Kompensation akustischer Echos unter Einfluß von nichtlinearen Audiokomponenten - Stenger, Alexander (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2001)

Lagrange multiplier selection for rate-distortion optimization in SVC - Li, Xiang; Amon, Peter; Hutter, Andreas; Kaup, André (2009 Picture Coding Symposium, PCS 2009, 2009)
The Lagrangian multiplier based rate-distortion optimization (RDO) has been widely employed in single layer video coding. During the development of scalable video coding (SVC) extension of H.264/AVC, it was directly applied in a multilayer scenario. However, such an application is not very efficient since the correlation between layers is not considered in the Lagrange multiplier selection. To improve the overall performance, in this paper a new selection algorithm is presented for RDO in SVC. Simulations show that the proposed method outperforms the recent SVC reference software. With a tiny computational cost, average gains of 0.22 dB and 0.35 dB were achieved in the tests of four-layer quality scalability and three-layer spatial scalability, respectivel
Keywords: Lagrange multiplier selection; Rate-distortion optimization; SVC

Laplace distribution based Lagrangian rate distortion optimization for hybrid video coding - Li, Xiang; Oertel, Norbert; Hutter, Andreas; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2009)
In today's hybrid video coding, Rate-Distortion Optimization (RDO) plays a critical role. It aims at minimizing the distortion under a constraint on the rate. Currently, the most popular RDO algorithm for one-pass coding is the one recommended in the H.264/AVC reference software. It, or HR-A for convenience, is actually a kind of universal method which performs the optimization only according to the quantization process while ignoring the properties of input sequences. Intuitively, it is not efficient all the time and an adaptive scheme should be better. Therefore, a new algorithm Lap-A is presented in this paper. Based on the Laplace distribution of transformed residuals, the proposed Lap-A is able to adaptively optimize the input sequences so that the overall coding efficiency is improved. Cases which cannot be well captured by the proposed models are considered via escape methods. Comprehensive simulations verify that compared with HR-A, Lap-A shows a much better or similar performance in all scenarios. Particularly, significant gains of 1.79 dB and 1.60 dB in PSNR are obtained for slow sequences and B-frames, respectivel
Keywords: Hybrid video coding; Lagrange multiplier selection; Rate-distortion optimization (RDO)

Large-scale multiple input/multiple output system identification in room acoustics - Schneider, Martin; Kellermann, Walter (21st International Congress on Acoustics, ICA 2013 - 165th Meeting of the Acoustical Society of America, 2013)
In audio reproduction scenarios, room acoustics may be described as a multiple-input/multiple-output system response from multiple loudspeaker to multiple microphones in the listening space. This system response may, e.g., be used for an equalization of the listening room and must be identified from observing the available loudspeaker and microphone signals in real-world systems. For few transducers this task is mostly solved, but massive multichannel reproduction with dozens to hundreds of loudspeakers left many research questions open. This contribution points out the fundamental challenges, previous solutions and recent advances. As a key issue, the so-called nonuniqueness problem for multiple-input/multiple-output system identification by adaptive filtering will be discussed along with decorrelation schemes for the loudspeaker signals to alleviate this problem. Successful adaptation algorithms suitable for these scenarios imply considerable computational demands and require additional measures to ensure robustness. Recently emerging system models in spatial transform domains allow for approximative models and seem to be promising for robust real-time implementations. © 2013 Acoustical Society of Americ

Latent Channel Energy in Learned Image Codecs - Prativadibhayankaram, Srivatsa; Richter, Thomas; Fößel, Siegfried; Kaup, André (Proceedings of SPIE - The International Society for Optical Engineering, 2024)
With the advent of learned image compression, numerous models have been developed. These models make use of non-linear transforms that are learnt during the training process, where an image is transformed into a latent space, quantized and entropy coded. At the decoder, the quantized latent is recovered and transformed back to image space through a synthesis transform. In this work, we attempt to present an analysis of the energy distribution across channels. In our prior works, we demonstrated the features captured by the analysis transform, that can provide insights into the bitrate distribution across channels. Building on that, we extend our findings with quantitative measurements. We consider various learned image codecs that are based on the variational autoencoder framework and compare them with Karhunen Loève Transform (KLT) in terms of energy compaction. We also compare the closeness of the learned transforms to KLT to study the relationship between the design of classical codecs and learned codecs.
CRIS-Team Scopus Importer:2024-11-01
Keywords: deep learning; image compression; Linear transform coding; non-linear transform coding

Learned Wavelet Video Coding using Motion Compensated Temporal Filtering - Meyer, Anna; Brand, Fabian; Kaup, André (IEEE Access, 2023)

Learning-based acoustic source localization in acoustic sensor networks using the coherent-to-diffuse power ratio - Brendel, Andreas; Kellermann, Walter (European Signal Processing Conference (EUSIPCO), 2018)
A distributed learning-based algorithm for the localization of acoustic sources in an acoustic sensor network is proposed. It is based on estimates of the Coherent-to-Diffuse Power Ratio (CDR), which serve as feature for the source-microphone distance, i.e., the range. The relation between the estimated CDR and the range is learned by using Gaussian processes for non-parametric regression. The range estimates obtained from evaluating the regression function are fused by a weighted least squares estimation, which is implemented recursively, allowing for a distributed version of the algorithm. The resulting method is computationally efficient, works in highly reverberant and noisy scenarios and needs only a small amount of data shared over the network. The training phase of the algorithm requires only a few labeled observations. We show the efficacy of the approach with data obtained from image-source simulatio
Keywords: Coherent-to-Diffuse Power Ratio; Gaussian Process Regression; Weighted Least Squares; Distributed Algorithm; Acoustic Sensor Network; Localization

Learning-based Acoustic Source Localization using Directional Spectra - Brendel, Andreas; Laufer-Goldshtein, Bracha; Gannot, Sharon; Kellermann, Walter (IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019)

Learning-based acoustic source-microphone distance estimation using the coherent-to-diffuse power ratio - Brendel, Andreas; Kellermann, Walter (IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2018)
We propose a method for estimating the distance between a sound source and a pair of recording microphones. The developed algorithm operates in the short-time Fourier transform domain and is based on estimates of the coherent-to-diffuse power ratio, which provides a measure for the amount of reverberation in each time-frequency bin. For a direct use of these estimates, precise knowledge on the room characteristics is necessary, which is in practice usually not available and hard to obtain. Therefore, we use a learning-based method, which adapts to the characteristics of the room in a training phase and estimates the source-microphone distance in a testing phase. The experiments comprise various setups with simulated and real data. It is shown that the proposed method generalizes well for different microphone positions and works robustly for different source signals, directions of arrival, reverberation times, and signal observation intervals. This leads to a high estimation accuracy at a low computational complexity with a small amount of training data.
Keywords: Acoustic range estimation; Learning; Coherent-to-Diffuse Power Ratio

LEARNING-BASED CONDITIONAL IMAGE CODER USING COLOR SEPARATION - Jia, Panqi; Koyuncu, Ahmet Burakhan; Gaikov, Georgii; Karabutov, Alexander; Alshina, Elena; Kaup, André (2022 PICTURE CODING SYMPOSIUM (PCS), 2022)
CRIS-Team WoS Importer:2023-03-03

Learning-based lossless compression of 3d point cloud geometry - Nguyen, Dat Thanh; Quach, Maurice; Valenzise, Giuseppe; Duhamel, Pierre (ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021)
This paper presents a learning-based, lossless compression method for static point cloud geometry, based on context-adaptive arithmetic coding. Unlike most existing methods working in the octree domain, our encoder operates in a hybrid mode, mixing octree and voxel-based coding. We adaptively partition the point cloud into multi-resolution voxel blocks according to the point cloud structure, and use octree to signal the partitioning. On the one hand, octree representation can eliminate the sparsity in the point cloud. On the other hand, in the voxel domain, convolutions can be naturally expressed, and geometric information (i.e., planes, surfaces, etc.) is explicitly processed by a neural network. Our context model benefits from these properties and learns a probability distribution of the voxels using a deep convolutional neural network with masked filters, called VoxelDNN. Experiments show that our method outperforms the state-of-the-art MPEG G-PCC standard with average rate savings of 28% on a diverse set of point clouds from the Microsoft Voxelized Upper Bodies (MVUB) and MPEG. The implementation is available at https://github.com/Weafre/VoxelDNN.<br

Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors - Nguyen, Dat Thanh; Kaup, André (IEEE International Conference on Image Processing ICIP 2022, 2022)
Most point cloud compression methods operate in the voxel or octree domain which is not the original representation of point clouds. Those representations either remove the geometric information or require high computational power for processing. In this paper, we propose a context-based lossless point cloud geometry compression that directly processes the point representation. Operating on a point representation allows us to preserve geometry correlation between points and thus to obtain an accurate context model while significantly reduce the computational cost. Specifically, our method uses a sparse convolution neural network to estimate the voxel occupancy sequentially from the x,y,z input data. Experimental results show that our method outperforms the state-of-the-art geometry compression standard from MPEG with average rate savings of 52% on a diverse set of point clouds from four different datasets.<br

Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-Driven Image Coding - Fischer, Kristian; Brand, Fabian; Herglotz, Christian; Kaup, André (IEEE International Conference on Image Processing (ICIP), 2022)
Today, visual data is often analyzed by a neural network without any human being involved, which demands for specialized codecs. For standard-compliant codec adaptations towards certain information sinks, HEVC or VVC provide the possibility of frequency-specific quantization with scaling lists. This is a well-known method for the human visual system, where scaling lists are derived from psycho-visual models. In this work, we employ scaling lists when performing VVC intra coding for neural networks as information sink. To this end, we propose a novel data-driven method to obtain optimal scaling lists for arbitrary neural networks. Experiments with Mask R-CNN as information sink reveal that coding the Cityscapes dataset with the proposed scaling lists result in peak bitrate savings of 8.9% over VVC with constant quantization. By that, our approach also outperforms scaling lists optimized for the human visual system. The generated scaling lists can be found under https://github.com/FAU-LMS/VCM_scaling_lists. <
Keywords: Video Coding for Machines; Scaling Lists; Adapted Quantization; VVC; Instance Segmentation

Learning Pitch-Class Representations from Score-Audio Pairs of Classical Music - Weiß, Christof; Zeitler, Johannes; Zunner, Tim; Schuberth, Florian; Müller, Meinard (Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2021)
Chroma or pitch-class representations of audio recordings are an essential tool in music information retrieval. Traditional chroma features relying on signal processing are often influenced by timbral properties such as overtones or vibrato and, thus, only roughly correspond to the pitch classes indicated by a score. Deep learning provides a promising possibility to overcome such problems but requires large annotated datasets. Previous approaches therefore use either synthetic audio, MIDI-piano recordings, or chord annotations for training. Since these strategies have different limitations, we propose to learn transcription-like pitch-class representations using pre-synchronized score-audio pairs of classical music. We train several CNNs with musically inspired architectures and evaluate their pitch-class estimates for various instrumentations including orchestra, piano, chamber music, and singing. Moreover, we illustrate the learned features' behavior when used as input to a chord recognition system. In all our experiments, we compare cross-validation with cross-dataset evaluation. Obtaining promising results, our strategy shows how to leverage the power of deep learning for constructing robust but interpretable tonal representation

