded hierarchical video coding is first analyzed with dependent rate-distortion function. Then the optimal offset in linear QP cascading scheme is derived theoretically. It is shown that the widely accepted empirical QP cascading method is actually an approximation to the theoretical solution in fast movement environment. For slow sequences, an average gain of 0.43 dB can be achieved by the combination of two proposed adaptive algorithms. ©2010 IEE

Adaptive Variance-Threshold-Based Skip Modes for Learned Video Compression Using a Motion Complexity Criterion - Brand, Fabian; Seiler, Jürgen; Sauer, Johannes; Alshina, Elena; Kaup, André (2024 Picture Coding Symposium, PCS 2024 - Proceedings, 2024)
Skip modes are a powerful tool to reduce the rate in video compression. The main idea is that the residual areas where the prediction performs well are not transmitted since the prediction quality is good enough that the prediction signal itself can be used as the reconstruction signal. This is commonly used, e.g., in the compression standard VVC, where a skip flag can be transmitted for inter blocks under certain conditions. The skipped residual block is then not transmitted and the content is instead inferred to be zero at the decoder. Current learning-based methods use different kinds of skip modes. One possibility here arises from the fact that the coders estimate and transmit the variance for each transmitted symbol. It has been proposed to use this estimated variance to derive a skip mode. When the variance falls below a threshold, the symbol is not transmitted. In this paper we propose an extension to this method. By classifying each position in the latent space according to the local motion complexity, we can transmit adaptive thresholds for each class. That way, we can employ motion information to refine the granularity of the skip mode. When we implement this method in FVC, we are able to save up 2.11% rate on a GOP 20 sequence. We also discuss the behavior of increasingly adaptive skip modes in scenarios with larger GOP size, where error-propagation becomes a larger issue.
CRIS-Team Scopus Importer:2024-07-19
Keywords: inter coding; learning-based compression; motion compensation; skip mode; video compression

Adaptive Volterra Filters for Acoustic Echo Cancellation - Stenger, Alexander; Rabenstein, Rudolf (Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP'99), 1999)
More than 20 years ago it was shown that adaptive Volterra filters can be used for telephone echo cancellation, but the inherent need for a large number of coefficients has inhibited their practical use. Recently, first attempts have been made to combat nonlinearities in the acoustic echo path of hands-free telephones, which are caused by low-cost audio components. As these attempts are based on a memoryless nonlinear model, they are constrained to saturation effects, which can not describe the nonlinear behavior of small loudspeakers accurately enough. In this contribution, we report real time measurements with an adaptive Volterra filter. At first, we verify that loudspeaker nonlinearities encountered in the echo path of hands-free telephones have to be modelled with memory. Based on these results, we propose an adaptive Volterra filter structure with a reduced number of coefficients. The new structure is experimentally compared to echo cancellers with a memoryless nonlinear model using a real acoustic echo cancelling setup with a small loudspeaker. With the new technique, an echo reduction improvement of 7 dB over conventional linear adaptive filters is achieved. Furthermore it is shown that the proposed adaptive Volterra filter structure outperforms the memoryless approaches.

Adaptive Volterra filters with evolutionary quadratic kernels using a combination scheme for memory control - Zeller, Marcus; Azpicueta-Ruiz, Luis A.; Arenas-Garcia, Jeronimo; Kellermann, Walter (IEEE Transactions on Signal Processing, 2011)
This paper proposes a new paradigm for adaptive Volterra filtering using a time-variant size of the quadratic kernel memory in order to optimally identify any unknown transversal second-order nonlinear system. To this end, competing Volterra structures of different sizes are employed in a hierarchical combination scheme so as to find the best configuration of the second-order kernel memory, using the already known diagonal-coordinate representation. The length and number of required quadratic kernel diagonals can be concurrently estimated by monitoring the combination performance. Subsequently, the memory size of the involved models is dynamically increased or decreased, following a set of intuitive rules. Since this automatic memory adaptation is performed along with the coefficient updates, an efficient Volterra filter is realized, offering great flexibility and minimizing the risk of under- or overmodeling any given quadratic nonlinearity. Besides the straightforward scheme, a simplified version is presented, greatly reducing the algorithmic demands. This efficient version is based on a virtualization of the competing Volterra filters by jointly using common coefficients and hence exhibits a computational complexity suitable for practical implementations. The robust estimation performance of the approach is demonstrated by various examples for a nonlinear acoustic echo cancellation scenario, involving stationary noise, real speech signals and realistic Volterra kernels. © 2010 IEE
Keywords: Combination of filters; echo cancellation; nonlinear system identification; structure selection; Volterra filters

A Data Set Providing Synthetic and Real-World Fisheye Video Sequences - Eichenseer, Andrea; Kaup, André (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016)
In video surveillance as well as automotive applications, so-called fisheye cameras are often employed to capture a very wide angle of view. As such cameras depend on projections quite different from the classical perspective projection, the resulting fisheye image and video data correspondingly exhibits non-rectilinear image characteristics. Typical image and video processing algorithms, however, are not designed for these fisheye characteristics. To be able to develop and evaluate algorithms specifically adapted to fisheye images and videos, a corresponding test data set is therefore introduced in this paper. The first of those sequences were generated during the authors' own work on motion estimation for fish-eye videos and further sequences have gradually been added to create a more extensive collection. The data set now comprises synthetically generated fisheye sequences, ranging from simple patterns to more complex scenes, as well as fisheye video sequences captured with an actual fisheye camera. For the synthetic sequences, exact information on the lens employed is available, thus facilitating both verification and evaluation of any adapted algorithms. For the real-world sequences, we provide calibration data as well as the settings used during acquisition. The sequences are freely available via www.lms.lnt.de/fisheyedataset
Keywords: data set; fisheye video sequences; test images; synthetic sequences; sensor data

Additive non-Gaussian noise attacks on the scalar Costa scheme (SCS) - Tzschoppe, Roman; Bäuml, R.; Fischer, R.; Huber, J.; Kaup, André (Proceedings of SPIE-IS and T Electronic Imaging - Security, Steganography, and Watermarking of Multimedia Contents VII, 2005)
The additive attack public mutual information game is explicitly solved for one of the simplest quantization based watermarking schemes, the scalar Costa scheme (SCS). It is a zero-sum game played between the embedder and the attacker, and the payoff function is the mutual information.. The solution of the game, a subgame perfect nash equilibrium, is found by backward induction. Therefore, the Blahut-Arimoto algorithm is employed for numerically optimizing the mutual information over noise distributions. Although the worst case distribution is in general strongly non-Gaussian, the capacity degradation compared to a suboptimal Gaussian noise attack is quite small. The loss, if the embedder optimizes SCS for a Gaussian attack but the worst case attack is employed, is negligible. © 2005 SPIE and IS&
Keywords: Additive noise attacks; Lattice Costa scheme; Mutual information game; Non-Gaussian noise; Scalar Costa scheme; Worst case attack

A Digital Frequency Selective Fading Simulator - Schüßler, Hans Wilhelm; Thielecke, Jörn; Preuss, Klaus-Peter; Edler, Wolfgang; Gerken, Max (Frequenz, 1989)
UnivIS-Import:2015-03-05:Pub.1989.tech.IE.komele.adigit

A direct derivation of transforms for wave-domain adaptive filtering based on circular harmonics - Schneider, Martin; Kellermann, Walter (European Signal Processing Conference (EUSIPCO), 2012)
Wave-domain adaptive filtering (WDAF) is advantageous for modeling loudspeaker-enclosure-microphone systems with many loudspeakers and microphones (MIMO LEMS). The necessary transforms may be derived by considering free field propagation between loudspeaker and microphones first, followed by wave field analysis of the true and simulated microphone signals. However, transforms obtained following this strategy are not invertible when there are more loudspeakers than microphones. As invertibility of the transforms is required for many applications, this restricts the applicability of WDAF. In this paper we propose a strategy to describe the free field propagation directly in the wave domain, so that invertible transforms can be defined independently from the number of actually used microphones. With this strategy, transforms for a MIMO LEMS comprising a uniform circular microphone array and an arbitrary loudspeaker array are derived. © 2012 EURASI
Keywords: Adaptive filtering; domain; multichannel; wave

A Direct Method to Computational Acoustics - Rabenstein, Rudolf; Zayati, A. (Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99), 1999)
The exact knowledge of the sound field within an enclosure is essential for a number of applications in electro-acoustics. Conventional methods for the assessment of room acoustics model the sound propagation in analogy to the propagation of light. More advanced computational methods rely on the numerical solution of the wave equation. A recently presented method is based on multidimensional wave digital principles. It allows a physically exact numerical modelling of the relevant acoustical effects and yields robust algorithms. This contribution presents a new foundation of the multidimensional wave digital principle as applied to room acoustics. It starts from the first principles of physics. From there, the derivation of the algorithm only involves basic knowledge of numerical mathematics, linear algebra, and multidimensional system theory. An example for the simulation of dynamic three-dimensional sound propagation demonstrates the capability of the method.

Adjustable boundary conditions for multidimensional transfer function models - Rabenstein, Rudolf; Petrausch, Stefan (10th International Conference on Digital Audio Effects, DAFx 2007, 2007)
Block based physical modeling requires to provide a library of modeling blocks for standard components of real or virtual musical instruments. Complex synthesis models are built by connecting standard components in a physically meaningful way. These connections are investigated for modeling a resonating structure as a distributed parameter system. The dependence of a resonator's spectral structure on the termination of its ports is analyzed. It is shown that the boundary conditions of a distributed parameter system can be adjusted by proper termination only. Examples show the corresponding variation of the resonator's spectral structure in response to variations of the external terminatio

A dual-model approach to blind quality assessment of noisy images - Zhai, Guangtao; Kaup, André; Wang, J.; Yang, X. (2013 Picture Coding Symposium, PCS 2013, 2013)
Physiological and psychological evidences exist that the human visual system (HVS) has different behavioral patterns under low and high noise/artifact levels. We propose in this paper a dual-model approach to blind or no-reference (NR) image quality assessment (IQA) of noisy images through differentiating near-threshold and suprathreshold noise conditions. The underlying assumption for the proposed dual-model method is that for images with low level near-threshold noise, HVS tries to gauge the strength of the noise, so the image quality can be well approximated via measuring strength of the noise. And for images with their contents overwhelmed by high level suprathreshold noise, the HVS tries to recover meaningful structure from the noisy pixels using past experiences and prior knowledge encoded into an internal generative model of the brain. So image quality is closely related to the agreement between the noisy observation and the internal generative model explainable part of the image. More specifically, under the near-threshold noise condition, a noise level estimation algorithm based on natural image statistics is used, while under suprathreshold condition, an active inference model based on the free energy principle is adopted. The near-and suprathreshold models can be seamlessly integrated through a transformation between both estimates. The proposed dual-model algorithm has been tested on additive Gaussian noise contaminated images. Experimental results and comparative studies suggest that although being a no-reference approach, the proposed algorithm has prediction accuracy comparable to some of the best full-reference (FR) IQA methods. © 2013 IEE
Keywords: Free energy model; Image quality assessment; Near-threshold noise; Noise estimation; Suprathreshold noise

Advanced Design Space Exploration for Joint Energy and Quality Optimization for VVC - Kränzler, Matthias; Herglotz, Christian; Kaup, André (Picture Coding Symposium (PCS), 2022)
In recent studies, it could be shown that the energy demand of Versatile Video Coding (VVC) decoders can be twice as high as comparable High Efficiency Video Coding (HEVC) decoders. A significant part of this increase in complexity is attributed to the usage of new coding tools. By using a design space exploration algorithm, it was shown that the energy demand of VVC-coded sequences could be reduced if different coding tool profiles were used for the encoding process. This work extends the algorithm with several optimization strategies, methodological adjustments to optimize perceptual quality, and a new minimization criterion. As a result, we significantly improve the Pareto front, and the rate-distortion and energy efficiency of the state-of-the-art design space exploration. Therefore, we show an energy demand reduction of up to 47% with less than 30% additional bit rate, or a reduction of over 35% with approximately 6% additional bit rat
Keywords: Energy-Efficiency; Optimization; VVC; Decoder