Learning True Rate-Distortion-Optimization for End-To-End Image Compression - Brand, Fabian; Fischer, Kristian; Kopte, Alexander; Kaup, André (DCC 2022: 2022 DATA COMPRESSION CONFERENCE (DCC), 2022)
CRIS-Team WoS Importer:2022-09-23

Least-Squares Adaptation of Affine Combinations of Multiple Adaptive Filters - Azpicueta Ruiz, L.A.; Zeller, Marcus; Figueiras-Vidal, Anibal R.; Arenas-Garcia, Jeronimo (2010 IEEE International Symposium on Circuits and Systems, 2010)
Adaptive combinations of adaptive filters are gaining popularity as a flexible and versatile solution to improve adaptive filters performance. In the recent years, combination schemes have focused on two different approaches: Convex and affine combinations, developing principally practical implementations with just two component filters. However, combinations of a higher number of adaptive filters can offer additional advantages, mainly in tracking environments. In this paper, we introduce a practical adaptation scheme for the affine combination of an arbitrary number of filters, including a steady-state analysis where the proposed rule is compared with the optimal combination. Several experiments both in tracking and stationary scenarios serve to demonstrate the appropriate performance of this approac
LMS::842

Least-squares error beamforming using minimum statistics and multichannel frequency-domain adaptive filtering - Aichner, Robert; Herbordt, Wolfgang; Buchner, Herbert; Kellermann, Walter (Conf. Rec. Intl. Workshop on Acoustic Echo and Noise Control (IWAENC), 2003)
LMS::439

Leistungsfähigkeit und Komplexität von ratengesteuerter Bewegungsschätzung in MPEG-4 - Kaup, André; Mooshofer, H. (Multimedia: Anwendungen, Technologie, Systeme, ITG-Fachbericht 156, 1999)
LMS::460

Light-field Acquisition and Processing System for Film Productions - Foessel, S.; Zilly, F.; Schöberl, Michael; Schäfer, P.; Ziegler, M.; Keinert, J. (SMPTE Annual Technical Conference, 2013)
With traditional film cameras, important creative parameters such as the depth of field and the viewpoint are burned into the footage after acquisition without the possibility to change them in post-production. In consequence, special attention is required on set to frame the scene and to pull the focus. Against this background, we propose a light-field capturing and processing system, suitable to be used on set, and allowing to change the camera viewpoint and focal plane in post-production. Our approach involves a synchronized array of compact high definition cameras to capture multiple viewpoints enabling a high image quality of the resulting footage. Based on computational imaging methods virtual camera positions and synthetic apertures will be created afterwards. First scenes captured with such an array will be demonstrate
LMS::1390

Light-field acquisition system allowing camera viewpoint and depth of field compositing in post-production - Zilly, F.; Schöberl, Michael; Schäfer, P.; Ziegler, M.; Keinert, J.; Foessel, S. (International Broadcast Convention Conference (IBC), 2013)
Traditional film and broadcast cameras capture the scene from a single view point while important creative parameters such as the camera position and orientation, the depth of field, and the 3D parallax when using a 3D camera rig, are burned into the footage during acquisition. To realize artistic effects such as the Matrixor the Vertigo-effect with traditional cameras is a complex task which requires skilled personnel. Within the Matrix-effect the scene itself appears frozen, while a camera movement is simulated by placing dozens of cameras in a mainly horizontal arrangement. The vertigo-effect, however, requires physical movement of the camera which is usually mounted on a dolly and translates towards and backwards from the scene while the zoom and possibly the focus are changed accordingly. Usually, the scene needs to be shot multiple times until the best combination of zoom corrections and dolly movement is found. Against this background, we present a system which allows capturing the light-field of a sequence and thus offers the full creative leeway of changing camera paths and orientation as well as setting the focal plane in post-production. In contrast to previous approaches, the handling of the involved acquisition system is compact and thus suitable for on-set operation. In this paper we describe the associated content production and present first results which underlie the suitability of the approac
LMS::1370
Keywords: image sequences; rendering (computer graphics); image sensors; data acquisition; image motion analysis

Lightfield Media Production System using Sparse Angular Sampling - Zilly, F.; Schöberl, Michael; Schäfer, P.; Ziegler, M.; Keinert, J.; Foessel, S. (ACM SIGGRAPH, 2013)
Traditional film and broadcast cameras capture the scene from a single view point or in the case of 3D cameras from two slightly shifted viewpoints. Important creative parameters such as the camera position and orientation, the depth of field, and the amount of 3D parallax are burned into the footage during acquisition. To realize artistic effects such as the matrix- or the vertigo-effect, complex equipment and very skilled personnel is required. Within the former effect the scene itself appears frozen, while a camera movement is simulated by placing dozens of cameras in a mainly horizontal arrangement. The latter, however, requires physical movement of the camera which is usually mounted on a dolly and translates towards and backwards from the scene while the zoom (and focus) are changed accordingly. Beside the demanding requisites towards equipment and personnel, the resulting effects can usually not be changed in post-production. In contrast lightfield acquisition te chniques allow for changing the mentioned parameters in post-production. Traditionally, in absence of a geometric model of the scene, a dense sampling of the lightfield is required. This can be achieved using large cameras arrays as used by [Wilburn et al. 2005] or hand-held plenoptic cameras as proposed by [Ng et al. 2005]. While the former approach is complex in calibration and operation due to the huge number of cameras, the latter suffers from a low resolution per view, as the total resolution of the imaging sensor needs to be shared between all sub-images captured by the individual micro-lense
LMS::1369

Limitations in the extrapolation of wave fields from circular measurements - Kuntz, Achim; Rabenstein, Rudolf (15th European Signal Processing Conference, EUSIPCO 2007, 2007)
This paper analyzes the constraints in extrapolating sound fields from measurements using circular microphone setups. Different representations of 2D wave fields are compared and several derivations for the decomposition into circular harmonics are summarized. Theoretical limits are derived for the order of the obtainable circular harmonic components due to spatial sampling, the measuring radius, and sensor noise. The consequences of order limitations are addressed for the extrapolation of wave fields to different radii. It is shown that even though an exact extrapolation is only possible within the measuring aperture, a coarse extrapolation can be possible beyond the radius of the microphone setup depending on the direction relative to the wave propagation. For this purpose far field approximations are derived for the extrapolation of an order limited decomposition of a plane wave in different directions. Real world measurements show good agreement with these investigations. © 2007 EURASI

Limiting effects of active room compensation using Wave field synthesis - Spors, Sascha; Renk, M.; Rabenstein, Rudolf (118th Convention Spring Preprints 2005, 2005)
Wave field synthesis (WFS) is an auralization technique which allows to control the 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 WFS system for their compensation. Current realizations of WFS systems are limited to the reproduction in a plane only. This reduction in dimensionality leads to effects that limit the performance of active room compensation. This paper analyzes these limiting effects on a theoretical and practical basi

Limits for generalized sidelobe cancellers with embedded acoustic echo cancellation - Herbordt, Wolfgang; Kellermann, Walter (2001 IEEE Interntional Conference on Acoustics, Speech, and Signal Processing, 2001)
This paper analyzes positive synergies and theoretical limits of the combination of acoustic echo cancellation (AEC) and Generalized Sidelobe Cancellers (GSC) for the removal of echoes of loudspeaker signals and local interferers. While the proposed system only requires one AEC for an arbitrary number of microphones, the array gain is limited by the number of sensor channels when all interferers arrive from different directions-of-arrival (DOAs). The paper also shows that the degrees of freedom of the adaptive sidelobe cancelling path are not sufficient when local interferers and acoustic echoes have common DOAs.

Linearized System and Fault Modeling Methods for DC-Grids Including Factorial Analysis - Strobl, Christian; Rabenstein, Rudolf (2021 IEEE 4th International Conference on DC Microgrids (ICDCM), 2021)
For the purpose of refined fault detection in DC grids, linear models of components, of cabling as well as of possible faults can be applied in order to model the first milliseconds after a possible sudden event. Using a semi-analytical model, a factorial analysis of signals at voltage and current sensors is set up – specific signal patterns representing either fault events or changes between normal operation modes are estimated. With these results, refined fault detection methods avoiding false tripping can be implemented and parametrized.
Keywords: DC grids; fault detection; system model; linearization; factorial analysis

Linearly-constrained multichannel interference suppression algorithms derived from a minimum mutual information criterion - Reindl, Klaus; Kellermann, Walter (IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), 2013)
In this contribution, a generic framework for linearly-constrained multichannel noise and interference suppression algorithms is presented. It is derived from a linearly-constrained minimum mutual information (LCMMI) criterion between mutually statistically independent desired and undesired components, which also accounts for three fundamental signal properties characteristic, e.g., for speech and audio signals: Nonwhiteness, nonstationarity, and nongaus-sianity. We demonstrate links to prominent second order statistics-based algorithms such as the linearly-constrained minimum variance (LCMV) filter and its realization as a generalized sidelobe canceller (GSC). Additionally, we will show how specific supervised constrained and unconstrained multichannel algorithms result as special cases. The presented LCMMI concept leads to new insights for the development of improved adaptation algorithms for noise and interference suppression. © 2013 IEE
Keywords: Frost beamformer; generalized sidelobe canceller (GSC); interference cancellation; linearly-constrained minimum variance (LCMV); Minimum mutual information

Listening room compensation for wave field synthesis - Spors, Sascha; Kuntz, Achim; Rabenstein, Rudolf (2003 International Conference on Multimedia and Expo, ICME 2003, 2003)
Common room compensation algorithms are capable of dereverberating the listening room at some discrete points only. Outside these equalization points the sound quality is often even worse compared to the unequalized case. As wave field synthesis in principle allows to control the wave field within the listening area it can also be used to compensate for the reflections caused by the listening room in the complete listening area. We present a novel approach to listening room compensation which is based upon the theory of wave field synthesis and that results in a large compensated area.