Advanced Lagrange multiplier selection for hybrid video coding - Li, Xiang; Oertel, Norbert; Hutter, A.; Kaup, André (IEEE International Conference onMultimedia and Expo, ICME 2007, 2007)
The Lagrangian multiplier based rate-distortion optimization has been proved to be an effective way in hybrid video coding. In this paper, an advanced Lagrange multiplier selection method is presented, Based on Laplace distribution, the variance of transformed residuals is introduced into the rate and distortion models. Moreover, inspired by the ρ-domain method, the percentage of non-zeros among quantized residuals is also considered to refine the rate model. Thanks to the more accurate models, the proposed method is able to adaptively optimize the encoding process for videos with different properties. It outperforms the algorithm used in the reference software of H.264/AVC. According to the simulation, a gain up to 1.1 dB was achieved. © 2007 IEE

Advances in adaptive filtering theory and applications to acoustic and speech signal processing - Rupp, Markus; Kellermann, Walter; Zoubir, Abdelhak; Schmidt, Gerhard (EURASIP Journal on Advances in Signal Processing, 2016)
UnivIS-Import:2017-03-24:Pub.2016.tech.IE.LEN.advanc

Advances in identification and compensation of nonlinear systems by adaptive volterra models - Zeller, Marcus; Kellermann, Walter (44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010, 2010)
In this contribution, we present some recent advances in the modeling of unknown nonlinearities by adaptive Volterra filters. In particular, a system identification scenario in the form of the nonlinear acoustic echo cancelation problem is considered, which is most challenging due to large kernel sizes and excitation by colored (noise) and/or nonstationary signals (speech). After reviewing the general filter structure and discussing a possibly more efficient DFT-domain realization by resorting to a multichannel representation, mainly two aspects are covered: On the one hand, convergence aspects are addressed (i) by framewise iterated updating that is shown to result in a faster adaptation for DFT-domain implementations and (ii) by combining Volterra kernels with different adaptation parameters, thus yielding an elegant method of robust step-size control. On the other hand, a modification of these combination schemes lends itself to an especially promising approach that enables an evolutionary self-configuration of the adaptive nonlinear structure while providing estimates for the optimum memory size parameters. From these developments, conclusions are drawn and some guidelines for future work are given. © 2010 IEE

A fast algorithm for selective signal extrapolation with arbitrary basis functions - Seiler, Jürgen; Kaup, André (EURASIP Journal on Advances in Signal Processing, 2011)
Signal extrapolation is an important task in digital signal processing for extending known signals into unknown areas. The Selective Extrapolation is a very effective algorithm to achieve this. Thereby, the extrapolation is obtained by generating a model of the signal to be extrapolated as weighted superposition of basis functions. Unfortunately, this algorithm is computationally very expensive and, up to now, efficient implementations exist only for basis function sets that emanate from discrete transforms. Within the scope of this contribution, a novel efficient solution for Selective Extrapolation is presented for utilization with arbitrary basis functions. The proposed algorithm mathematically behaves identically to the original Selective Extrapolation but is several decades faster. Furthermore, it is able to outperform existent fast transform domain algorithms which are limited to basis function sets that belong to the corresponding transform. With that, the novel algorithm allows for an efficient use of arbitrary basis functions, even if they are only numerically defined. © 2011 Jrgen Seiler and Andr Kau

A fast H.263 to H.264 inter-frame transcoder with motion vector refinement - Bialkowski, Jens; Menden, M.; Barkowsky, Marcus; Kaup, André (Picture Coding Symposium 2004, 2004)
Video transcoding techniques supply interoperability of a great variety of devices that can be connected by various communication networks with different data rate requirements. Particularly inhomogeneous video transcoding is the conversion of an existing video bitstream from one standard into a bitstream of another standard, for example the conversion from H.263 data into H.264 data. It may also include parameter adaptations such as bitrate or frame rate reduction. In this work, we present a low-complexity transcoder design for transcoding Interframe macroblocks from H.263 to H.264. The large complexity reduction comes from reusing motion vectors of the input bitstream and from the fact that only a subset of all possible H.264 coding parameters is used. The selection of these parameters is based on statistical investigations of encoded H.264 parameters from a full parameter search on decoded H.263 sequences. Our approach leads to small rate-distortion losses compared to the full parameter encoder below 0.5 dB at comparable data rates, but the computational complexity reduction is over 98% for finding a suitable macroblock decision. Compared to simply copying the motion vectors without post-processing, the rate-distortion gain of our approach is up to 2 dB at equivalent rate.

A flexible mixed-signal image processing pipeline using 3D chip stacks - Shi, Lan; Söll, Christopher; Pfundt, Benjamin; Bänisch, Andreas; Reichenbach, Marc; Seiler, Jürgen; Ussmueller, Thomas; Weigel, Robert (Journal of Real-Time Image Processing, 2016)
This work presents a highly flexible mixed-signal CMOS image sensor suitable for smart camera applications. These systems need to fit different constraints regarding power consumption, speed and quality, and the optimal compromise may differ depending on the application. Moreover, the best implementation of a desired image processing task may be in the analog or the digital domain, or even a combined computation. Different aspects starting from the image sensor and signal acquisition up to the pre-processing in analog and digital domain are investigated in this paper to optimize not just one part of the system, but the whole system altogether. Moreover, it is shown that analog processing algorithms can improve signal quality, processing speed and latency while being able to save power, which is important for real-time systems. In order to be able to carry out spatial operations, the state-of-the-art sensor is modified to be able to read out multiple pixels at the same time. This allows analog spatial filter operations which consume significantly less power. As an example, an averaging filter is described which needs less than 5.3 % of the power–time product of a digital implementation for one computation. To enhance data throughput and flexibility, 3D chip stacking is proposed to partition the sensor in smaller units and enable massively parallel processin
Keywords: 3D chip stacking; Analog pre-processing; Application optimized image pipeline; Partitioning; Smart camera

A fundamental relation between blind and supervised adaptive filtering illustrated for blind source separation and acoustic echo cancellation - Buchner, Herbert; Kellermann, Walter (2008 Hands-free Speech Communication and Microphone Arrays, HSCMA 2008, 2008)
In recent years broadband signal aquisition by sensor arrays, e.g., for speech and audio signals in a hands-free scenario, has become a popular research field in order to separate certain desired source signals from competing or interfering source signals ((blind) source separation or interference cancellation) and to possibly dereverberate them (blind deconvolution). In various practical scenarios, some or even all interfering source signals may be directly accessible and/or some side information on the propagation path is known. In these cases we can tackle the separation problem by supervised adaptation algorithms, e.g., the popular LMS- or RLS-type algorithms, rather than the more involved blind adaptation algorithms. In contrast, for blind estimation, such as in the blind source separation (BSS) scenario where both the propagation paths and the original source signals are unknown, the method of independent component analysis (ICA) is typically applied. Traditionally, the ICA method and supervised adaptation algorithms have been treated as different research areas. In this paper, we establish a conceptually simple, yet fundamental relation between these two worlds. This is made possible using the previously introduced generic broadband adaptive filtering framework, called TRINICON. As we will demonstrate, not only both the well-known blind and supervised adaptive filtering algorithms turn out as special cases of this generic framework, but we also gain various new insights and synergy effects for the development of new and improved adaptation algorithms. ©2008 IEE
Keywords: Adaptive signal processing; Echo suppression; MIMO systems; Robustness; Separation

A general approach to block-based physical modeling with mixed modeling strategies for digital sound synthesis - Petrausch, Stefan; Escolano, Jose; Rabenstein, Rudolf (2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05, 2005)
Block-based physical modeling algorithms for sound synthesis have gained more and more interest in the past few years. They have the advantage, that new models can be created by combining existing blocks and they offer the opportunity to combine different modeling techniques. In this paper we propose a general approach how to create physical models in blocks and how to connect these blocks assuring their correct physical interaction. Furthermore we show how to implement the interconnection of these models with wave digital filter principles. The complete procedure is applied to the example application scenario of the one-dimensional wave equation. Three different modeling techniques are used. One part is implemented with a finite difference time domain approach, one with the functional transformation method, and the boundaries are implemented by wave digital filters. ©2005 IEE

A Generalization of a Class of Blind Source Separation Algorithms for Convolutive Mixtures - Buchner, Herbert; Aichner, Robert; Kellermann, Walter (Proc. IEEE Int. Symposium on Independent Component Analysis and Blind Signal Separation (ICA), 2003)
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A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics - Buchner, Herbert; Aichner, Robert; Kellermann, Walter (IEEE Transactions on Speech and Audio Processing, 2005)
In this paper, we present a general broadband approach to blind source separation (BSS) for convolutive mixtures based on second-order statistics. This avoids several known limitations of the conventional narrowband approximation, such as the internal permutation problem. In contrast to traditional narrow-band approaches, the new framework simultaneously exploits the nonwhiteness property and nonstationarity property of the source signals. Using a novel matrix formulation, we rigorously derive the corresponding time-domain and frequency-domain broadband algorithms by generalizing a known cost-function which inherently allows joint optimization for several time-lags of the correlations. Based on the broadband approach time-domain, constraints are obtained which provide a deeper understanding of the internal permutation problem in traditional narrowband frequency-domain BSS. For both the time-domain and the frequency-domain versions, we discuss links to well-known, and also, to novel algorithms that constitute special cases. Moreover, using the so-called generalized coherence, links between the time-domain and the frequency-domain algorithms can be established, showing that our cost function leads to an update equation with an inherent normalization ensuring a robust adaptation behavior. The concept is applicable to offline, online, and block-online algorithms by introducing a general weighting function allowing for tracking of time-varying real acoustic environment
Keywords: Adaptive filtering; Blind source separation (BSS); Convolutive mixtures; Multiple-input-multiple-output (MIMO) systems; Second-order statistics

A Generalization of the Teager Algorithm - Moore, M.; Bernstein, Reinhard; Mitra, Sanjit (Proceedings IEEE Workshop on Nonlinear Signal and Image Processing NSIP 97, 1997)

A GPU-accelerated real-time implementation of TRINICON-BSS for multiple separation units - Anderson, Craig; Meier, Stefan; Kellermann, Walter; Teal, Paul D.; Poletti, Mark A. (Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2014)
In this demonstration, a GPU-accelerated implementation of the second-order statistics version of the TRINICON (Triple-N independent component analysis for convolutive mixtures) blind source separation (BSS) algorithm is presented. The single-precision arithmetic separation performance is compared with a double-precision CPU (Central Processing Unit) reference implementation to establish the effect of parallel algorithms and the reduction of floating-point precision. Computational performance results are presented for a single 2-channel separation unit and multiple simultaneous 2-channel separation units to study the potential future use in a sensor array network with multiple 2-channel separation unit
Keywords: Blind source separation; parallel algorithms; adaptive filtering

A Guided Upsampling Network for Short Wave Infrared Images Using Graph Regularization - Sippel, Frank; Seiler, Jürgen; Kaup, André (2024 IEEE International Conference on Acoustics, Speech and Signal Processing, 2024)

A highly efficient optimization scheme for remos-based distant-talking speech recognition - Maas, Roland; Sehr, Armin; Gugat, Martin; Kellermann, Walter (18th European Signal Processing Conference, EUSIPCO 2010, 2010)
A highly efficient decoding algorithm for the REMOS (REverberationMOdeling for Speech recognition) concept for distant-talking speech recognition as proposed in [1] is suggested to reduce the computational complexity by about two orders of magnitude and thereby allowing for first real-time implementations. REMOS is based on a combined acoustic model consisting of a conventional hidden Markov model (HMM), modeling the clean speech, and a reverberation model. During recognition, the most likely clean-speech and reverberant contributions are estimated by solving an inner optimization problem for logarithmic melspectral (logmelspec) features. In this paper, two approximation techniques for the inner optimization problem are derived. Connected digit recognition experiments confirm that the computational complexity is significantly reduced. Ensuring that the global optima of the inner optimization problem are found, the decoding algorithm based on the proposed approximations even increases the recognition accuracy relative to interior point optimization techniques. © EURASIP, 201

A Hilbert-Space approach to the complete investigation of Vaidyanathan's procedure applied to the design of unitary filter banks of the generation of orthogonal wavelet bases - Steffen, Peter; Brandhuber, Wolfgang (Proceedings of SPIE, Optical and Digital Image Processing, 2008)
The decomposition of signals using the classical Daubechies wavelets can equivalently be described as a decomposition using a two channel filterbank with the scaling function corresponding to the low pass and the mother wavelet to the high pass channel. This classical two channel approach was extended to also comprise filterbanks with more than two channels, corresponding to one scaling function and two or more mother wavelets.1 Although these newly found wavelets fitted in very well with the theory of the existing Daubechies wavelets, the frequency selectivity of the corresponding filterbank was in general not satisfying. We presented a new method2 to improve the selectivity of the corresponding filterbank by introducing additional elementary building blocks in the design process, leaving the low pass filter and the polynomial degree of the impulse responses of the other filters of the filterbank untouched. In this paper we take the new method one step further and not only show how the improvement could be achieved but also what possibilities there are to modify the design and what patterns they adhere to. Furthermore we introduce a figure of merit of the total filterbank and its individual filters and show the improvements to the design proces
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A Hybrid Approach for Runtime Analysis Using a Cycle and Instruction Accurate Model - Rachuj, Sebastian; Herglotz, Christian; Reichenbach, Marc; Kaup, André; Fey, Dietmar (31st International Conference on Architecture of Computing Systems (ARCS), 2018)

A Hybrid Motion Estimation Technique for Fisheye Video Sequences Based on Equisolid Re-Projection - Eichenseer, Andrea; Bätz, Michel; Seiler, Jürgen; Kaup, André (Proc. of IEEE Internationl Conference on Image Processing (ICIP), 2015)
Capturing large fields of view with only one camera is an important aspect in surveillance and automotive applications, but the wide-angle fisheye imagery thus obtained exhibits very special characteristics that may not be very well suited for typical image and video processing methods such as motion estimation. This paper introduces a motion estimation method that adapts to the typical radial characteristics of fisheye video sequences by making use of an equisolid re-projection after moving part of the motion vector search into the perspective domain via a corresponding back-projection. By combining this approach with conventional translational motion estimation and compensation, average gains in luminance PSNR of up to 1.14 dB are achieved for synthetic fish-eye sequences and up to 0.96 dB for real-world data. Maximum gains for selected frame pairs amount to 2.40 dB and 1.39 dB for synthetic and real-world data, respectivel
UnivIS-Import:2015-10-26:Pub.2015.tech.IE.LEN.ahybri
Keywords: GRK-1773

Airborne-Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data - Kreuzer, Matthias; Schmidt, David; Wokusch, Simon; Kellermann, Walter (2023 IEEE International Conference on Prognostics and Health Management (ICPHM), 2023)
In this paper, we address the challenging problem of detecting bearing faults in railway vehicles by analyzing acoustic signals recorded during regular operation. For this, we introduce Mel Frequency Cepstral Coefficients (MFCCs) as features, which form the input to a simple Multi-Layer Perceptron classifier. The proposed method is evaluated with real-world data that was obtained for state-of-the-art commuter railway vehicles in a measurement campaign. The experiments show that with the chosen MFCC features bearing faults can be reliably detected even for bearing damages that were not included in trainin
Keywords: bearing fault; condition monitoring; airborne sound analysis; MFCCs; railway vehicle; acoustic diagnosis

A latently constrained mixture model for audio source separation and localization - Deleforge, Antoine; Horaud, Radu (Proceedings of th 10th International Conference on Latent Variable Analysis and Source Separation (LVA/ICA), 2012, 2012)
We present a method for audio source separation and localization from binaural recordings. The method combines a new generative probabilistic model with time-frequency masking. We suggest that device-dependent relationships between point-source positions and interaural spectral cues may be learnt in order to constrain a mixture model. This allows to capture subtle separation and localization features embedded in the auditory data. We illustrate our method with data composed of two and three mixed speech signals in the presence of reverberations. Using standard evaluation metrics, we compare our method with a recent binaural-based source separation-localization algorith
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Keywords: sound source; source position; room impulse response; constrain mixture model; sound intensity level

Aliasing Reduction for Modified Discrete Cosine Transform Domain Filtering and its Application to Speech Enhancement - Küch, Fabian; Edler, Bernd (Proceedings of the IEEE 2007 Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2007)
Efficient combinations of coding and manipulation of audio signals in the spectral domain are often desirable in communication systems. The modified discrete cosine transform (MDCT) represents a popular spectral transform in audio coding as it leads to compact signal representations. However, as the MDCT corresponds to a critically sampled filter bank, it is in general not appropriate to directly apply it to filtering tasks. In this paper we present a method to compensate for aliasing terms that arise from such direct MDCT domain filtering. The discussion is thereby based on a rigorous matrix representation of critically sampled filter banks which also leads to corresponding efficient realizations. As an application showcase, noise reduction for MDCT based speech coding is considered in simulations.<br

A Locally Optimal Design Algorithm for Block-Based Multi-Hypothesis Motion-Compensated Prediction - Flierl, Markus; Wiegand, Thomas; Girod, Bernd (IEEE Data Compression Conference (DCC'98), 1998)
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A Low-Complexity Metric for the Estimation of Perceived Chrominance Sub-Sampling Errors in Screen Content Images - Heindel, Andreas; Wige, Eugen; Fleckenstein, Felix; Prestele, Benjamin; Gehlert, Alexander; Kaup, André (IEEE Int. Conf. on Image Processing (ICIP), 2017)
In web conferencing and remote desktop applications screen sharing is a popular feature. Due to the limited data rate, image and video compression technologies are used to enable this functionality over the internet. However, the commonly used basic profiles of modern video codecs typically utilize the YCbCr color space involving 4:2:0 chrominance sub-sampling. This sub-sampling introduces visually disturbing artifacts for screen content, which may be avoided or reduced if they could be automatically detected. However, these artifacts are shown to be hardly captured by conventional image quality metrics. To address this problem, we developed the Perceived Chrominance Sub-sampling Error (PCSE) metric. Moreover, we created a subjectively evaluated screen content image data set, which is employed for the performance evaluation. The PCSE achieves absolute correlation coefficient values of almost 0.8. Thus, conventional quality metrics are significantly outperforme
Keywords: Chrominance sub-sampling; image quality assessment; PCSE; screen content

A Lower Error Bound for Oversampled Subband Adaptive Filters - Weiß, S.; Stenger, Alexander; Stewart, R. W.; Rabenstein, Rudolf (Electronics Letters, 1998)
The authors evaluate error performance limitations in oversampled subband adaptive filter applications based on an analysis of aliasing in the subband signals. The power spectral density of the minimum error signal is given by the aliased signal components. The presented analysis closely agrees with simulation results.

A Low-Parametric Model for Bit-Rate Estimation of VVC Residual Coding - Brand, Fabian; Herglotz, Christian; Kaup, André (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2022)
There are many tasks within video compression which require fast bit rate estimation. As an example, rate-control algorithms are only feasible because it is possible to estimate the required bit rate without needing to encode the entire block. With residual coding technology becoming more and more sophisticated, the corresponding bit rate models require more advanced features. In this work, we propose a set of four features together with a linear model, which is able to estimate the rate of arbitrary residual blocks which were compressed using the VVC standard. Our method outperforms other methods which were used for the same task both in terms of mean absolute error and mean relative error. Our model deviates by less than 4 bit on average over a large dataset of natural images.
CRIS-Team Scopus Importer:2022-07-22
Keywords: Rate Control; Rate Model; Versatile Video Coding

A measurement method for the linear and nonlinear properties of electro-acoustic transmission systems - Heinle, Frank; Rabenstein, Rudolf; Stenger, Alexander (Signal Processing, 1998)
Competitive audio consumer products require not only cheap signal processing hardware but also low-cost analog equipment and sound transducers. The nonlinear distortions produced by these electro-acoustic transmission systems cannot be described and analyzed by standard methods based on linear systems theory alone. In order to take the nonlinear properties into account, we present a measurement method for the linear and nonlinear transmission characteristics of almost arbitrary systems and show its application to the analysis of electro-acoustic systems. Examples demonstrate the measurement of the impulse response of a loudspeaker-enclosure-microphone-system with cheap analog equipment in the presence of background noise. © 1998 Elsevier Science B. V. All rights reserved.
Keywords: Electro-acoustic systems; Frequency response; Measurement; Nonlinear systems; Power spectral density

A methodology for evaluating the accuracy of wave field rendering techniques - Canclini, A.; Annibale, Paolo; Antonacci, F.; Sarti, A.; Rabenstein, Rudolf; Tubaro, S. (36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, 2011)
In this paper we propose a methodology for assessing the accuracy of techniques of wave field rendering through loudspeaker arrays. In order to measure the rendered wave field we adopt a solution based on a circular harmonic analysis of the sound field captured by a virtual microphone array. As a result of this analysis stage, we are able to compare the target, the theoretical and the measured wave fields, which may differ due to the non-ideality in the loudspeaker array or in the environment that generates some spurious reverberations. Moreover, in order to quantify the error between target, theoretical and measured wave fields, we define some evaluation metrics, based on RMSE and modal analysis of the acquired wave fields. We show some experimental results on real data. © 2011 IEE
Keywords: Loudspeaker array; virtual microphone array; wave field rendering evaluation

A MINIMUM VARIANCE DISTORTIONLESS RESPONSE SPECTRAL ESTIMATOR WITH KRONECKER PRODUCT FILTERS - Wang, Xianrui; Benesty, Jacob; Huang, Gongping; Chen, Jingdong (European Signal Processing Conference, 2022)
Spectral estimation is of significant practical importance in a wide range of applications. This paper proposes a minimum variance distortionless response (MVDR) method for spectral estimation based on the Kronecker product. Taking advantage of the particular structure of the Fourier vector, we decompose it as a Kronecker product of two shorter vectors. Then, we design the spectral estimation filters under the same structure, i.e., as a Kronecker product of two filters. Consequently, the conventional MVDR spectrum problem is transformed to one of estimating two filters of much shorter lengths. Since it has much fewer parameters to estimate, the proposed method is able to achieve better performance than its conventional counterpart, particularly when the number of available signal samples is small. Also presented in this paper is the generalization to the estimation of the cross-spectrum and coherence function.
CRIS-Team Scopus Importer:2022-11-11
Keywords: coherence function; cross-spectrum; Kronecker product; minimum variance distortionless response (MVDR) filter; Spectral estimation