Localization of an Unknown Number of Speakers in Adverse Acoustic Conditions Using Reliability Information and Diarization - Brendel, Andreas; Laufer-Goldshtein, Bracha; Gannot, Sharon; Talmon, Ronen; Kellermann, Walter (IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2019)
This paper investigates localization of an arbitrary number of simultaneously active speakers in an acoustic enclosure. We propose an algorithm capable of estimating the number of speakers, using reliability information to obtain robust estimation results in adverse acoustic scenarios and estimating individual probability distributions describing the position of each speaker using convex geometry tools. To this end, we start from an established algorithm for localization of acoustic sources based on the EM algorithm. There, the estimation of the number of sources as well as the handling of reverberation has not been addressed sufficiently. We show improvement in the localization of a higher number of sources and in the robustness in adverse conditions including interference from competing speakers, reverberation and noise.<br
Keywords: Acoustic source localization; Number of speakers estimation; Diarization; EM algorithm

Localization of distinct reflections in rooms using spherical microphone array eigenbeam processing - Sun, Haohai; Kellermann, Walter; Mabande, Edwin; Kowalczyk, Konrad (Journal of the Acoustical Society of America, 2012)
This paper presents an experimental and comparative study of several spherical microphone array eigenbeam (EB) processing techniques for localization of early reflections in room acoustic environments, which is a relevant research topic in both audio signal processing and room acoustics. This paper focuses on steered beamformer-based and subspace-based localization techniques implemented in the spherical EB domain, including the plane-wave decomposition, eigenbeam delay and sum, eigenbeam minimum variance distortionless response, eigenbeam multiple signal classification (EB-MUSIC), and eigenbeam estimation of signal parameters via rotational invariance techniques (EB-ESPRIT) methods. The directions of arrival of the original sound source and the associated reflection signals in acoustic environments are estimated from acoustic maps of the rooms, which are obtained using a spherical microphone array. The EB-domain-based frequency smoothing and white noise gain control techniques are derived and employed to improve the performance and robustness of reflection localization. The applicability of the presented methods in practice is confirmed by experiments carried out in real rooms. (C) 2012 Acoustical Society of America. [http://dx.doi.org/10.1121/1.368847

Localization of environmental reflectors from a single measured transfer function - Zamaninezhad, Ladan; Annibale, Paolo; Rabenstein, Rudolf (6th International Symposium on Communications, Control and Signal Processing, ISCCSP 2014, 2014)
This paper introduces a method for localizing reflectors in one dominant direction of a three-dimensional acoustic enclosure. The method is based on the response of the acoustic environment to an acoustic excitation. Only a single sound source and a single microphone are required. A two-step algorithm is proposed. In the first step, the distance between the reflectors is determined from the estimation of resonant frequencies. In the second step, the distance of one of the reflectors from the sound source is determined by numerical optimization. An experiment in a real three-dimensional acoustic environment shows that the position of two reflectors at opposite walls can be localized with high accuracy using a single microphone. © 2014 IEE
Keywords: acoustic reflector localization; geometry estimation; Green's function; room acoustics

Localization of Multiple Independent Sound Sources in Adverse Environments - Lombard, Anthony (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2012)
With the increasing availability of low-cost computational power, significant research efforts have been dedicated to developing signal processing strategies for microphone arrays. By jointly processing the signals captured by spatially distributed sensors, some spatial information can be gained in addition to the spectrotemporal one to achieve a spatially-selective filtering of the incoming sound or to estimate the position of one or several sound sources. The latter constitutes the main topic of this work. This thesis provides a unified and comprehensive treatment of current state-of-the-art as well as novel techniques for the localization of broadband acoustic sources. Furthermore, a major goal in this work is to link the fields of acoustic source localization and Independent Component Analysis (ICA), which have both received considerable attention over the last few decades. Several innovative strategies are developed to exploit the ICA signal model which explicitly accounts for the presence of multiple simultaneously active sources and propagation paths. Simulation results obtained from real measurements show that the ICA-based approach is applicable in noisy and reverberant conditions and yields thereby improved localization results compared to conventional methods, even in the presence of more sources than sensors. Finally, a real-time system is presented which applies the concepts developed in this work to localize several sources in space with high accuracy and using only a minimal number of microphone

Localization of multiple simultaneously active sources in acoustic sensor networks using ADP - Brendel, Andreas; Kellermann, Walter (IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017)
In this contribution, we propose a new localization approach for multiple simultaneously active sound sources using acoustic sensor networks (ASNs). It is based on the averaged directivity pattern (ADP) approach, which explicitly models the influence of reverberation on its direction of arrival (DOA) estimates and has shown to be robust against additive noise and reverberation. We develop a framework capable of handling prior information, the integration of several DOA estimators and reliability information. In particular, we derive range information from the estimated ADP-energy map and use a contribution removal technique to tackle the problem of ghost sources. Experiments with simulated room impulse responses show the efficacy of this approac
Keywords: Acoustic Sensor Networks; Localization; Averaged Directivity Pattern

Localization of multiple simultaneously active speakers in an acoustic sensor network - Brendel, Andreas; Gannot, Sharon; Kellermann, Walter (IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2018)
This paper addresses the localization of an unknown number of acoustic sources in an enclosure. We extend a well established algorithm for localization of acoustic sources, which is based on the Expectation Maximization (EM) algorithm for clustering of phase differences by a Gaussian mixture model. Supporting a more appropriate probabilistic model for spherical data such as direction of arrival or phase differences, the von Mises distribution is used to derive a localization algorithm for multiple simultaneously active sources. Experiments with simulated room impulse responses confirm the superiority of the proposed algorithm to the existing method in terms of localization performanc

Localizing Spatial Information in Neural Spatiospectral Filters - Briegleb, Annika; Haubner, Thomas; Belagiannis, Vasileios; Kellermann, Walter (2023 31st European Signal Processing Conference (EUSIPCO), 2023)
Beamforming for multichannel speech enhancement relies on the estimation of spatial characteristics of the acoustic scene. In its simplest form, the delay-and-sum beamformer (DSB) introduces a time delay to all channels to align the desired signal components for constructive superposition. Recent investigations of neural spatiospectral filtering revealed that these filters can be characterized by a beampattern similar to one of traditional beamformers, which shows that artificial neural networks can learn and explicitly represent spatial structure. Using the Complex-valued Spatial Autoencoder (COSPA) as an exemplary neural spatiospectral filter for multichannel speech enhancement, we investigate where and how such networks represent spatial information. We show via clustering that for COSPA the spatial information is represented by the features generated by a gated recurrent unit (GRU) layer that has access to all channels simultaneously and that these features are not source- but only direction of arrival-dependen
Keywords: spatial filtering; multichannel speech enhancement; DNN interpretability

Local Statistics Estimation For Rapid Frequency Selective Extrapolation - Genser, Nils; Seiler, Jürgen; Kaup, André (International Conference on Systems, Signals and Image Processing (IWSSIP), 2017)
Fast error concealment algorithms play an important role in image and video signal processing. Within this paper, a novel and highly accelerated Frequency Selective Extrapolation adaption is introduced for rapid image error concealment. The state-of-the-art complex-valued Frequency Selective Extrapolation runs with fixed parameters, e.g., a constant number of iterations, so far. In this paper, the algorithm is accelerated by determining local statistics of neighboring blocks. As a consequence, internal parameters can be estimated to match the processed image content dynamically instead of using predefined constants. Due to this, it is possible to achieve the same reconstruction quality as the state-of-the-art algorithm, while the execution time decreases on average by 41.30 % and at best by up to 57.08
Keywords: error concealment; impainting; signal extrapolation

LOCATA Challenge - Evaluation and Measures - Evers, Christine; Löllmann, Heinrich; Mellmann, Heinrich; Schmidt, Alexander; Barfuß, Hendrik; Naylor, Patrick; Kellermann, Walter (International Workshop on Acoustic Signal Enhancement (IWAENC), 2018)
Sound source localization and tracking algorithms provide estimates of the positional information about active sound sources in acoustic environments. Despite substantial advances and significant interest in the research community, a comprehensive benchmarking campaign of the various different approaches using a common database of audio recordings has, to date, not been performed. The aim of the IEEE-AASP Challenge on sound source localization and tracking (LOCATA) is to objectively benchmark state-of-the-art localization and tracking algorithms using an open-access data corpus of recordings for scenarios typically encountered in audio and acoustic signal processing applications. The challenge tasks range from the localization of a single source from static microphones arrays to tracking of multiple moving sources from moving microphone arrays. This paper provides an overview of the challenge tasks, describes the performance measures used for evaluation of the LOCATA Challenge, and presents baseline results for the development dataset.<br
Keywords: LOCATA challenge; source localization; source tracking; benchmark

Long-Term Memory Motion-Compensated Prediction - Wiegand, Thomas; Zhang, Xiaozheng; Girod, Bernd (IEEE Transactions on Circuits and Systems For Video Technology, 1999)
LMS::284

Lossless Coding of Point Cloud Geometry using a Deep Generative Model - Nguyen, Dat Thanh; Quach, Maurice; Valenzise, Giuseppe; Duhamel, Pierre (IEEE Transactions on Circuits and Systems For Video Technology, 2021)
This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy. First, to take into account the PC sparsity, our method adaptively partitions a point cloud into multiple voxel block sizes. This partitioning is signalled via an octree. Second, we employ a deep auto-regressive generative model to estimate the occupancy probability of each voxel given the previously encoded ones. We then employ the estimated probabilities to code efficiently a block using a context-based arithmetic coder. Our context has variable size and can expand beyond the current block to learn more accurate probabilities. We also consider using data augmentation techniques to increase the generalization capability of the learned probability models, in particular in the presence of noise and lower-density point clouds. Experimental evaluation, performed on a variety of point clouds from four different datasets and with diverse characteristics, demonstrates that our method reduces significantly (by up to 37%) the rate for lossless coding compared to the state-of-the-art MPEG codec.<br
Keywords: Three-dimensional displays; Octrees; Geometry; Image coding; Encoding; Context modeling; Convolutional codes

Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model - Nguyen, Dat Thanh; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2023)
In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three attribute features at different bit depths in a unified sparse representation. This allows us to efficiently exploit feature-wise and point-wise dependencies within point clouds using a sparse tensor-based neural network and thus build an accurate auto-regressive context model for an arithmetic coder. To the best of our knowledge, this is the first learning-based lossless point cloud geometry and attribute compression approach. Compared with the-state-of-the-art lossless point cloud compression method from Moving Picture Experts Group (MPEG), our method achieves 22.6% reduction in total bitrate on a diverse set of test point clouds while having 49.0% and 18.3% rate reduction on geometry and color attribute component, respectively.
CRIS-Team Scopus Importer:2023-02-10
Keywords: CNeT; Context modeling; Deep Learning; G-PCC; Generative Probability Model; Geometry; Image coding; Octrees; Point Cloud Attribute Coding; Point cloud compression; Sparse Convolution; Three-dimensional displays; Transforms

Lossless Scalable Respresentations of Hyper-Volume Data Using Compensated Wavelet Lifting - Schnurrer, Wolfgang (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
Hyper-volume data have three or more dimensions and play a very important role especially in the medical environment. Computed Tomography and Magnetic Resonance Imaging allow insights into the patient and became invaluable for medical diagnosis. The size of the hyper-volume data can quickly become very large and unhandy. To obtain scalable representations of hyper-volume data with a very high quality, various aspects of the concept of compensated wavelet lifting are analyzed and improvements are developed. The data acquisition and data characteristics are taken into account. Different compensation methods are analyzed to compensate the deforming displacements within dynamic medical hyper-volume data. In addition to the scalable representation, lossless reconstruction plays an essential role.