A model for the temporal evolution of the spatial coherence in decaying reverberant sound fields - Nees, Sam; Schwarz, Andreas; Kellermann, Walter (Journal of the Acoustical Society of America, 2015)
Reverberant sound fields are often modeled as isotropic. However, it has been observed that spatial properties change during the decay of the sound field energy, due to non-isotropic attenuation in non-ideal rooms. In this letter, a model for the spatial coherence between two sensors in a decaying reverberant sound field is developed for rectangular rooms. The modeled coherence function depends on room dimensions, surface reflectivity, and orientation of the sensor pair, but is independent of the position of source and sensors in the room. The model includes the spherically isotropic (diffuse) and cylindrically isotropic sound field models as special case

A Molecular Communications Framework for Understanding the Floral Transition - Magarini, Maurizio; Morris, Richard J.; Ratti, Francesca; Schäfer, Maximilian; Bassel, George W.; Awan, Hamdan; Haselmayr, Werner (7th ACM International Conference on Nanoscale Computing and Communication, 2020)

A morphological approach to single-channel wind-noise suppression - Hofmann, Christian; Wolff, Tobias; Buck, Markus; Haulick, Tim; Kellermann, Walter (International Workshop on Acoustic Signal Enhancement (IWAENC), 2012)
Today, a variety of technical devices deploy spoken language processing technology. In many practical use cases, not only stationary ambient noises but non-stationary interferences, such as wind noise, degrade their performance. If only one microphone is available, regular noise reduction schemes are capable of suppressing stationary interferences. Wind noises, however, are highly non-stationary and may be annoying in a hands-free application, for instance. To suppress such interferences in a speech signal, we propose to consider the signal spectrogram as an image and to exploit neighborhood relations in the time-frequency plane, hence morphological features. To this end, wind-noise-typical energy patterns are extracted by morphological operations employing both the signal spectrogram and its temporal derivative. The resulting estimate of the interference power spectral density is then used for speech enhancement in a subband-processing framework. Distance measures and informal listening tests indicate the effectiveness of the proposed method compared to classical noise-reduction schemes in the presence of non-stationary interferences of wind nois
Keywords: Instationary; Interference; Morphological processing; Noise; Non-stationary; Single-channel; Speech enhancement; Wind-noise reduction

A multidimensional state-space approach for the numerical simulation of sound propagation in enclosures - Spors, Sascha; Rabenstein, Rudolf (11th European Signal Processing Conference, EUSIPCO 2002, 2002)
This paper presents a multidimensional state-space approach for the numerical simulation of sound propagation in enclosures. The simulation algorithm is essentially based on the wave digital filter principle however we will give a more direct access to the numerical solution of the wave equation here. To simulate sound propagation in enclosures, a detailed treatment of the boundary conditions is necessary. We focus in this paper on the treatment of memoryless boundary conditions in the new simulation algorithm.

A Multidimensional Transfer Function Model for Frequency Dependent Transmission Lines - Schäfer, Maximilian; Rabenstein, Rudolf; Strobl, Christian (2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017)
Measurements of the input impedance of electrical transmission lines indicate a frequency dependency of the line parameters. This behavior can be simulated by adjustments to a suitable transfer function model for frequency independent parameters. To this end, functional transformations in time and space set up a transfer function model as a decomposition into individual modes. Adjusting the parameters for each single mode to measured input impedances faithfully describes the frequency dependent behavior. The presented approach does not only model the input impedance, it is also suitable for time domain simulation
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A Multidimensional Wave Digital Filter Algorithm for the 3D Simulation of Room Acoustics - Rabenstein, Rudolf; Schetelig, Th. (Proc. IEEE Image and Multidimensional Digital Signal Processing Workshop (IMDSP98), 1998)
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A multiframe parametric Wiener filter for acoustic echo suppression - Huang, Hai; Hofmann, Christian; Kellermann, Walter; Chen, Jingdong; Benesty, Jacob (Int. Workshop on Acoustic Echo and Noise Control (IWAENC), 2016)
Acoustic echo arises due to the acoustic coupling between the loudspeaker and the microphone in a full-duplex voice communication device. How to reduce or eliminate echo has been an important problem in voice communications. This paper deals with this problem in the short-time Fourier transform (STFT) domain. An approach to acoustic echo suppression (AES) is developed, which uses a linear filter in every STFT subband. A non-parametric variable step-size (NPVSS) normalized-least-mean-square (NLMS) algorithm is used to estimate the echo signal in every STFT bin and time frame. We then define the desired output signal as the sum of the desired near-end speech and a residual echo component. A parametric Wiener filter (PWF) is subsequently derived by minimizing the near-end speech distortion with a constraint on the residual echo. There are two parameters in this PWF, which can be adjusted to control the compromise between the amount of echo attenuation and the degree of near-end speech distortion, and the level of the residual echo remaining at the output. These two parameters can be tuned to optimize the perceptual quality of the echo-reduced signal. Experimental results demonstrate that the developed PWF can effectively attenuate the undesired echo and estimate the near-end signal with little distortion.
Keywords: Acoustic echo suppression (AES); interframe correlation; parametric Wiener filter (PWF); tradeoff filter; residual echo control

A Multirate Approach to Physical Modeling Synthesis Using the Functional Transformation Method - Trautmann, Lutz; Välimäki, V. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2003)
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A Multi-Room Transition Dataset for Blind Estimation of Energy Decay - Götz, Philipp; Gotz, Georg; Meyer-Kahlen, Nils; Lee, Kyung Yun; Prawda, Karolina; Habets, Emanuël; Schlecht, Sebastian (2024 18th International Workshop on Acoustic Signal Enhancement (IWAENC), 2024)
We present an acoustic room impulse response dataset focusing on multi-room environments characterized by complex geometries and acoustic conditions. The dataset is accompanied by positional information and visual documentation in the form of 360° photographs. Additionally, we present a method to estimate acoustic energy decay functions from noisy, reverberant speech signals. We demonstrate the effectiveness of the proposed method based on experiments conducted with the presented dataset.
CRIS-Team Scopus Importer:2024-11-01
Keywords: dataset; energy decay functions; Multi-room acoustics

A Multi-Sensor Object Localization System - Spors, Sascha; Strobel, Norbert; Rabenstein, Rudolf (Vision, Modeling and Visualization (VMV), 2001)
LMS::202

An Acoustic Echo Canceller With Compensation of Nonlinearities - Stenger, Alex; Rabenstein, Rudolf (Proc. EUSIPCO 98, 1998)
LMS::336

An acoustic front-end for interactive TV incorporating multichannel acoustic echo cancellation and blind signal extraction - Reindl, Klaus; Zheng, Yuanhang; Lombard, Anthony; Schwarz, Andreas; Kellermann, Walter (44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010, 2010)
In this contribution, an acoustic front-end for distant-talking interfaces as developed within the European Union-funded project DICIT (Distant-talking interfaces for Control of Interactive TV) is presented. It comprises state-of-the-art multichannel acoustic echo cancellation and blind source separation-based signal extraction and only requires two microphone signals. The proposed scheme is analyzed and evaluated for different realistic scenarios when a speech recognizer is used as back-end. The results show that the system significantly outperforms simple alternatives, i.e., a two-channel Delay & Sum beamformer for speech signal extraction. © 2010 IEE

An acoustic human-machine front-end for multimedia applications - Herbordt, Wolfgang; Buchner, Herbert; Kellermann, Walter (EURASIP J APPL SIG P, 2003)
A concept of robust adaptive beamforming integrating stereophonic acoustic echo cancellation is presented which reconciles the need for low-computational complexity and efficient adaptive filtering with versatility and robustness in real-world scenarios. The synergetic combination of a robust generalized sidelobe canceller and a stereo acoustic echo canceller is designed in the frequency domain based on a general framework for multichannel adaptive filtering in the frequency domain. Theoretical analysis and real-time experiments show the superiority of this concept over comparable time-domain approaches in terms of computational complexity and adaptation behaviour. The real-time implementation confirms that the concept is robust and meets well the practical requirements of real-world scenarios, which makes it a promising candidate for commercial products. © 2003 Hindawi Publishing Corporation.
Keywords: Frequency-domain adaptive filters; Generalized sidelobe canceller; Hands-free acoustic human-machine front-end; Microphone arrays; Robust adaptive beamforming; Stereophonic acoustic echo cancellation

An acoustic human-machine interface with multi-channel sound reproduction - Buchner, Herbert; Kellermann, Walter (2001 IEEE fourth Workshop on Multimedia Signal Processing, 2001)
For hands-free man-machine audio interfaces with multi-channel sound reproduction and automatic speech recognition (ASR), sometimes both an acoustic echo canceller (AEC) and a beamforming (BF) microphone array are necessary for sufficient recognition rates. In the context of multimedia systems, multi-channel sound reproduction (e.g. stereo or 5.1 channel - surround systems) typically requires multi-channel AEC (M-C AEC). With M-C AEC being known to be a very demanding problem in signal processing, no practically relevant simulation results have yet been presented for more than two channels. In this contribution, we examine a recently proposed frequency-domain adaptive filtering scheme with some extensions for the case of more than two loudspeaker channels. The results using this approach show a remarkable performance at a relatively moderate computational complexity. Moreover, we show how to efficiently combine this M-C AEC structure with microphone arrays.

An Adaptive Close-talking Microphone Array - Teutsch, Heinz; Elko, G.W. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2001)

An adaptive microphone array topology for target signal extraction with humanoid robots - Barfuß, Hendrik; Kellermann, Walter (International Workshop on Acoustic Signal Enhancement (IWAENC), 2014)
In this paper, an unsupervised adaptation algorithm for the microphone array topology of a humanoid robot is proposed, so that the spatial filtering performance is improved. In the given exemplary case, the target suppression ('blocking') performance of a geometrically-constrained BSS (GC-BSS) algorithm is shown to improve by the adaptation of the array topology. As a decisive feature, an online performance measure for blind source separation is introduced which provides a robust and reliable estimate of the instantaneous signal separation performance based on currently observable data. Experimental results based on simulated environments confirm the efficacy of the concep
Keywords: Adaptive array topology; blind source separation; humanoid robots; microphone arrays; Target suppression

Analyse und Kompensation von Quantisierungs- und Codierungsfehlern in implementierten Multiratensystemen - Heinle, Frank (Friedrich-Alexander-Universität Erlangen-Nürnberg, 1998)

Analysis and Exploitation of CTU-Level Parallelism in the HEVC Mode Decision Process Using Actor-based Modeling - Rosales, Rafael; Herglotz, Christian; Glaß, Michael; Teich, Jürgen; Kaup, André (In Proceedings of the International Conference on Architecture of Computing Systems (ARCS), 2016)
The new High-Efficiency Video Coding (HEVC) standard achieves much better compression ratios than previous ones by offering multiple coding modes, albeit with a significant increase over the required computational power especially at the encoder side. As the first major contribution, we propose a fine-grained parallelization of the encoding mode decision process using a SystemC actor-based model, exploiting multi-core platforms. Second, based on this model, we analyze achievable speedups compared to the single core sequential implementation of the HM-16.0 reference software. Using four different video sequences, we find that our approach achieves an equivalent rate-distortion performance for different quantization parameter values with a simulated encoding time improvement factor of up to \(9× \) for a maximally parallelized mode decision process. Third, an HEVC encoder has a huge number of different standard-complying encoding modes to choose from for each encoded frame, making the exploration space almost impossible to be fully covered by a brute-force search. Here, we systematically investigate the trade-off in encoding time versus required number of processor cores by proposing a multi-objective Design Space Exploration (DSE) of the mapping of the parallelized mode decision tasks to processing resources, taking as optimization objectives the resulting bitrate, image quality, number of processor cores used, execution time, and total energy consumptio
UnivIS-Import:2016-06-01:Pub.2016.tech.IE.LEN.analys
Keywords: Video encoding · HEVC · ESL · DSE · Parallelization · Actor-based modeling · Hardware/software co-design

Analysis of additional stable gain by frequency shifting for acoustic feedback suppression using statistical room acoustics - Zheng, Chengshi; Hofmann, Christian; Li, Xiaodong; Kellermann, Walter (IEEE Signal Processing Letters, 2016)
Currently, a quantitative prediction of the performance of the frequency shifting (FS) approach for stabilizing acoustic feedback loops of public address (PA) systems is only possible in case of negligibly small direct sound components in the feedback path. By employing a statistical room acoustics model, this letter derives a novel analytical expression for the additional stable gain (ASG) with the FS approach. This allows a quantitative prediction of the performance of FS in the presence of significant direct sound components for the first time. Simulation results confirm the theoretical analysis.
Keywords: Additional stable gain; feedback; statistical room acoustics

Analysis of blocking matrices for generalized sidelobe cancellers for non-stationary broadband signals - Herbordt, Wolfgang; Kellermann, Walter (2002 IEEE International Conference on Acoustic, Speech, and Signal Processing, 2002)
This contribution relates the robust generalized sidelobe canceller (RGSC) with an adaptive blocking matrix (ABM) after Hoshuyama et al. for non-stationary broadband signals with linearly constrained minimum variance (LCMV) beamformers. While alternative approaches only exploit spatial wave field characteristics, it is shown that the ABM introduces dependency on the desired signal power spectral density (PSD) into the array look-direction constraints. The ABM yields better interference suppression than for stationary frequency independent look-direction constraints, since the ABM does not suppress frequencies without desired signal components.

Analysis of Compression of 4D Volumetric Medical Image Datasets Using Multi-View Video Coding Methods - Martin, Uwe-Erik; Kaup, André (Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI, 2008)
Due to the huge amount of data and the increasing usage, compression of 4D medical data sets is essential. These datasets consist of a number of sampled volume elements varying in time and are compressed either with spatial transformation based (e.g. JPEG2000-3D) or motion estimation based schemes. This paper presents a combined approach incorporating both, temporal and spatial information at the same time to compress 4D medical datasets. It is adopting a very similar four-dimensional Multi-View-Coding (MVC) scheme which is known from video processing to 4D medical datasets and compares experimental results with H.264 compression. Rate distortion characteristics show the advantages of such a combined spatio-temporal approac
Keywords: 4-D medical image compression; H.264; Motion analysis; Multi-view coding (MVC)

Analysis of Digital Watermarks Subjected to Optimum Linear Filtering and Additive Noise - Su, Jonathan; Eggers, Joachim; Girod, Bernd (Signal Processing, 2001)

Analysis of displacement compensation methods for wavelet lifting of medical 3-D thorax CT volume data - Schnurrer, Wolfgang; Seiler, Jürgen; Wige, Eugen; Kaup, André (2012 IEEE Visual Communications and Image Processing, VCIP 2012, 2012)
A huge advantage of the wavelet transform in image and video compression is its scalability. Wavelet-based coding of medical computed tomography (CT) data becomes more and more popular. While much effort has been spent on encoding of the wavelet coefficients, the extension of the transform by a compensation method as in video coding has not gained much attention so far. We will analyze two compensation methods for medical CT data and compare the characteristics of the displacement compensated wavelet transform with video data. We will show that for thorax CT data the transform coding gain can be improved by a factor of 2 and the quality of the lowpass band can be improved by 8 dB in terms of PSNR compared to the original transform without compensation. © 2012 IEE
Keywords: Computed Tomography; Discrete wavelet transforms; Motion compensation; Scalability; Signal analysis

Analysis of dual-channel ICA-based blocking matrix for improved noise estimation - Zheng, Yuanhang; Reindl, Klaus; Kellermann, Walter (EURASIP Journal on Advances in Signal Processing, 2014)
For speech enhancement or blind signal extraction (BSE), estimating interference and noise characteristics is decisive for its performance. For multichannel approaches using multiple microphone signals, a BSE scheme combining a blocking matrix (BM) and spectral enhancement filters was proposed in numerous publications. For such schemes, the BM provides a noise estimate by suppressing the target signal only. The estimated noise reference is then used to design spectral enhancement filters for the purpose of noise reduction. For designing the BM, 'Directional Blind Source Separation (BSS)' was already proposed earlier. This method combines a generic BSS algorithm with a geometric constraint derived from prior information on the target source position to obtain an estimate for all interfering point sources and diffuse background noise. In this paper, we provide a theoretical analysis to show that Directional BSS converges to a relative transfer function (RTF)-based BM. The behavior of this informed signal separation scheme is analyzed and the blocking performance of Directional BSS under various acoustical conditions is evaluated. The robustness of Directional BSS regarding the localization error for the target source position is verified by experiments. Finally, a BSE scheme combining Directional BSS and Wiener-type spectral enhancement filters is described and evaluated. © 2014 Zheng et al.; licensee Springe

Analysis of Error Propagation in Hybrid Video Coding with Application to Error Resilience - Färber, Niko; Stuhlmüller, Klaus; Girod, Bernd (International Conference on Image Processing ICIP-99, 1999)
LMS::299

Analysis of in-loop denoising in lossy transform coding - Wige, Eugen; Yammine, Gilbert; Amon, Peter; Hutter, Andreas; Kaup, André (28th Picture Coding Symposium, PCS 2010, 2010)
When compressing noisy image sequences, the compression efficiency is limited by the noise amount within these image sequences as the noise part cannot be predicted. In this paper, we investigate the influence of noise within the reference frame on lossy video coding of noisy image sequences. We estimate how much noise is left within a lossy coded reference frame. Therefore we analyze the transform and quantization step inside a hybrid video coder, specifically H.264/AVC. The noise power after transform, quantization, and inverse transform is calculated analytically. We use knowledge of the noise power within the reference frame in order to improve the inter frame prediction. For noise filtering of the reference frame, we implemented a simple denoising algorithm inside the H.264/AVC reference software JM15.1. We show that the bitrate can be decreased by up to 8.1% compared to the H.264/AVC standard for high resolution noisy image sequences. © 2010 IEE
Keywords: High quality compression; Predictor denoising; Quantization effects

Analysis of Inter-Layer Prediction and Hierarchical Prediction Structures in Scalable Video Coding - Li, X.; Amon, P.; Hutter, Andreas; Kaup, André (IEEE Transactions on Broadcasting, 2011)
Scalable video coding (SVC) is a good approach for video services over heterogeneous networks. To achieve efficient video broadcasting systems, a good understanding on the performance of coding tools in SVC is necessary. In this paper, the efficiency of inter-layer prediction tools in SVC extension of H.264/AVC is thoroughly investigated by simulations. First, it is shown that inter-layer prediction is more efficient for fast/complex sequences than for slow/simple scenarios. Second, among the three inter-layer prediction methods, inter-layer residual prediction contributes most gains in simulations suggested by SVC standardization group. Nevertheless, all of them are comparatively less efficient for sequences with many details in the environment of spatial scalability. In addition, medium-grain quality scalability (MGS) outperforms coarse-grain quality scalability (CGS): MGS is able to provide up to 1 dB gain over CGS while keeping an even higher flexibility. However, MGS introduces a relatively large PSNR fluctuation which impacts visual quality. © 2006 IEEE.
Keywords: Inter-layer prediction; Scalable video coding; Single layer video coding

Analysis of Mesh-Based Motion Compensation in Wavelet Lifting of Dynamical 3-D+t CT Data - Schnurrer, Wolfgang; Richter, Thomas; Seiler, Jürgen; Kaup, André (IEEE International Workshop on Multimedia Signal Processing, 2012)
Factorized in the lifting structure, the wavelet transform can easily be extended by arbitrary compensation methods. Thereby, the transform can be adapted to displacements in the signal without losing the ability of perfect reconstruction. This leads to an improvement of scalability. In temporal direction of dynamic medical 3-D+t volumes from Computed Tomography, displacement is mainly given by expansion and compression of tissue. We show that these smooth movements can be well compensated with a mesh-based method. We compare the properties of triangle and quadrilateral meshes. We also show that with a mesh-based compensation approach coding results are comparable to the common slice wise coding with JPEG 2000 while a scalable representation in temporal direction can be achieve

Analysis of Multi-Reference Block Matching for Multi-View Video Coding - Kaup, André; Fecker, Ulrich (7th Workshop Digital Broadcasting, 2006)
Increasing interest can be observed in applications of image-based rendering, especially three-dimensional televi-sion (3D TV). In such systems, the original object or scene is recorded using a setup of several cameras. From the recorded video streams, arbitrary views can be interpolated, so that the user can navigate freely around the scene. However, the amount of data involved is huge, and efficient compression is needed. Several coding schemes have been proposed which exploit the spatial correlation between the views in addition to the temporal correlation between subsequent frames. In this paper, the block matching step of multi-view video coding is statistically analysed. Simulation results for several test sequences are presented. It is shown how much prediction gain can be achieved by introducing block matching across the camera views. The distribution of best matches among a set of possible references is investigated in detail, and from these results, conclusions can be drawn about the reasonable selection of reference frames in a practical multi-view video coding schem
LMS::639

Analysis of Neural Image Compression Networks for Machine-to-Machine Communication - Fischer, Kristian; Forsch, Christian; Herglotz, Christian; Kaup, André (IEEE International Conference on Image Processing (ICIP), 2021)
Video and image coding for machines (VCM) is an emerging field that aims to develop compression methods resulting in optimal bitstreams when the decoded frames are analyzed by a neural network. Several approaches already exist improving classic hybrid codecs for this task. However, neural compression networks (NCNs) have made an enormous progress in coding images over the last years. Thus, it is reasonable to consider such NCNs, when the information sink at the decoder side is a neural network as well. Therefore, we build-up an evaluation framework analyzing the performance of four state-of-the-art NCNs, when a Mask R-CNN is segmenting objects from the decoded image. The compression performance is measured by the weighted average precision for the Cityscapes dataset. Based on that analysis, we find that networks with leaky ReLU as non-linearity and training with SSIM as distortion criteria results in the highest coding gains for the VCM task. Furthermore, it is shown that the GAN-based NCN architecture achieves the best coding performance and even out-performs the recently standardized Versatile Video Coding (VVC) for the given scenari
Keywords: Neural Compression Networks; Video Coding for Machines; Machine-to-Machine Communication

Analysis of Neural Video Compression Networks for 360-Degree Video Coding - Regensky, Andy; Brand, Fabian; Kaup, André (Picture Coding Symposium (PCS), 2024)

Analysis of prediction algorithms for residual compression in a lossy to lossless scalable video coding system based on HEVC - Heindel, Andreas; Wige, Eugen; Kaup, André (Applications of Digital Image Processing XXXVII, 2014)
Lossless image and video compression is required in many professional applications. However, lossless coding results in a high data rate, which leads to a long wait for the user when the channel capacity is limited. To overcome this problem, scalable lossless coding is an elegant solution. It provides a fast accessible preview by a lossy compressed base layer, which can be refined to a lossless output when the enhancement layer is received. Therefore, this paper presents a lossy to lossless scalable coding system where the enhancement layer is coded by means of intra prediction and entropy coding. Several algorithms are evaluated for the prediction step in this paper. It turned out that Sample-based Weighted Prediction is a reasonable choice for usual consumer video sequences and the Median Edge Detection algorithm is better suited for medical content from computed tomography. For both types of sequences the effciency may be further improved by the much more complex Edge-Directed Prediction algorithm. In the best case, in total only about 2.7% additional data rate has to be invested for scalable coding compared to single-layer JPEG-LS compression for usual consumer video sequences. For the case of the medical sequences scalable coding is even more efficient than JPEG-LS compression for certain values of Q
Keywords: Enhancement layer; HEVC; Lossy to lossless; Scalable coding; SELC; SWP