Lossy compression of floating point High Dynamic Range images using JPEG2000 - Springer, Dominic; Kaup, André (Visual Communications and Image Processing 2009, 2009)
In recent years, a new technique called High Dynamic Range (HDR) has gained attention in the image processing field. By representing pixel values with floating point numbers, recorded images can hold significantly more luminance information than ordinary integer images. This paper focuses on the realization of a lossy compression scheme for HDR images. The JPEG2000 standard is used as a basic component and is efficiently integrated into the compression chain. Based on a detailed analysis of the floating point format and the human visual system, a concept for lossy compression is worked out and thoroughly optimized. Our scheme outperforms all other existing lossy HDR compression schemes and shows superior performance both at low and high bitrates. © 2009 SPIE-IS&
Keywords: Floating point; HDR; HDR-J2K; High Dynamic Range; JPEG2000; LogLuv; Lossy compression

Loudspeaker Methods for Surround Sound - Blauert, Jens; Rabenstein, Rudolf (57th Open Seminar on Acoustics, OSA, 2010)

Low-Complexity Enhancement Layer Compression for Scalable Lossless Video Coding based on HEVC - Heindel, Andreas; Wige, Eugen; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2017)
Lossless compression is desired especially for professional applications like medical imaging. However, scalability may be necessary to transmit a fast preview of pictures when the channel capacity is limited. Furthermore, there may be the need for random access to single frames of a losslessly reconstructed video. We present and evaluate a lossy-to-lossless scalable video coding system, where the scalability is achieved using a lossy base layer in conjunction with lossless compression of the reconstruction error in the enhancement layer. High Efficiency Video Coding (HEVC) is employed for encoding the base layer. For compression of the enhancement layer we propose a low-complexity scheme called "Sample-based weighted prediction for Enhancement Layer Coding" (SELC). Furthermore, enhancement layer compression using JPEG-LS and the scalable extension of HEVC (SHVC) are evaluated. The performance of scalable coding using each of these three methods is compared to lossless single-layer coding using the Intra Main RExt configuration. Experimental results show that scalable coding with SELC achieves average bitrate savings of 7.3% compared to lossless single-layer coding, while average bitrate reductions of only 6.3% and 3.9% are obtained using JPEG-LS and SHVC, respectively. Concerning runtime, the additional encoder runtimes of SELC and JPEG-LS compared to base layer coding are negligible. In contrast to that, the runtime of the SHVC enhancement layer encoder is much higher and similar to the runtime of the base layer encode
Keywords: Base layer; enhancement layer; High Efficiency Video Coding; scalable coding; SELC; SWP

Low-Complexity Heterogeneous Video Transactions using Data Mining - Fernandez-Escribano, Gerardo; Bialkowski, Jens; Gamez, Jose A.; Kalva, Hari; Cuenca, Pedro; Orozco-Barbosa, Luis; Kaup, André (IEEE Transactions on Multimedia, 2008)
Recent developments have given birth to H.264/AVC: a video coding standard offering better bandwidth to video quality ratios than previous standards (such as H.263, MPEG-2, MPEG-4, etc.), due to its improved inter- and intraprediction modes at the expense of higher computation complexity. It is expected that H.264/AVC will take over the digital video market, replacing the use of previous standards in most digital video applications. This creates an important need for heterogeneous video transcoding technologies from older standards to H.264. In this paper, we focus our attention on the interframe prediction, the most computationally intensive task involved in the heterogeneous video transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for interframe prediction based on data mining techniques to be used as part of a very low complexity heterogeneous video transcoder. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in the decoded video. We use data mining tools to exploit the correlation and derive decision trees to classify the incoming decoded MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. For general validation purposes, we apply our algorithm to two of the most important heterogeneous video transcoders: MPEG-2 to H.264 and H.263 to H.264. Our results show that the our data-mining based transcoding algorithm is able to maintain a good video quality while considerably reducing the computational complexity by 72% on average when applied in MPEG-2 to H.264 transcoders, and by 62% on average when applied in H.263 to H.264 transcoders. Finally, we conduct a comparative study with some of the most prominent fast interprediction methods for H.264 presented in the literature. Our results show that the proposed data mining-based approach achieves the best results for video transcoding applications. © 2008 IEE
Keywords: Data mining; H.263; H.264; Interprediction; MPEG-2; Supervised classification; Video transcoding

Low-complexity inter-sequence error concealment based on scale-invariant feature transform - Tröger, Tobias; Heiber, Henning; Schmitt, Andreas; Kaup, André (2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09, 2009)
Inter-sequence error concealment defines a novel class of concealment techniques which utilize two or more representations of a particular video sequence for image reconstruction. An image-based approach introduced recently provides superior reconstruction quality of concealed image parts and outperforms well-known intra methods by up to 15 dB PSNR. However, computational complexity is high due to a numerical solution of the image registration problem. In this paper, we propose a feature-based technique for image registration which is included in the concept of inter-sequence error concealment. Based on the determination of scale-invariant features, the processing time is reduced on average by a factor of up to 28. At the same time, the loss in terms of objective image quality compared to the image-based approach is less than 1 dB PSNR. © 2009 IEE

Low complexity streak noise reduction for mobile TV using line selective interpolation of field information - Friebe, Markus; Kaup, André (IEEE International Conference on Image Processing 2005, ICIP 2005, 2005)
This contribution presents a method for quality enhancement of mobile received analog TV signals using line selective interpolation of field information (LSI-FI). Using combining techniques of neighboring lines one can restaurate mobile received images successfully. Spatial statistical methods are used to detect distorted lines of an image. Distorted lines are interpolated by correctly received neighboring lines. The low complexity of the algorithm makes it possible to enable real-time implementation in a DSP or FPGA. © 2005 IEE

Low-complexity transcoding of inter coded video frames from H.264 to H.263 - Bialkowski, Jens; Barkowsky, Marcus; Leschka, F.; Kaup, André (2006 IEEE International Conference on Image Processing, ICIP 2006, 2006)
The presented work addresses the reduction of computational complexity for transcoding of interframes from H.264 to H.263 baseline profiles maintaining the quality of a full search approach. This scenario aims to achieve fast backward compatible interoperability inbetween new and existing video coding platforms, e.g. between DVB-H and UMTS. By exploiting side information of the H.264 input bitstream the encoding complexity of the motion estimation is strongly reduced. Due to the possibility to divide a macroblock (MB) into partitions with different motion vectors (MV), one single MV has to be selected for H.263. It will be shown, that this vector is suboptimal for all sequences, even if all existing MVs of a MB of H.264 are compared as candidate. Also motion vector refinement with a fixed 1/2-pel refinement window as used by transcoders throughout the literature is not sufficient for scenes with fast movement. We propose an algorithm for selecting a suitable vector candidate from the input bitstream and this MV is then refined using an adaptive window. Using this technique, the complexity is still low at nearly optimum rate-distortion results compared to an exhaustive full-search approach. ©2006 IEE
Keywords: H.263; H.264/AVC; Video transcoding

Low complexity two classes Gauss weighting filter for noise reduction in mobile received analog TV signals - Friebe, Markus; Kaup, André (13th European Signal Processing Conference, EUSIPCO 2005, 2005)
We introduce a low complexity method for noise reduction in mobile received analog TV signals. The two classes Gauss weighting filter is smoothing images while preserving image details and motion regions. This approach classifies neighboring image samples either as an edge or non-edge sample. Only image samples which belong to non-edge samples are used for noise reduction. Image samples which belong to edges are excluded from filtering.

Luminance Simulation in CARLA under Cloud Coverage - Model Validation and Implications - Ulreich, Fabian; Moser, Elisabeth; Olbrich, Florian; Ebert, Martin; Bierl, Rudolf; Kaup, André (2023 IEEE International Workshop on Metrology for Automotive, MetroAutomotive 2023 - Proceedings, 2023)
To decrease the number of kilometers driven during the development of autonomous cars or driving assistance systems, performant simulation tools are necessary. Currently, domain distance effects between simulation and reality are limiting the successful application of rendering engines in data-driven perception tasks. In order to mitigate those domain distance effects, simulation tools have to be as close to reality as possible for the given task. For optical sensors like cameras, the luminance of the scene is essential. We provide within this paper a method to measure the luminance of rendered scenes within CARLA, an often used open-source simulation environment. Thereby, it is possible to validate the environment and weather models by taking real-world measurements with photometric sensors or with the help of open-source weather data, published e.g. by the German federal service for weather data (DWD - "Deutscher Wetterdienst"). Employing our proposed luminance measurement, the domain gap resulting from the simulation can be specified, which makes it possible to evaluate the statements about the safety of the automated driving system determined within the simulation. We show that the ratio between global and diffuse radiation modeled by the default atmosphere models within CARLA are under limited conditions similar to real-world measurements taken by the DWD. Nevertheless, we show, that the ratio's temporal variability in real-world situations is not modeled by CARLA.
CRIS-Team Scopus Importer:2023-09-29
Keywords: carla; cloud coverage; luminance; meteorology; simulation; weather

Making machines understand us in reverberant rooms: Robustness against reverberation for automatic speech recognition - Yoshioka, Takuya; Sehr, Armin; Delcroix, Marc; Kinoshita, Keisuke; Maas, Roland; Nakatani, Tomohiro; Kellermann, Walter (IEEE Signal Processing Magazine, 2012)
Speech recognition technology has left the research laboratory and is increasingly coming into practical use, enabling a wide spectrum of innovative and exciting voice-driven applications that are radically changing our way of accessing digital services and information. Most of today's applications still require a microphone located near the talker. However, almost all of these applications would benefit from distant-talking speech capturing, where talkers are able to speak at some distance from the microphones without the encumbrance of handheld or body-worn equipment [1]. For example, applications such as meeting speech recognition, automatic annotation of consumer-generated videos, speech-to-speech translation in teleconferencing, and hands-free interfaces for controlling consumer-products, like interactive TV, will greatly benefit from distant-talking operation. Furthermore, for a number of unexplored but important applications, distant microphones are a prerequisite. This means that distant talking speech recognition technology is essential for extending the availability of speech recognizers as well as enhancing the convenience of existing speech recognition applications. © 2012 IEEE.