Analysis of Processing Pipelines in Digital RAW Cameras - Schöberl, Michael; Keinert, J.; Kaup, André; Foessel, S. (Microelectronic Systems: Circuits, Systems and Applications, 2011)
Traditionally, image and video processing algorithms start from an RGB image. However, current image sensors deliver camera raw data that needs additional processing and interpolation for conversion into an RGB representation. While recent research delivers important improvements in image quality for processing and reconstruction of raw images, these algorithms come with a heavy computational complexity. Consequently, they are not suited for mobile solutions such as cameras for media production, mobile phones or surveillance. Offline processing on the other hand offers both higher computational power and better flexibility and is well suited for executing those algorithms. The workflow for utilizing this enhanced quality thus requires a shift from camera centric imaging to new off-camera processing strategies. This requires a novel infrastructure for transportation and interchange and enables the possibility for development of even more sophisticated algorithms for processing of camera raw data. This contribution highlights the challenges and possibilities arising from the above mentioned paradigm shift. We discuss algorithms that should stay within the camera and algorithms that benefit from offloading. Our research contributes to the increase in image quality of workflows for future video application
Keywords: Image Sensor; Calibration Data; Lossy Compression; Color Filter Array; Correlate Double Sampling

Analysis of spatial filtering in neural spatiospectral filters and its dependence on training target characteristics - Briegleb, Annika; Kellermann, Walter (EURASIP Journal on Audio, Speech, and Music Processing, 2024)
Mask-based multichannel speech enhancement methods based on artificial neural networks estimate a mask that is applied to the multichannel input signal or a reference channel to obtain the estimated desired signal. For the estimation, both spectral and spatial cues from the multichannel input can be used. However, the interplay of the two inside the neural network is typically unknown. In this contribution, we propose a framework to analyze neural spatiospectral filters (NSSFs) with respect to their capabilities to extract and represent spatial information. We explicitly take the characteristics of the training target signal into account and analyze its effect on the functionality of the NSSF. Using two conceptually different NSSFs as example, we show that not all NSSFs use spatial information under all circumstances and that the training target signal has a significant influence on the spatial filtering behavior of an NSSF. These insights help to assess the signal processing capabilities of neural networks and allow to make informed decisions when configuring, training, and deploying NSSF
Keywords: spatial filtering; multichannel speech enhancement; DNN interpretability

Analysis of spatio-temporal prediction methods in 4D volumetric medical image datasets - Martin, Uwe-Erik; Kaup, André (2008 IEEE International Conference on Multimedia and Expo, ICME 2008, 2008)
Due to the huge amount of data and the increasing utilization of 4D medical image processing, compression of such data sets is essential. Unlike moving 3D objects in computer graphic applications, medical 4D datasets consist of a number of sampled volume elements, varying in time. Based on an analysis of H.264 compression for such data, this paper presents a spatio-temporal prediction scheme leveraging effective block-based prediction for 4D volumetric image data. Experimental results show that this new spatiotemporal approach achieves better prediction results than pure spatial or temporal schemes. © 2008 IEE
Keywords: Compression; Fourdimensional (4-D) medical image; H.264; Motion analysis

Analysis of the performance and limitations of ICA-based relative impulse response identification - Meier, Stefan; Kellermann, Walter (23rd European Signal Processing Conference (EUSIPCO), 2015)
Estimating impulse responses for a single source is a crucial problem for many applications in audio signal processing, such as source extraction. Since absolute impulse responses are hard to identify, relative impulse responses or, equivalently, relative transfer functions are identified instead. Independent Component Analysis (ICA) for convolutivemixtures offers the possibility to determine relative impulse responses implicitly by separating the target source from interfering sources. In this paper, fundamental limitations of relative transfer function (RTF) estimation are analyzed by calculating least-squares (LS)-optimal estimates in adverse scenarios, where the influence of scatterers and reverberation on the performance must be accounted for. Hereupon, ICA-based RTF estimation in the TRINICON framework is compared with the LS-optimal estimate
Keywords: Independent component analysis; impulse response estimation; relative transfer functions

Analysis of the Quantization Error in Digital Multipliers with Small Wordlength - Dehner, G.; Rabenstein, Rudolf; Schäfer, Maximilian; Strobl, Christian (European Signal Processing Conf. (EUSIPCO), 2016)
The analysis of the quantization error in fixed-point arithmetic is usually based on simplifying assumptions. The quantization error is modelled as a random variable which is independent of the quantized variable. This contribution investigates the wordlength reduction of a digital multiplier in greater detail. The power spectrum of the quantization is expressed by the power spectrum of the multiplier input. The analytical results agree with measurements of the quantization error. The presented error model is shown to be superior to the simplified one for wordlengths in the range of eight bi

Analysis of two generic wiener filtering concepts for binaural speech enhancement in hearing aids - Reindl, Klaus; Zheng, Yuanhang; Kellermann, Walter (18th European Signal Processing Conference, EUSIPCO 2010, 2010)
In this paper, we present a detailed analysis for two generic singlechannel Wiener filtering concepts for binaural hearing aids, namely, a dual-channel filter approach (individual filters for individual channels) and a single filter approach (one filter applied to both channels). After a general description of the concept, this scheme is thoroughly discussed for a two-source scenario and especially the influence of a common noise estimate on the two different concepts is analyzed in detail. Moreover, by evaluating the interaural transfer function (ITF) comprising the interaural level differences (ILDs) as well as interaural phase/time differences (IPDs/ITDs), the influence of both Wiener filter approaches on these binaural cues is analyzed. The theoretical findings are confirmed by experimental results. © EURASIP, 201

Analysis of Video Transmission over Lossy Channels - Stuhlmüller, Klaus; Färber, Niko; Link, M.; Girod, Bernd (IEEE Journal on Selected Areas in Communications, 2000)
LMS::129

Analysis on Spatial Scalable Multiview Video Coding with Wavelets - Garbas, Jens-Uwe; Kaup, André (2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09, 2009)
Scalable multiview video coding might become important in future immersive telecommunication scenarios. Therefore, we present a fully scalable multiview video coding framework that is based on wavelets. It offers scalable decoding of the bitstream in the time, view, spatial, and quality dimensions. Whereas its performance is already comparable to state-of-the-art multiview video coding at the full spatial resolution, the quality of the downscaled spatial resolution is not satisfying. After reviewing the reasons for this, we propose an extended coding scheme where a spatial wavelet transform is performed first and the high spatial resolution layers of the multiview video sequence are predicted from the lower ones in the wavelet domain. The structure and performance of this in-band prediction are analyzed and several enhancements are proposed. Finally, it can be shown that efficient spatial scalability can be achieved in this framework with only minor performance loss at the full resolution. © 2009 IEE

Analytical Models for Particle Diffusion and Flow in a Horizontal Cylinder with a Vertical Force - Schäfer, Maximilian; Wicke, Wayan; Rabenstein, Rudolf; Schober, Robert (IEEE International Conference on Communications (ICC), 2019)
This paper considers particle propagation in a cylindrical molecular communication channel, e.g. a simplified model of a blood vessel. Emitted particles are influenced by diffusion, flow, and a vertical force induced e.g. by gravity or magnetism. The dynamics of the diffusion process are modeled by multi-dimensional transfer functions in a spatio-temporal frequency domain. Realistic boundary conditions are incorporated by the design of a feedback loop. The result is a discrete-time semi-analytical model for the particle concentration in the channel. The model is validated by comparison to particle-based simulations. These numerical experiments reveal that the particle concentration of the proposed semi-analytical model and the particle-based model are in excellent agreement. The analytical form of the proposed solution provides several benefits over purely numerical models, e.g. high variability, existence of low run-time algorithms, extendability to several kinds of boundary conditions, and analytical connection to parameters from communication theor
Keywords: Numerical models; Eigenvalues and eigenfunctions; Boundary conditions; Diffusion processes; Receivers; Transfer functions; Magnetic forces

Analyzing Facial Expressions for Virtual Conferencing - Eisert, Peter; Girod, Bernd (IEEE Computer Graphics and Applications, 1998)
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An analytical fault model for direct current lines - Strobl, Christian; Schäfer, Maximilian; Rabenstein, Rudolf (2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016, 2016)
DC grids of system voltages above 20V demand different safety concepts compared to conventional AC grids. More elaborate protection devices have to be developed to detect not only high-power, but also low-power faults. The discrimination between faults and load variations can be supported by model-based machine learning methods. For this purpose, this contribution developes time-invariant and linearized system-models with chains of two-ports including possible faults. To consider faults in transmission lines occuring after steady-state system conditions, the initial distribution of voltages and currents is modeled by spatially concentrated equivalent sources. This approach leads to an analytic frequency domain solution without spatial discretization. Measurements on a two-core cable compare favourably with this closed form mode

An Analytical Model of Diffusion in a Sphere with Semi-Permeable Boundary - Schäfer, Maximilian; Rabenstein, Rudolf (4th Workshop on Molecular Communications, 2019)
Modeling biological systems is of high interest in molecular communications. Exact analytical models of biological environments, e.g. cells or blood vessels offer several advantages in analysis and design of molecular communication channels. For the diffusion of particles in cells or their transport through a chain of cells functional transformations are a promising modeling approach. This contribution considers the modeling of particle diffusion in a sphere with impedance boundary conditions, serving e.g. as a simple model for diffusion in cells bounded by a semi-permeable membran

An Angular Frequency Domain Metric for the Evaluation of Wave Field Rendering Technique - Canclini, A.; Annibale, Paolo; Antonacci, F.; Sarti, A.; Rabenstein, Rudolf; Tubaro, S. (19th European Signal Processing Conference, EUSIPCO 2011, 2011)
Spatial sound rendering techniques are based on established theoretical frameworks. The theoretical wave field predicted by the underlying of a specific rendering technique is an approximation of the desired ideal wave field. Moreover the practical implementations require often compromises which cause an additional deviation of the rendered wave field from the desired one. To assess these deviations, metrics are devised which quantify the difference of two wave fields. Typically they rely on the comparison of the wave fields at a discrete set of positions in the area of interest (discrete-space metric). In this paper a novel metric is proposed which is formulated in the angular frequency domain. It expresses the difference of two wave fields in terms of their Fourier series angular coefficients. It is efficient since it avoids the spatial summation over a discrete grid in the area of interest, moreover it is flexible since it considers a weighting function to focus the evaluation on specific regions of spatial and temporal frequencies. After a theoretical treatment such a metric is computed for simulated and measured data and compared with a discrete-space metric. The comparison shows that the new metric yields results which allow a flexible assessment of the characteristics of the rendering techniques. © 2011 EURASI

An approach to global noise control by wave field synthesis - Kuntz, Achim; Rabenstein, Rudolf (12th European Signal Processing Conference, EUSIPCO 2004, 2004)
Current approaches for active noise control in large listening spaces require a high number of sensors to control the sound field within a given volume. This contribution presents a new approach based on the theory of acoustic wave propagation. It exploits the fact, that a distribution of sources and sensors on the boundary of the listening space is sufficient for global control of the enclosed field. This principle has already been successfully exploited for spatial audio reproduction and is now applied to active noise control. Theoretical considerations, simulations, and measurements with a wave field synthesis array show that this approach is capable of considerable noise reduction in low-reverberant enclosures. Extensions to listening spaces with higher reverberation times are straightforward but require an increased number of output channels.