Making of the Soundtrack for a Silent Movie with Wave Field Synthesis - Case Study: Metropolis by Fritz Lang - Ammann, M.; Brendel, J.; Kuntz, Achim; Rabenstein, Rudolf (5. Thüringer Medienseminar, 2009)

Manifold learning-supported estimation of relative transfer functions for spatial filtering - Brendel, Andreas; Zeitler, Johannes; Kellermann, Walter (Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022)
Many spatial filtering algorithms used for voice capture in, e.g., teleconferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs). Accordingly, many RTF estimators have been proposed which, however, suffer from performance degradation under acoustically adverse conditions or need prior knowledge on the properties of the interfering sources. While state-of-the-art RTF estimators ignore prior knowledge about the acoustic enclosure, audio signal processing algorithms for teleconferencing equipment are often operating in the same or at least a similar acoustic enclosure, e.g., a car or an office, such that training data can be collected. In this contribution, we use such data to train Variational Autoencoders (VAEs) in an unsupervised manner and apply the trained VAEs to enhance imprecise RTF estimates. Furthermore, a hybrid between classic RTF estimation and the trained VAE is investigated. Comprehensive experiments with real-world data confirm the efficacy for the proposed method.

MAP-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and l(1)-norm minimization - Winter, Stefan; Kellermann, Walter; Sawada, H.; Makino, Shoji (EURASIP Journal on Advances in Signal Processing, 2007)
We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures, we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori ( MAP) approach. In the first step, we estimate the mixing matrix based on hierarchical clustering, assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the l(1)-norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming (SOCP) approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers, its results are comparable to, or even better than, the SOCP solution. The advantage is a lower computational cost for problems with low input/output dimensions. Copyright (C) 2007 Stefan Winter et a

MAP-Estimation of Dense Disparity-Fields for Stereoscopic Images - Aach, T.; Kaup, André (2nd Singapore Intern. Conf. on Image Processing, Singapore, 1992)
LMS::1011

Mapping sounds onto images using binaural spectrograms - Deleforge, Antoine; Drouard, Vincent; Girin, Laurent; Horaud, Radu (22nd European Signal Processing Conference (EUSIPCO), 2014)
We propose a novel method for mapping sound spectrograms onto images and thus enabling alignment between auditory and visual features for subsequent multimodal processing. We suggest a supervised learning approach to this audio-visual fusion problem, on the following grounds. Firstly, we use a Gaussian mixture of locally-linear regressions to learn a mapping from image locations to binaural spectrograms. Secondly, we derive a closed-form expression for the conditional posterior probability of an image location, given both an observed spectrogram, emitted from an unknown source direction, and the mapping parameters that were previously learnt. Prominently, the proposed method is able to deal with completely different spectrograms for training and for alignment. While fixed-length wide-spectrum sounds are used for learning, thus fully and robustly estimating the regression, variable-length sparse-spectrum sounds, e.g., speech, are used for alignment. The proposed method successfully extracts the image location of speech utterances in realistic reverberant-room scenario
LMS::1518

Maschinelles Lernen für Schutzgeräte in DC-Netzen - Strobl, Christian; Schäfer, Maximilian; Bauer, Johannes; Rabenstein, Rudolf (VDE-Fb. 73: Kontaktverhalten und Schalten, 2017)
Klein- und Niederspannungsgleichstromnetze erfordern Schutzkonzepte, die gezielt für die jeweilige Applikation und Systemspannung ausgelegt sind. Mit Hilfe von Methoden der Systemidentifikation und des maschinellen Lernens kann dabei eine Identifikation und Zuordnung von Fehlerereignissen verfeinert werden. Nichtrekursive und rekursive Großsignalverfahren für den Zeitbereich sowie Konzepte für Kleinsignalanalysen im Frequenzbereich werden vorgestell

Massively parallel lossless compression of medical images using least-squares prediction and arithmetic coding - Weinlich, Andreas; Rehm, Johannes; Amon, P.; Hutter, A.; Kaup, André (2013 20th IEEE International Conference on Image Processing, ICIP 2013, 2013)
Medical imaging in hospitals requires fast and efficient image compression to support the clinical work flow and to save costs. Least-squares autoregressive pixel prediction methods combined with arithmetic coding constitutes the state of the art in lossless image compression. However, a high computational complexity of both prevents the application of respective CPU implementations in practice. We present a massively parallel compression system for medical volume images which runs on graphics cards. Image blocks are processed independently by separate processing threads. After pixel prediction with specialized border treatment, prediction errors are entropy coded with an adaptive binary arithmetic coder. Both steps are designed to match particular demands of the parallel hardware architecture. Comparisons with current image and video coders show efficiency gains of 3.3-13.6% while compression times can be reduced to a few seconds. © 2013 IEE
Keywords: 2-D least-squares autoregression; adaptive binary arithmetic coding; computed tomography; Nvidia CUDA GPGPU parallelization; parallel predictive coding

Matched Quality Evaluation of Temporally Downsampled Videos with Non-Integer Factors - Herglotz, Christian; Ramasubbu, Geetha; Kaup, André (International Conference on Quality of Multimedia Experience (QoMEX), 2020)
Recent research has shown that temporal downsampling of high-frame-rate sequences can be exploited to improve the rate-distortion performance in video coding. However, until now, research only targeted downsampling factors of powers of two, which greatly restricts the potential applicability of temporal downsampling. A major reason is that traditional, objective quality metrics such as peak signal-to-noise ratio or more recent approaches, which try to mimic subjective quality, can only be evaluated between two sequences whose frame rate ratio is an integer value. To relieve this problem, we propose a quality evaluation method that allows calculating the distortion between two sequences whose frame rate ratio is fractional. The proposed method can be applied to any full-reference quality metric.<br
Keywords: video; coding; frame rate; downsampling; quality evaluation

Matching the acoustic model to front-end signal processing for ASR in noisy and reverberant environments - Maas, Roland; Schwarz, Andreas; Reindl, Klaus; Zheng, Yuanhang; Meier, Stefan; Sehr, Armin; Kellermann, Walter (Deutsche Jahrestagung für Akustik (DAGA), 2012)
Distant-talking automatic speech recognition (ASR) represents an extremely challenging task. The major reason is that unwanted additive interference and reverberation are picked up by the microphones besides the desired signal. A hands-free human-machine interface should therefore comprise a powerful acoustic preprocessing unit in line with a robust ASR back-end. However, since perfect speech enhancement cannot be achieved in practice, the output of the front-end will always contain some residual interference and some distortion of the desired signal. It is hence of decisive importance to carefully adjust the hidden Markov models (HMMs) of the ASR system to the front-end. In this contribution, we present a two-channel acoustic front-end based on blind source separation along with Wiener filtering. For the front-end integration into the ASR system, different types of multi-style as well as adaptive training and HMM adaptation are investigated.

Maximum likelihood estimation of a reverberation model for robust distant-talking speech recognition - Sehr, Armin; Zheng, Yuanhang; Nöth, Elmar; Kellermann, Walter (Proc. European Signal Processing Conference, 2007)
We propose a novel approach for estimating a reverberation model for a robust recognizer according to [1], which is designed to allow distant-talking automatic speech recognition (ASR) in reverberant environments. Based on a few calibration utterances with known transcriptions recorded in the target environment, a maximum likelihood estimator is used to find the means and variances of the reverberation model. In contrast to [1] and to HMM training on artificially reverberated training data (e. g. [2]), measurements of room impulse responses become unnecessary, and the effort for training is greatly reduced. Simulations of a connected digit recognition task show that, in highly reverberant environments, the reverberation models estimated by the proposed approach achieve significantly higher recognition rates than HMMs trained on reverberant data. © 2007 EURASI

M-channel multiple description coding based on uniformly offset quantizers with optimal deadzone - Meng, L.; Liang, Jie; Zhao, Y.; Bai, H.; Lin, C.; Kaup, André (2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, 2013)
This paper proposes an improved source-splitting-based two-rate M-channel multiple description coding scheme, where the source is split into M subsets. In each description, one subset is coded at a high rate, and others are predictively coded at a low rate. Uniform offsets among low-rate quantizers of different descriptions are achieved by employing unequal deadzones and by quantizing the predictions. When several descriptions are received, the optimal reconstruction of each subset is achieved by finding the intersection of all received quantization bins. The closed-form expression of the expected distortion is obtained. The proposed scheme is applied to lapped transform-based multiple description image coding and achieves improved performance. The optimal deadzone selection and its impact are also given in this paper. © 2013 IEE
Keywords: deadzone quantization; Multiple description coding; predictive coding; random quantization

Mean and Differential Phase Centers of Rectangular Dielectric RodAntennas - Richter, J; Garbas, Jens-Uwe; Schmidt, Lorenz-Peter (2004)
lhft_intern.bib::richter04a

Mean and Differential Phase Centers of Rectangular Dielectric Rod Antennas - Richter, Jürgen; Garbas, Jens-Uwe; Schmidt, Lorenz-Peter (Proceedings of the European Microwave Conference, 2004)
Dielectric rod antennas of rectangular cross section are of increasing interest at millimeterwave frequencies. For many applications like e.g. densely packaged feed arrays for reflector/lens systems or electromagnetic field probes the exact knowledge of the phase center location is required. This paper will describe two efficient methods to determine the mean and differential phase center locations of such antennas by means of numerical electromagnetic field simulations and a subsequent analysis of the computed antenna phase pattern. Phase center locations of various dielectric rod antennas with rectangular cross section are presented and the influence of geometrical parameters and frequency on the phase center location are analyzed and discussed in detail. The theoretically derived results are also compared to measurements performed at X-band frequencies.
UnivIS-Import:2015-03-09:Pub.2004.tech.IE.LEH.meanan

Mean shift object tracking using a 4d kernel and linear prediction - Quast, Katharina; Kobylko, C.; Kaup, André (International Conference on Computer Vision Theory and Application, VISAPP 2011, 2011)
A new mean shift tracker which tracks not only the position but also the size and orientation of an object is presented. By using a four-dimensional kernel, the mean shift iterations are performed in a four-dimensional search space consisting of the image coordinates, a scale and an orientation dimension. Thus, the enhanced mean shift tracker tracks the position, size and orientation of an object simultaneously. To increase the tracking performance by using the information about the position, size and orientation of the object in the previous frames, a linear prediction is also integrated into the 4D kernel tracker. The tracking performance is further improved by considering the gradient norm as an additional object featur
Keywords: Mean shift tracking; Object tracking

Measuring Airborne Heat Flows in Passive Solar Buildings with Tracer Gas Methods - Rabenstein, Rudolf; Heidt, Frank Dietrich (Proceedings 1991 ISES Solar World Congress, 1991)

Measuring performance limits of subband adaptive systems - Weiß, S.; Stenger, Alexander; Stewart, R. W.; Rabenstein, Rudolf (Proc. 32nd Asilomar Conference on Signals, Systems and Computers, 1998)
We discuss a method to measure the convergence limits of general subband adaptive systems due to non-ideal filter banks. Aliasing caused in such filter banks presents a distortion to the subband adaptive system which forms a lower limit for the minimum mean squared error The accuracy of the achievable model is given by the transfer function of the filter bank. To measure both aliasing and filter bank distortions, we employ the measurement technique by Heinle and Schussler (1996). The presented approach is applicable to a wide range of subband adaptive filter systems. Examples for the measured limits are presented.