An Approach to Listening Room Compensation with Wave Field Synthesis - Spors, Sascha; Kuntz, Achim; Rabenstein, Rudolf (Proceedings of the AES 24th International Conference, 2003)
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A natural acoustic front-end for interactive TV in the EU-project DICIT - Marquardt, Lutz; Svaizer, P.; Mabande, Edwin; Brutti, A.; Zieger, C.; Omologo, M.; Kellermann, Walter (PACRIM 2009 - 2009 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 2009)
"Distant-talking Interfaces for Control of Interactive TV" (DICIT) is a European Union-funded project whose main objective is to integrate distant-talking voice interaction as a complementary modality to the use of a remote control in interactive TV systems. Hands-free and seamless control enables a natural user-system interaction providing a suitable means to greatly ease information retrieval. In the given living room scenario the system recognizes commands spoken by multiple and possibly moving users, even in the presence of background noise and TV surround audio. This paper focuses on the multichannel acoustic front-end (MCAF) processing for acoustic scene interpretation which is based on the combination of multi-channel acoustic echo cancellation, blind source separation, beamform-ing, acoustic event classification, and multiple speaker localization. The fully functional DICIT prototype consists of the MCAF, automatic speech recognition, natural language understanding, mixed-initiative dialogue and satellite connection. © 2009 IEE

An audio-visual database for evaluating person tracking algorithms - Krindis, M.; Stamou, G.; Teutsch, Heinz; Spors, Sascha; Nikolaidis, N.; Rabenstein, Rudolf; Pitas, I. (2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05, 2005)
This paper presents an audio-visual database that can be used as a reference database for testing and evaluation of video, audio or joint audio-visual person tracking algorithms, as well as speaker localization methods. Additional possible uses include the testing of face detection and pose estimation algorithms. A number of different scenes are included in the database, ranging from simple to complex scenes that can challenge existing algorithms. They include different subjects, with appearances that can cause problems to video tracking algorithms, (e.g. facial features such as beards, glasses, etc.), optimal and artificially created sub-optimal lighting conditions, subject movement based on simple as well as random motion trajectories, different distances from the camera/microphones and occlusion. The database incorporates ground truth data (3-D position in time) originating from a commercially available 4-camera infrared (IR) tracking system. Examples of how the database can be used to evaluate video and audio tracking algorithms are also provided. © 2005 IEE

An Efficient Alternative to Network Pruning Through Ensemble Learning - Pöllot, Martin; Zhang, Rui; Kaup, André (Proceedings of the International Conference on Acoustics, Speech and Signal Processing, 2020)
Convolutional Neural Networks (CNNs) currently represent the best tool for classification of image content. CNNs are trained in order to develop generalized expressions in form of unique features to distinguish different classes. During this process, one or more filter weights might develop the same or similar values. In this case, the redundant filters can be pruned without damaging accuracy.Unlike normal pruning methods, we investigate the possibility of replacing a full-sized convolutional neural network with an ensemble of its narrow versions. Empirically, we show that the combination two narrow networks, which has only half of the original parameter count in total, is able to contest and even outperform the full-sized original network. In other words, a pruning rate of 50% can be achieved without any compromise on accuracy. Furthermore, we introduce a novel approach on ensemble learning called ComboNet, which increases the accuracy of the ensemble even furthe
Keywords: Deep Learning; Convolutional Neural Network; Resource-efficient Machine Learning; Network Pruning; Ensemble Learning

An Efficient and Accurate Structured Light Algorithm for Direct Measurement of Cylindrical Surface Parameters - Sörgel, Wolfgang; Schalkoff, R. J. (Optical Engineering, 1997)
LMS::388

An Efficient Combination of Multi-Channel Acoustic Echo Cancellation With a Beamforming Microphone Array - Buchner, Herbert; Herbordt, Wolfgang; Kellermann, Walter (Proc. Int. Workshop on Hands-Free Speech Communication (HSC), 2001)
LMS::140

An Efficient Fourier Method for 3-D Radon Inversion in Exact Cone-Beam CT Reconstruction - Schaller, Stefan; Flohr, Th.; Steffen, Peter (IEEE Transactions on Medical Imaging, 1998)
LMS::349

An Eigenfunction Approach to Parameter Estimation for 1D Diffusion Problems - Schäfer, Maximilian; Rabenstein, Rudolf; Ruderer, Alexander (European Control Conference (ECC), 2019)
The behavior of a distributed parameter system can be represented by an expansion into eigenfunctions. It allows to calculate the output signal in dependence on the parameters of the system. This contribution considers the inverse problem: Estimate the system parameters from noisy measurements of the output. To this end, an eigenfunction expansion serves to establish a state-space description of the distributed parameter system. The corresponding state-space matrices define an extended Kalman filter to perform estimation and tracking of parameter values. This approach is shown here for a diffusion problem in one spatial dimension. The value of the diffusion parameter is estimated from a simulated particle concentration under varying conditions. Problems of this kind arises for example in the emerging field of molecular communication
Keywords: Modeling; Distributed parameter systems; Biological systems

An error-based recursive filling ordering for image error concealment - Koloda, Jan; Seiler, Jürgen; Kaup, André; Sanchez, V.; Peinado, A. (IEEE International Conference on Image Processing (ICIP), 2014)
Many image error concealment (EC) algorithms can be employed to reconstruct a lost region by dividing it into a set of (sub)blocks which are estimated from a set of known context pixels. In turn, these former pixels may have been previously obtained by estimation. In this situation, the order in which the lost region is recursively filled will clearly condition the resulting reconstruction. This paper proposes a novel filling order aimed to improve the performance of EC algorithms that are applied recursively over the lost area. It takes into account the reconstruction quality of the already concealed blocks in order to determine the filling order. Blocks surrounded by known pixels or by high quality reconstructions are prioritized. The proposed technique is applicable to a wide range of EC algorithms and achieves an improvement of up to 1dB (in PSNR) with negligible additional computational loa
Keywords: Error Concealment; Filling Order; Recursive Recovery

A Neural Network-based Nonlinear Acoustic Echo Canceller - Halimeh, Mhd Modar; Hümmer, Christian; Kellermann, Walter (IEEE Signal Processing Letters, 2019)

A new algorithm for reducing the requantization loss in video transcoding - Bialkowski, Jens; Barkowsky, Marcus; Kaup, André (14th European Signal Processing Conference, EUSIPCO 2006, 2006)
Video transcoders are devices that convert one video bitstream into another type of bitstream, either with or without standard format conversion. One step to be applied in video transcoders is the requantization of the transform coefficients, if an adaptation to a lower data rate is necessary. During this step, the quality is in most cases degraded compared to a single quantization. This is a consequence of non-overlapping quantization characteristics of the input and the output quantizer. In this work we propose a new choice of the reconstruction level for the requantization step depending on the effective quantization curve of both quantization parameters involved. The reconstruction level is calculated such that it is centered in each effective quantization interval after requantization. Compared to the standard midpoint requantization this leads to quality gains of 3 dB PSNR for most pairs of input and output quantization parameters (QP). The algorithm is useful for intra-and inter-frame codin

A New Approach to the Compensation of Aliasing in Transform and Subband Coders - Heinle, Frank (Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997)
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A New Approach Towards Description of Arbitrarily Shaped Image Segments - Kaup, André; Aach, T. (IEEE International Workshop on Intelligent Signal Processing and Communication Systems, Taipei, 1992)
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A new concept for feature-domain dereverberation for robust distant-talking ASR - Sehr, Armin; Kellermann, Walter (2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, 2007)
The feature-domain dereverberation capabilities of a novel approach for automatic speech recognition in reverberant environments are investigated in this paper. By combining a network of clean speech HMMs and a reverberation model, the most likely combination of the HMM output and the reverberation model output is found during decoding time by an extended version of the Viterbi algorithm. We show in this paper that the most likely HMM output represents a good estimate of the clean speech feature sequence and can be used as input to subsequent speech recognizers. © 2007 IEE
Keywords: Dereverberation; Distant-talking speech recognition; Feature-domain processing; Robust speech recognition; Viterbi decoding

A New Control Algorithm for the Measurement of Variable Air Change Rates - Rabenstein, Rudolf; Heidt, Frank Dietrich (Proceedings of the Twelvth AIVC Conference on Air Movement and Ventilation Control within Buildings, 1991)
A new algorithm for the continuous measurement of variable air change rates with tracer gases will be presented. It differs from the constant concentration method by allowing the concentration level to vary according to the air change rate. Also the mixing process of tracer gas within the room under investigation is considered and limited measurement ranges and injection rates of the tracer gas equipment can be accounted for. The new algorithm has a number of advantages, such as quick response to variations in the air change rate and reduced tracer gas consumption. The development of the control strategy is described and its practical applicability is shown by measurements in a laboratory room.

A new uncertainty decoding scheme for DNN-HMM hybrid systems with multichannel speech enhancement - Hümmer, Christian; Schwarz, Andreas; Maas, Roland; Barfuß, Hendrik; Astudillo, Ramon Fernandez; Kellermann, Walter (International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016)
Uncertainty decoding combines a probabilistic feature description with the acoustic model of a speech recognition system. For DNN-HMM hybrid systems, this can be realized by averaging the DNN outputs produced by a finite set of feature samples (drawn from an estimated probability distribution). In this article, we employ this sampling approach in combination with a multi-microphone speech enhancement system. We propose a new strategy for generating feature samples from multichannel signals, based on modeling the spatial coherence estimates between different microphone pairs as realizations of a latent random variable. From each coherence estimate, a spectral enhancement gain is computed and an enhanced feature vector is obtained, thus producing a finite set of feature samples, of which we average the respective DNN outputs. In the experimental part, this new uncertainty decoding strategy is shown to consistently improve the recognition accuracy of a DNN-HMM hybrid system for the 8-channel REVERB Challenge tas
Keywords: DNN-based acoustic model; multichannel speech enhancement; uncertainty decoding

An extended multidelay filter: Fast low-delay algorithms for very high-order adaptive systems - Buchner, Herbert; Benesty, J.; Kellermann, Walter (2003 IEEE International Conference on Accoustics, Speech, and Signal Processing, 2003)
We propose a novel class of efficient adaptive algorithms in the frequency domain that is tailored to very long adaptive filters and highly autocorrelated input signals as they arise, e.g., in high-quality full-duplex audio applications. The approach exhibits good tracking capabilities of the signal statistics and very low delay. Moreover, it is shown that the low order of computational complexity of the conventional frequency-domain adaptive algorithms can be maintained thanks to efficient realizations. The algorithm allows a tradeoff between the well-known multidelay filter (MDF) and the recursive least-squares (RLS) algorithm. It is also well suited for an efficient generalization to the multichannel case.