Measuring the Linear and Nonlinear Properties of Electro-Acoustic Transmission Systems - Heinle, Frank; Rabenstein, Rudolf; Stenger, Alex (Proc. Int. Workshop on Acoustic Echo and Noise Control (IWAENC'97), 1997)
LMS::405

Methods and tools for wavelet-based scalable multiview video coding - Garbas, Jens-Uwe; Pesquet-Popescu, B.; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2011)
A wavelet-based multiview video coding scheme is presented in this paper. It uses a 4-D wavelet transform, which is composed of a 1-D temporal wavelet transform, namely motion compensated temporal filtering, a 1-D view-directional wavelet transform, namely disparity compensated view filtering and a 2-D spatial wavelet transform. Since the presented framework can make use of the inherent scalability properties of the wavelet transforms involved, it allows full scalability of the coded bitstream in the temporal, view, spatial, and quality dimensions. Coding performance close to the H.264/advanced video coding based standard multiview video codec is shown. Enhancements of the view transform, in order to better account for brightness and color variations across views are introduced. Additionally, the use of a signal adaptive anisotropic wavelet packet (WP) transform as a generalization of WP transforms for the spatial decomposition is proposed. Both enhancements lead to a decrease of bit rate of up to 11% compared with the baseline version of the codec. © 2011 IEE
Keywords: Multiview; scalability; video coding; wavelets

Methods for Enhancement Layer Compression in Two-Layer Quality Scalable Video Coding - Heindel, Andreas (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2018)
Video communication applications are prevalent in our digital society. Smart phones and other mobile devices like tablet computers are equipped with both high-resolution displays and cameras, which allow digital video communication almost whenever and wherever desired. Leading-edge technologies from multiple fields like digital communications and digital signal processing jointly enable high quality video applications as we know it today. However, capabilities like processing power, energy supply, display size, or the supported video formats of the devices in the field are very heterogeneous. Moreover, also the network conditions may vary significantly. This challenge is tackled by scalable video coding, which creates bit stream layers of the same content but with different properties, denoted as base layer and enhancement layer. Depending on the network and receiver device capabilities, only the base layer or both layers may be transmitted and decoded. However, scalable video coding with the possibility for lossless reconstruction of individual pictures is not optimally supported by state-of-the-art approaches. Additionally, the specific scalability of the color resolution is not possible by the state of the art. Consequently, two scalable coding schemes that support these requirements are proposed in this thesis. The first scalable coding framework encodes the residual signal of the base layer in a lossless manner. Pixel-wise intra picture prediction is applied, that is efficient for lossless coding and retains the individual access to single pictures. The experimental results showed that there is no bit rate overhead for the scalable representation compared to state-of-the-art direct lossless coding with only one layer, when intra picture coding is considered. Exploiting temporal correlations by means of inter picture coding of the base layer allows to unite the advantages of both approaches, base layer video coding optimized for lossy compression and lossless enhancement layer coding. An extension of this approach supporting near-lossless coding is also presented in this thesis and outperforms the state of the art in most cases. The second enhancement layer codec proposed in this thesis allows to reconstruct the high-resolution color format when the base layer is compressed using a reduced color component resolution. Experiments showed that scalable coding can increase the average quality in terms of the peak signal-to-noise ratio by 1.82 dB compared to base layer coding only. Additionally, a novel metric to measure the artifact level originating from the reduced color component resolution is introduced in this contex
Keywords: Scalable Coding; Screen Content Coding; Video Coding

Methods for Improved Video Compression Using Image Noise Modeling - Wige, Eugen (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2015)

Microphone Array Signal Processing for Robot Audition - Löllmann, Heinrich; Moore, Alastair H.; Naylor, Patrick A.; Rafaely, Boaz; Horaud, Radu; Mazel, Alexandre; Kellermann, Walter (Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2017)
Robot audition for humanoid robots interacting naturally with humans in an unconstrained real-world environment is a hitherto unsolved challenge. The recorded microphone signals are usually distorted by background and interfering noise sources (speakers) as well as room reverberation. In addition, the movements of a robot and its actuators cause ego-noise which degrades the recorded signals significantly. The movement of the robot body and its head also complicates the detection and tracking of the desired, possibly moving, sound sources of interest. This paper presents an overview of the concepts in microphone array processing for robot audition and some recent achievements.
LMS::1715
Keywords: Humanoid robots; robot audition; microphone array processing; ego-noise suppression; source tracking

Microphone Utility-based Weighting for Robust Acoustic Source Localization in Wireless Acoustic Sensor Networks - Günther, Michael; Brendel, Andreas; Kellermann, Walter (14th ITG Conference on Speech Communication, 2021)

Microphone utility estimation in acoustic sensor networks using single-channel signal features - Günther, Michael; Brendel, Andreas; Kellermann, Walter (EURASIP Journal on Audio, Speech, and Music Processing, 2023)
In multichannel signal processing with distributed sensors, choosing the optimal subset of observed sensor signals to be exploited is crucial in order to maximize algorithmic performance and reduce computational load, ideally both at the same time. In the acoustic domain, signal cross-correlation is a natural choice to quantify the usefulness of microphone signals, i.e., microphone utility, for coherent array processing, but its estimation requires that the uncoded signals are synchronized and transmitted between nodes. In resource-constrained environments like acoustic sensor networks, low data transmission rates often make transmission of all observed signals to the centralized location infeasible, thus discouraging direct estimation of signal cross-correlation. Instead, we employ characteristic features of the recorded signals to estimate the usefulness of individual microphone signals using the Magnitude-Squared Coherence (MSC) between the source and respective microphone signal as ground-truth metric. In this contribution, we provide a comprehensive analysis of model-based microphone utility estimation approaches that use signal features and, as an alternative, also propose machine learning-based estimation methods that identify optimal sensor signal utility features. The performance of both approaches is validated experimentally using both simulated and recorded acoustic data, comprising a variety of realistic and practically relevant acoustic scenarios including moving and static sources.
CRIS-Team Scopus Importer:2023-08-18
Keywords: Acoustic sensor network; Channel selection; Graph partitioning; Microphone utility

Mid-Level Chord Transition Features for Musical Style Analysis - Müller, Meinard; Weiß, Christof; Brand, Fabian (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019)

Milestone on phase-space optics - Testorf, M.; Ojeda-Castaneda, J.; Lohmann, Adolf (2006)
LMS::647

Minimization of transient signals in recursive time-varying digital filters - Rabenstein, Rudolf (Circuits Systems and Signal Processing, 1988)
Changing the coefficient set of a recursive digital filter during operation results in a transient signal at the filter output. The amplitude of this transient signal may exceed the amplitude of the output signal before and well after the change of coefficients. This effect is rather disturbing in some applications, such as the processing of audio signals. It is shown how these transients can be minimized without increasing the computational complexity of the filter by the use of a properly chosen set of intermediate coefficients. © 1988 Birkhäuser Boston Inc.

Minimizing a weighted error criterion for spatial error concealment of missing image data - Meisinger, Katrin; Kaup, André (2004 International Conference on Image Processing, ICIP 2004, 2004)
In this contribution we present an algorithm for spatial error concealment of lost image data caused by transmission of images in error prone environments. The surrounding correctly received image signal is approximated by a weighted linear combination of basis functions and the missing image data is obtained by a frequency selective extrapolation. During the approximation a novel weighted error criterion is minimized. We use an Isotropic correlation model for the weighting function taking the correlation among pixels into account and emphasizing pixels wich are closer to the missing area. 2D DFT basis functions are especially suited for the signal extrapolation in order to be able to reconstruct monotone areas, edges and noisy regions and allow an efficient realization of the algorithm in the frequency domain. Due to the weighting function we could improve the concealing performance of the algorithm considerably while halving the required FFT size. © 2004 IEEE.

Minimum mutual information-based linearly constrained broadband signal extraction - Reindl, Klaus; Meier, Stefan; Barfuß, Hendrik; Kellermann, Walter (IEEE Transactions on Audio Speech and Language Processing, 2014)
In this contribution, the problem of broadband acoustic signal extraction is treated as a specific source separation problem, where the desired signal components are to be separated from all remaining undesired components. For this, we exploit the generic TRIple-N Independent component analysis for CON-volutive mixtures (TRINICON) framework. The TRINICON optimization criterion is complemented with linear constraints leading to the Linearly Constrained Minimum Mutual Information (LCMMI) criterion for desired signal extraction. A general linearly constrained update rule for iterative filter optimization is derived, which can efficiently be realized in a novel Minimum Mutual Information (MMI)-Generalized Sidelobe Canceler (GSC). The general treatment of the signal extraction problem using an MMI criterion provides several advantages: Firstly, new insights into the signal extraction problem can be derived by establishing links to both the original GSC and the Multichannel Wiener Filter (MWF). Secondly, by exploiting fundamental properties characteristic for speech and audio signals, complicated and often unreliable Voice Activity Detection (VAD)-based control mechanisms become unnecessary. Thirdly, the overall realization requires only prior information of the desired source position. An evaluation of the MMI-GSC for the double-talk situation with two concurrently active speech sources under reverberant and noisy conditions demonstrates the effectiveness of this novel approac
UnivIS-Import:2016-02-10:Pub.2014.tech.IE.LEN.minimu
Keywords: Generalized sidelobe canceler (GSC); Interference cancellation; Linearly constrained minimum mutual information (LCMMI); Linearly constrained minimum variance (LCMV); Wiener filter

Misalignment recognition in acoustic sensor networks using a semi-supervised source estimation method and Markov random fields - Figueiredo Miller, Gabriel; Brendel, Andreas; Kellermann, Walter; Gannot, Sharon (IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2021)

Mixed-resolution view synthesis using non-local means refined image merging - Richter, Thomas; Schöberl, Michael; Seiler, Jürgen; Tröger, Tobias; Kaup, André (SPIE Electronic Imaging 2012 - Stereoscopic Displays and Applications XXIII, 2012)
Synthesizing novel views from originally available camera perspectives via depth maps is a key issue in the 3D video domain. Up to now, several high-resolution cameras are needed to obtain high-quality intermediate synthesized views. One possibility to reduce costs with regard to the used camera array is to replace some cameras by low-resolution cameras, which are cheaper on the one hand, but provide a much poorer image quality on the other hand. Unfortunately, some of the information inside the desired intermediate view may only be available in the low-resolution reference. Thus, the image quality of the low-resolution reference has a big influence on the visual quality of the synthesized view. This paper proposes a postprocessing step for the synthesized view, based on the non-local means algorithm. Thereby, all areas inserted from the low-resolution reference get efficiently adapted to their high-resolution environment. It is shown, that the non-local means refined image merging leads to a PSNR gain of up to 0.90 dB compared to an unrefined mixed-resolution setup. The approach can be easily extended to a hole-filling algorithm and yields a PSNR gain of up to 0.81 dB for hole areas compared to a reference hole-filling algorithm. The subjective image quality also increases convincingly in both applications. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE
Keywords: depth-image based rendering (DIBR); image merging; mixed-resolution; non-local means (NLM); view synthesis

Mobile Multimediakommunikation über DECT, ISDN und LAN - Hundt, E.; Huth, H.-P.; Kaup, André; Pandel, J. (Multimedia: Anwendungen, Technologie, Systeme, ITG-Fachbericht 144, 1997)
LMS::482

Mobile Multimediakommunikation über DECT, ISDN und LAN - Hundt, E.; Huth, H.-P.; Kaup, André; Pandel, J. (Der Fernmelde-Ingenieur, 1998)
LMS::475

Mobility Models for Cooperative Communications - Oriold, Daniel (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2015)

Modal array signal processing using circular microphone arrays applied to acoustic source detection and localization problems - Teutsch, Heinz; Kellermann, Walter (4th Joint Meeting of Acoustical Society of America and Acoustical Society of Japan, 2006)
Many applications of acoustic signal processing rely on estimates of several parameters present in the observed acoustic scene such as the number and location of acoustic sources.These parameters have been traditionally estimated by means of classical array signal processing (CASP) algorithms using microphone arrays. Algorithms for parameter estimation solely based on the paradigm of CASP often suffer from the narrowband assumption underlying the signal model. This restriction limits their usability when wideband signals, such as speech, are present in the wave field under observation. We investigate the parameter estimation problem by applying the notion of wave field decomposition using baffled circular microphone arrays. The obtained wave field representation is used as the basis for ‘‘modal array signal processing algorithms.’’ It is shown that by applying the notion of modal array signal processing, novel algorithms can be derived that have the potential to unambiguously detect and localize multiple simultaneously active wideband sources in the array’s full field‐of‐view. Performance evaluations by means of simulations, measurements, and real‐time case studies are presented. ©A
LMS::678

Modal Excitation in Feedback Delay Networks - Schlecht, Sebastian; Scerbo, Matteo; De Sena, Enzo; Valimaki, Vesa (IEEE Signal Processing Letters, 2024)
Feedback delay networks (FDNs) are used in audio processing and synthesis. The modal shapes of the system describe the modal excitation by input and output signals. Previously, the Ehrlich-Aberth method was used to find modes in large FDNs. Here, the method is extended to the corresponding eigenvectors indicating the modal shape. In particular, the computational complexity of the proposed analysis method does not depend on the delay-line lengths and is thus suitable for large FDNs, such as artificial reverberators. We show the relation between the compact generalized eigenvectors in the delay state space and the spatially extended modal shapes in the state space. We illustrate this method with an example FDN in which the suggested modal excitation control does not increase the computational cost. The modal shapes can help optimize input and output gains. This letter teaches how selecting the input and output points along the delay lines of an FDN adjusts the spectral shape of the system output.
Created from Fastlane, Scopus look-up
Keywords: Audio systems; modal analysis; polynomials; reverberation

Mode adaptive reference frame denoising for high fidelity compression in HEVC - Wige, Eugen; Yammine, Gilbert; Schnurrer, Wolfgang; Kaup, André (2012 IEEE Visual Communications and Image Processing, VCIP 2012, 2012)
A new video coding standard, High Efficiency Video Coding (HEVC), is currently under development. In this paper we propose two relatively low-complex adaptive Wiener schemes for efficient P-frame coding of noisy videos with HEVC. In the proposed inloop denoising framework the reference frame is noise filtered for P-frame prediction. The introduced algorithms adapt to the HEVC coding structure and thus can efficiently model the noise within the reference frame. The simulation results show that on the one hand considerable compression gains can be achieved using the proposed in-loop denoising framework. On the other hand the developed algorithms decrease the encoder runtime, but at the same time increase the decoder runtime. © 2012 IEE
Keywords: HEVC; In-loop denoising; inter-frame prediction

Model-Aided Coding: A New Approach to Incorporate Facial Animation into Motion-Compensated Video Coding - Eisert, Peter; Wiegand, Thomas; Girod, Bernd (IEEE Transactions on Circuits and Systems For Video Technology, 1999)
LMS::314

Model-Aided Coding: Using 3-D Scene Models in Motion-Compensated Video Coding - Eisert, Peter; Wiegand, Thomas; Girod, Bernd (ICIP 2000, 2000)
LMS::160

Model-Based 3D-Motion Estimation with Illumination Compensation - Eisert, Peter; Girod, Bernd (Proceedings 6th International Conference on Image Processing and its Applications (IPA 97), 1997)
LMS::395

Model based analysis for quantization parameter cascading in hierarchical video coding - Li, Xiang; Amon, Peter; Hutter, Andreas; Kaup, André (2009 IEEE International Conference on Image Processing, ICIP 2009, 2009)
Originally, hierarchical prediction structures were proposed to achieve temporal scalability. Soon after, it was realized that with a proper quantization parameter cascading (QPC) scheme the general performance can be significantly improved by hierarchical coding. However, the theory behind the gain has not been explored so far. In this paper, the QPC in hierarchical coding is investigated by model based emulations. From the analysis, it is noticed that a parameter β which represents the error propagation in a group of pictures greatly affects the performance of QPC in hierarchical coding. Based on β, a simple adaptive QPC algorithm is designed. Simulations verify the efficiency of this algorithm: a gain up to 0.89 dB is obtained over the most recent SVC reference software. ©2009 IEE
Keywords: Optimization methods; Video coding

Model-based Coding of Facial Image Sequences at Varying Illumination Conditions - Eisert, Peter; Girod, Bernd (10th IEEE Workshop on Image and Multidimensional Signal Processing 1998, 1998)

Model-based dereverberation in the logmelspec domain for robust distant-talking speech recognition - Sehr, Armin; Maas, Roland; Kellermann, Walter (2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, 2010)
The REMOS (REverberation MOdeling for Speech recognition) concept for reverberation-robust distant-talking speech recognition, introduced in [1] for melspectral features, is extended in this contribution to logarithmic melspectral (logmelspec) features. Based on a combined acoustic model consisting of a hidden Markov model network and a reverberation model, REMOS determines clean-speech and reverberation estimates during recognition by an inner optimization operation. A reformulation of this inner optimization problem for logmelspec features, allowing an efficient solution by nonlinear optimization algorithms, is derived in this paper so that an efficient implementation of REMOS for logmelspec features becomes possible. Connected digit recognition experiments show that the proposed REMOS implementation significantly outperforms reverberantly-trained HMMs in highly reverberant environments. ©2010 IEE
Keywords: Acoustic modeling; Distant-talking ASR; Model-based dereverberation; Reverberation; Robust ASR

Model-based dereverberation of speech in the mel-spectral domain - Sehr, Armin; Kellermann, Walter (2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008, 2008)
A model-based dereverberation approach for robust distant-talking speech recognition employing the powerful acoustic model of the recognizer to describe the clean speech feature sequence is discussed. The clean speech model is combined with a statistical reverberation model describing the acoustic path between speaker and microphone directly in the mel-spectral domain. Dereverberation is performed during recognition bydetermining the most likely contributions of the combined model's components to the current reverberant feature vector. The advantages of processing feature-domain representations of speech rather than using time- or frequency-domain speech representations are the dimension reduction and the possibility to obtain robust reverberation models valid for arbitrary speaker and microphone positions in the recording room. In this contribution, we emphasize that the criterion used for the dereverberation operation is equivalent to maximum a posteriori estimation. Connected-digit recognition experiments confirm the superior performance of the novel concept. © 2008 IEE

Model-Based Estimation of Facial Expression Parameters from Image Sequences - Eisert, Peter; Girod, Bernd (International Conference on Image Processing (ICIP 97), 1997)
LMS::414

Model based segmentation of motion fields in compressed video sequences using partition projection and relaxation - Treetasanatavorn, Siripong; Rauschenbach, U.; Heuer, A.; Kaup, André (Visual Communications and Image Processing 2005, 2005)
In the context of visual signal analysis for media adaptation, this paper presents a stochastic method for segmentation of motion vector fields or as known as displacement fields in compressed videos. The addressed field sequence is analysed and partitioned to temporally associated, motion-semantic regions by three analysis steps. The global motion based reliability analysis assesses the reliability extent of each encoded displacement vector. Upon this measure array, an initial partition is approximated by the region displacement prediction. The subsequent relaxation procedure optimises the partition shape, coverage, and location using the stochastic motion coherency model and the second-order random field based contour smoothness. The visually convincing results are demonstrated from the standard sequences.
Keywords: Compressed video analysis for media adaptation; Stochastic motion segmentation; Stochastic object tracking

Model-Based Synthetic View Generation from a Monocular Video Sequence - Tsai, C.; Eisert, Peter; Girod, Bernd; Katsaggelos, A. K. (International Conference on Image Processing (ICIP 97), 1997)
LMS::415

Modeling and Optimization of Video Transmission Systems - Stuhlmüller, Klaus (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2000)

Modeling an Experimental Biological Modulator for Molecular Communications by Electrical Circuits - Schäfer, Maximilian; Wicke, Wayan; Grebenstein, Laura; Strobl, Christian; Stavracakis Peixoto, Renata; Kirchner, Jens; Burkovski, Andreas; Fischer, Georg; Weigel, Robert; Rabenstein, Rudolf; Schober, Robert (4th Workshop on Molecular Communications, 2019)
Experimental systems are necessary to validate and improve theoretical molecular communication models. We propose an analytical model for a biological modulator based on genetically modified Escherichia coli (E. coli) bacteria which convert an electrically-controlled light signal into a chemical response. Techniques from system identification and electrical networks synthesis are used to model the complex biological system. Compared to a simple reference model, a better fit to the observed dynamics in the experimental data is obtaine

Modeling of Energy Consumption and Streaming Video QoE using a Crowdsourcing Dataset - Herglotz, Christian; Robitza, Werner; Kränzler, Matthias; Kaup, André; Raake, Alexander (The 14th International Conference on Quality of Multimedia Experience (QoMEX), 2022)
In the past decade, we have witnessed an enormous growth in the demand for online video services. Recent studies estimate that nowadays, more than 1% of the global greenhouse gas emissions can be attributed to the production and use of devices performing online video tasks. As such, research on the true power consumption of devices and their energy efficiency during video streaming is highly important for a sustainable use of this technology. At the same time, over-the-top providers strive to offer high-quality streaming experiences to satisfy user expectations. Here, energy consumption and QoE partly depend on the same system parameters. Hence, a joint view is needed for their evaluation. In this paper, we perform a first analysis of both end-user power efficiency and Quality of Experience of a video streaming service. We take a crowdsourced dataset comprising 447,000 streaming events from YouTube and estimate both the power consumption and perceived quality. The power consumption is modeled based on previous work which we extended towards predicting the power usage of different devices and codecs. The user-perceived QoE is estimated using a standardized model. Our results indicate that an intelligent choice of streaming parameters can optimize both the QoE and the power efficiency of the end user device. Further, the paper discusses limitations of the approach and identifies directions for future researc
Keywords: Quality of Experience; video streaming; energy consumption; energy efficiency