An improved combination of directional BSS and a source localizer for robust source separation in rapidly time-varying acoustic scenarios - Zheng, Yuanhang; Lombard, Anthony; Kellermann, Walter (2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays, HSCMA, 2011)
In rapidly time-varying acoustic scenarios, Blind Source Separation (BSS) often suffers from problems with slow convergence and poor separation performance. In this paper, we propose an improved scheme composed of Directional BSS and a source localizer for robustly separating and quickly tracking sources in a rapidly time-varying scene. Directional BSS is defined as a generic BSS algorithm combined with geometric constraints which are roughly equivalent to a set of delay⊂tract beamformers. This scheme combines benefits from BSS and null beamforming and achieves high performance for separating sources of known locations. For estimating the Time-Difference-Of- Arrival (TDOA) of each source, another BSS system running in parallel to the main Directional BSS is exploited to serve as a source localizer. Experimental results illustrate the efficiency of the proposed concept. © 2011 IEE
Keywords: Blind source separation; geometric constraints; shadow BSS

An improved uncertainty decoding scheme with weighted samples for multi-channel DNN-HMM hybrid systems - Kellermann, Walter; Hümmer, Christian; Astudillo, Ramón Fernández (Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2017)
In this paper, we improve a recently-proposed uncertainty decoding scheme for DNN-HMM (deep neural network - hidden Markov model) hybrid systems, which models acoustic features as random variables. This uncertainty decoding strategy averages DNN outputs produced by a finite set of feature samples to approximate the posterior likelihoods of the context-dependent HMM states. As main innovation, we propose a weighted (instead of arithmetic) DNN-output averaging based on a minimum classification error criterion and apply it to a new probabilistic distortion model for multi-channel front-end signal enhancement schemes. The experimental evaluation is performed on the 8-channel REVERB Challenge task using a DNN-HMM hybrid system with spatial filtering of the microphone signals. It is shown that the recognition accuracy of the DNN-HMM hybrid system improves by incorporating uncertainty decoding and that the proposed weighted DNN-output averaging further reduces the word error rate scores
Keywords: uncertainty decoding; robust DNN-based speech recognition; observation uncertainty; spatial diffuseness features

An integrated real-time system for immersive audio applications - Teutsch, Heinz; Spors, Sascha; Herbordt, Wolfgang; Kellermann, Walter; Rabenstein, Rudolf (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2003, 2003)
A real-time system for immersive audio applications is presented. Sound sources are recorded using a microphone array whose beam is steered according to the output of an acoustic source localization and tracking system. The output of the beamformer (BF) along with the source position updates are continuously transmitted to a wave field synthesis (WFS) system. By using WFS, the sound sources in the recording room are rendered in the reproduction room with the correct spatial cues.
Keywords: Acoustic arrays; Acoustic beams; Audio recording; Frequency; Laboratories; Loudspeakers; Microphone arrays; Production; Real time systems; Speech

An iterative least-squares design method for filters with constrained magnitude response in sound reproduction - Schneider, Martin; Habets, Emanuël (Deutsche Jahrestagung für Akustik - DAGA, 2017)
In this contribution, a previously presented efficient iterative least-squares filter design algorithm is modified such that it yields a filter with a constrained magnitude response. The algorithm is designed to maximize computational efficiency and can be straightforwardly implemente

An MPEG-7 tool for compression and streaming of XML data - Niedermeier, U.; Heuer, J.; Hutter, A.; Stechele, W.; Kaup, André (2002 IEEE International Conference on Multimedia and Expo, ICME 2002, 2002)
In the course of work on the MPEG-7 standard, a binary format with special features for the encoding of XML data was required. These required key features are a high data compression ratio, provision for streaming, dynamic update of the document structure and fast random access of data entities in the compressed stream. To support these features, we propose a novel, schema-aware approach which exploits the knowledge of the standardized MPEG-7 syntax definition of the encoded XML document on the encoder and decoder side. The technique is part of the MPEG-7 standard. This paper gives an overview of the coding algorithm, including a comparison to standard (XML) compression tools.

An nD Model for a Cylindrical Diffusion-Advection Problem with an Orthogonal Force Component - Schäfer, Maximilian; Wicke, Wayan; Rabenstein, Rudolf; Schober, Robert (23rd International Conference on Digital Signal Processing (DSP), 2018)
This paper presents a physical and algorithmic model for a complex propagation problem in a long cylinder, resembling a blood vessel or a pipeline. Particles are diffusing in the cylinder driven by a plug flow, in addition the particles are attracted by an orthogonal force, e.g. due to magnetism or gravity. The dynamics of this propagation problem are modeled by multidimensional (nD) transfer functions. They are established by suitable functional transformations in a spatio-temporal frequency domain by exploiting eigenfunctions of partial differential operators. Nontrivial boundary conditions are incorporated by the design of a feedback loop. The result is a discrete-time semianalytical model for the particle concentration.<br

An Operator Approach for Physical Modelling of Distributed Parameter Systems - Dymkou, Vitali; Rabenstein, Rudolf; Steffen, Peter (6th IMA Conf. on Mathematics in Signal Processing, 2004)
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An Optimal Search Strategy for 3-D Motion Estimation - Steinbach, Eckehard; Chaudhuri, S.; Girod, Bernd (3D Image Analysis and Synthesis, 1997)
LMS::418

A no-reference blocking artifacts visibility estimator in images - Yammine, Gilbert; Wige, Eugen; Kaup, André (2010 17th IEEE International Conference on Image Processing, ICIP 2010, 2010)
In this paper, we present a new low-complexity no-reference metric that assesses the visibility of blocking artifacts in DCT-coded images. The metric works in the spatial domain as well as in the gradient image domain without remarkable changes. Our conducted simulations showed that the metric is highly robust among many image distortion databases and extremely consistent with subjective mean opinion scores (MOS). The metric also provides a blocking visibility map that can be used for adaptive filtering of blocking artifacts. © 2010 IEE
Keywords: Adaptive filtering; Blocking artifacts; No-reference metric; Quality assessment; Subjective tests

An Outlier-Robust Extended Multidelay Filter with Application to Acoustic Echo Cancellation - Buchner, Herbert; Benesty, J.; Gaensler, T.; Kellermann, Walter (Conf. Rec. Intl. Workshop on Acoustic Echo and Noise Control (IWAENC), 2003)
LMS::440

A novel approach for matched reverberant training of HMMs using data pairs - Sehr, Armin; Hofmann, Christian; Maas, Roland; Kellermann, Walter (11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010, 2010)
For robust distant-talking speech recognition, a novel HMM training approach using data pairs is proposed. The data pairs of clean and reverberant feature vectors, also called stereo data, are used for deriving the HMM parameters of a matched-condition reverberant HMM from a well-trained clean-speech HMM in two steps. In the first step, the alignment of the frames to the states is determined from the clean data and the clean-speech HMM. This state-frame alignment (SFA) is then used in the second step to estimate the Gaussian mixture densities for each state of the reverberant HMM by applying the Expectation Maximization (EM) algorithm to the reverberant data. Thus, a more accurate temporal alignment is achieved than by standard matched condition training, and the discrimination capability of the HMMs is increased. Connected digit recognition experiments show that the proposed approach decreases the word error rate (WER) by up to 44% while substantially reducing the training complexity. These improvements will make reverberant training attractive for a wider range of applications. © 2010 ISC
Keywords: Distant-talking speech recognition; Hmm training; Reverberation; Robust ASR; Stereo data

A novel approach to active listening room compensation for wave field synthesis using wave-domain adaptive filtering - Spors, Sascha; Buchner, Herbert; Rabenstein, Rudolf (IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004)
Wave field synthesis is an auralization technique which allows to control the entire wave field within the entire listening area. However, reflections in the listening room interfere with the auralized wave field and may impair the spatial reproduction. Active listening room compensation aims at reducing these impairments by using the playback system. Due to the high number of playback channels used for wave field synthesis, the existing approaches to room compensation are not applicable. A novel approach to active room compensation overcomes these problems by a transformation from the space-time to the wave domain and application of wave-domain adaptive filterin

A Novel Approach to Discrete-Time Domain Simulation of String Sound Fields by Means of Combined Modeling - Escolano, Jose; Pueo, B.; Petrausch, Stefan; Rabenstein, Rudolf (International Computer Music Conference, ICMC 2005, 2005)
The scope of this paper is the time domain simulation of the sound field radiated by a string. Different time domain methods have been successfully implemented for string simulations. These methods not only perform realtime string sound synthesis, but also pressure and particle velocity generated around strings can be obtained. It is very useful to analyze the sound field interaction with external physical structures, as the body of a guitar, for example. This paper proposes a combination of two different time-domain methods for a complete description of the generated sound field. These methods are the Functional Transformation Method (FTM) and the Finite-Difference Time Domain (FDTD) method. They have different advantages, and when used in a mixed model scheme, give a wider broadband and more efficient solution of the sound field. Moreover, real-time modification of parameters without stability shortcomings is also feasibl

A novel Cross-Component Context Model for End-to-End Wavelet Image Coding - Meyer, Anna; Kaup, André (ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023)
In contrast to traditional compression techniques performing linear transforms, the latent space of popular compressive autoencoders is obtained from a learned nonlinear mapping and hard to interpret. In this paper, we explore a promising alternative approach for neural compression, with an autoencoder whose latent space represents a nonlinear wavelet decomposition. Previous work has shown that neural wavelet image coding can outperform HEVC. However, the approach codes color components independently, thereby ignoring inter-component dependencies. Hence, we propose a novel cross-component context model (CCM). With CCM, the entropy model for the chroma latent space can be conditioned on previously coded components exploiting correlations in the learned wavelet space. The proposed CCM outperforms the baseline model with average Bjøntegaard delta rate savings of 2.6 % and 1.6 % for the Kodak and Tecnick image sets. Also, our method is competitive with VVC and learning-based methods.

A novel ego-noise suppression algorithm for acoustic signal enhancement in autonomous systems - Schmidt, Alexander; Löllmann, Heinrich; Kellermann, Walter (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018)
The use of autonomous systems (ASs), such as humanoid robots, drones or self-driving vehicles, has expanded significantly in recent years. For such systems, acoustic scene analysis can provide useful information about the environment and supports the AS to react appropriately. However, compared to most other application areas, analysis and enhancement of acoustic signals captured by ASs is not only complicated by external sources of signal degradation but also by very specific challenges like internal and self-created ego-noise. This paper first gives an overview of a typical acoustic scenario an AS is exposed to. Then, we consider the specific problem of ego-noise suppression and propose to use motor data to predict the characteristic time-varying harmonic structure of ego-noise. This knowledge is then incorporated into a multichannel dictionary-based algorithm. The resulting two-stage ego-noise reduction scheme is evaluated for ego-noise of a humanoid robot and outperforms a comparable method that uses no motor data but a a larger dictionary.<br
Keywords: Acoustic Scene Analysis; autonomous systems; ego-noise reduction; humanoid robot

A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled Image Data Using Locally Fully Connected Layers - Grosche, Simon; Brand, Fabian; Kaup, André (23rd International Workshop on Multimedia Signal Processing (MMSP), 2021)

A novel multidelay adaptive algorithm for Volterra filters in diagonal coordinate representation - Küch, Fabian; Kellermann, Walter (IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004)
In this contribution we present a novel algorithm for the efficient computation of the output of Volterra filters in diagonal coordinate representation (DCR), while allowing for different memory length for each kernel. This is achieved by extending partitioned block filtering methods for fast convolution in the discrete Fourier transform (DFT) domain to Volterra filters. It is shown that the DCR is particularly favorable for modeling the cascade of a Volterra filter followed by a linear filter, as required for applications such as nonlinear acoustic echo cancellation. To obtain a corresponding adaptive structure of the proposed approach, we introduce a generalization of a known DFT-domain adaptive algorithm for linear systems to Volterra filter

A novel normalization and regularization scheme for broadband convolutive blind source separation - Aichner, Robert; Buchner, Herbert; Kellermann, Walter (6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006, 2006)
In this paper we propose a novel blind source separation (BSS) algorithm for convolutive mixtures combining advantages of broadband algorithms with the computational efficiency of narrowband techniques. It is based on a recently presented generic broadband algorithm. By selective application of the Szegö theorem which relates properties of Toeplitz and circulant matrices, a new normalization is derived which approximates well the exact normalization of the generic broadband algorithm presented in [2]. The new scheme thus results in a computationally efficient and fast converging algorithm while still avoiding typical narrowband problems such as the internal permutation problem or circularity effects. Moreover, a novel regularization method for the generic broadband algorithm is proposed and subsequently also derived for the proposed algorithm. Experimental results in realistic acoustic environments show improved performance of the novel algorithm compared to previous approximations. © Springer-Verlag Berlin Heidelberg 200