Modeling of Image Acquisition for Improving Digital Camera Systems - Schöberl, Michael (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2013)
Images and movies are an important aspect of our life as they transport stories and emotions. The image acquisition in a camera plays a major role as many decisions are made during recording whereas a correction afterward is often impossible. Thus, an image needs to be captured right in the first place - otherwise a unique moment might be gone forever. This work analyzes the image acquisition process in digital cameras and shows a complete model of the conversions that take place. The influence of spatial and temporal sampling as well as image noise are discussed. Furthermore, the limitations from noise and dynamic range are analyzed and optimized. Finally, the camera enhancement by non-regular sub-sampling is demonstrated. In all aspects, various camera improvements are show

Modeling of image shutters and motion blur in analog and digital camera systems - Schöberl, Michael; Foessel, Siegfried; Bloss, Hans; Kaup, André (2009 IEEE International Conference on Image Processing, ICIP 2009, 2009)
For motion imaging the perceived smoothness of a sequence highly depends on motion blur. The exposure for each frame is started and ended with a shutter mechanism. There are different implementations and some of them have non-ideal behavior which can introduce artifacts. In this paper an overview of real-world shutter implementations both for analog and digital camera systems is shown. We develop a general description that models all types of shutters and their imperfections. Specific models for common shutter types are presented. Measurements are used to estimate unknown parameters. The modeled shutters are finally used for a virtual camera simulation. Typical artifacts can be simulated and directly compared for different shutter types and parameters. This powerful tool is useful for the construction of camera systems and allows design decisions to be directly compared before building the camera system. ©2009 IEE
Keywords: Cameras; Image generation; Image sensors; Imaging; Modeling

Modeling of Transport Processes in Bounded Domains - Schäfer, Maximilian; Rabenstein, Rudolf (3rd Workshop on Molecular Comunications, 2018)
In the field of molecular communications (MC) new transport phenomena for the transmission of information have to be investigated. Especially the diffusion of particles in fluids serves as a channel model in MC. The dynamics of diffusion processes can be modeled by the application of functional transformations. They lead to powerful semi-analytical models, which offer advantages over pure numerical models, e.g low run-time algorithms, high variability, analytical relation to parameters from communications theory, and the extendability to different kinds of boundary conditions.
Keywords: Functional Transformations; Molecular Communications

Modeling the energy consumption of HEVC intra decoding - Herglotz, Christian; Springer, Dominic; Eichenseer, Andrea; Kaup, André (2013 20th International Conference on Systems, Signals and Image Processing, IWSSIP 2013, 2013)
Battery life is one of the major limitations to mobile device use, which makes research on energy efficient soft- and hardware an important task. This paper investigates the energy required by a CPU when decoding compressed bitstream videos on mobile platforms. A model is derived that describes the energy consumption of the new HEVC decoder for intra coded videos. We show that the relative estimation error of the model is smaller than 3.2% and that the model can be used to build encoders aiming at minimizing decoding energy. © 2013 IEE

Modeling the Energy Consumption of HEVC P- and B-Frame Decoding - Herglotz, Christian; Springer, Dominic; Kaup, André (Proc. of Intl. Conf. on Image Processing (ICIP), 2014)
Video decoding on portable devices such as smartphones or tablet PCs requires a considerable amount of battery power, shortening their operating time significantly. Hence, tools aiming at minimizing the decoding energy are of special interest. To this end, this paper presents a novel model capable of estimating the energy consumed when decoding an HEVC-coded video. This information can be used to optimize implementations and improve the encoding procedure. An accurate and a more convenient simple model is proposed that, for the evaluated set of test videos, achieved an average relative estimation error of 2.34% and 3.63%, respectivel
UnivIS-Import:2015-04-16:Pub.2014.tech.IE.LEN.modeli
Keywords: GRK-1773

Modeling the Energy Consumption of the HEVC Decoding Process - Herglotz, Christian; Springer, Dominic; Reichenbach, Marc; Stabernack, Benno; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2018)
In this paper, we present a bit stream feature based energy model that accurately estimates the energy required to decode a given HEVC-coded bit stream. Therefore, we take a model from literature and extend it by explicitly modeling the inloop filters, which was not done before. Furthermore, to prove its superior estimation performance, it is compared to seven different energy models from literature. By using a unified evaluation framework we show how accurately the required decoding energy for different decoding systems can be approximated. We give thorough explanations on the model parameters and explain how the model variables are derived. To show the modeling capabilities in general, we test the estimation performance for different decoding software and hardware solutions, where we find that the proposed model outperforms the models from literature by reaching frame-wise mean estimation errors of less than 7% for software and less than 15% for hardware based system
Keywords: decoder; energy; estimation; HEVC; model; power

Modeling the Energy Consumption of the HEVC Software Encoding Process using Processor Events - Ramasubbu, Geetha; Kaup, André; Herglotz, Christian (2024 IEEE 26th International Workshop on Multimedia Signal Processing (MMSP), 2024)

Modeling the HEVC Encoding Energy Using the Encoder Processing Time - Ramasubbu, Geetha; Kaup, André; Herglotz, Christian (IEEE International Conference on Image Processing (ICIP) 2022, 2022)
The global significance of energy consumption of video communication renders research on the energy need of video coding an important task. To do so, usually, a dedicated setup is needed that measures the energy of the encoding and decoding system. However, such measurements are costly and complex. To this end, this paper presents the results of an exhaustive measurement series using the x265 encoder implementation of HEVC and analyzes the relation between encoding time and encoding energy. Finally, we introduce a simple encoding energy estimation model which employs the encoding time of a lightweight encoding process to estimate the encoding energy of complex encoding configurations. The proposed model reaches a mean estimation error of 11.35% when averaged over all presets. The results from this work are useful when the encoding energy estimate is required to develop new energy-efficient video compression algorithms.<br
Keywords: video coding; energy-efficiency; HEVC; x265; presets

Modelle zur regionenorientierten Bildbeschreibung - Kaup, André (Friedrich-Alexander-Universität Erlangen-Nürnberg, 1995)

Modelling the Visual Process of filling in Based on Localised Spectral Image Analysis - Kaup, André; Aach, T. (Sixteenth European Conference on Visual Perception, Edinburgh, United Kingdom, 1993)
LMS::994

Modellreduktion als Werkzeug zur effektiven Simulation des Gebäudewärmehaushalts - Rabenstein, Rudolf (Tagungsbericht 8. Internationales Sonnenforum, 1992)

Models for Continuous and Discrete Multidimensional Systems - Rabenstein, Rudolf; Trautmann, Lutz (Proc. Second Workshop on N-Dimensional Systems (NDS2000), 2000)
LMS::157

Monte Carlo Simulation of the PLL FM Demodulator - Hasan, Pavel (Proceedings 11th European Simulation Multiconference, 1997)
LMS::407

Montgomery self-imaging using computer-generated diffractive optical elements - Jahns, J.; Knuppertz, H.; Lohmann, Adolf (Optics Communications, 2003)
LMS::650

Motion-Adapted Three-Dimensional Frequency Selective Extrapolation - Spruck, Andreas; Jonscher, Markus; Seiler, Jürgen; Kaup, André (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019)
It has been shown, that high resolution images can be acquired using a low resolution sensor with non-regular sampling. Therefore, post-processing is necessary. In terms of video data, not only the spatial neighborhood can be used to assist the reconstruction, but also the temporal neighbor-hood. A popular and well performing algorithm for this kind of problem is the three-dimensional frequency selective extrapolation (3D-FSE) for which a motion adapted version is introduced in this paper. This proposed extension solves the problem of changing content within the area considered by the 3D-FSE, which is caused by motion within the sequence. Because of this motion, it may happen that regions are em-phasized during the reconstruction that are not present in the original signal within the considered area. By that, false content is introduced into the extrapolated sequence, which affects the resulting image quality negatively. The novel extension, presented in the following, incorporates motion data of the sequence in order to adapt the algorithm accordingly, and compensates changing content, resulting in gains of up to 1.75 dB compared to the existing 3D-FS

Motion-based Analysis and Segmentation of Image Sequences using 3-D Scene Models - Steinbach, Eckehard; Eisert, Peter; Girod, Bernd (Signal Processing, 1998)
LMS::340

Motion Compensated Frame Rate Up-Conversion Using 3D Frequency Selective Extrapolation and a Multi-Layer Consistency Check - Bätz, Michel; Brand, Fabian; Eichenseer, Andrea; Kaup, André (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017)
A high temporal resolution is desirable in many applications such as entertainment systems, automotive systems, or video surveillance. Apart from using cameras with a higher temporal resolution, it is also possible to employ frame rate up-conversion methods to obtain an enhanced temporal resolution. In principle, those algorithms can be grouped into approaches that rely on a motion estimation and approaches that do not. Both strategies typically process a video sequence frame by frame and take into account only the directly adjacent frames to compute the intermediate frame. In this paper, we propose a frame rate up-conversion technique that employs a motion compensated three-dimensional reconstruction algorithm. As a result, the proposed method takes into account more than two frames and is capable of jointly reconstructing up to a certain amount of missing frames in a video sequence. Furthermore, we present a multi-layer consistency check to further improve the reconstruction. On average, simulation results show a luminance PSNR gain compared to a conventional frame rate up-conversion method of 0.5 dB. Visual examples substantiate our objective result
LMS::1706
Keywords: temporal resolution enhancement; video processing; frame rate up-conversion; signal extrapolation

Motion compensated frequency selective extrapolation for error concealment in video coding - Seiler, Jürgen; Kaup, André (16th European Signal Processing Conference, EUSIPCO 2008, 2008)
Although wireless and IP-based access to video content gives a new degree of freedom to the viewers, the risk of severe block losses caused by transmission errors is always present. The purpose of this paper is to present a new method for concealing block losses in erroneously received video sequences. For this, a motion compensated data set is generated around the lost block. Based on this aligned data set, a model of the signal is created that continues the signal into the lost areas. Since spatial as well as temporal informations are used for the model generation, the proposed method is superior to methods that use either spatial or temporal information for concealment. Furthermore it outperforms current state of the art spatio-temporal concealment algorithms by up to 1.4 dB in PSNR. copyright by EURASI

Motion Compensated Three-Dimensional Frequency Selective Extrapolation for improved error concealment in video communication - Seiler, Jürgen; Kaup, André (Journal of Visual Communication and Image Representation, 2011)
During transmission of video data over error-prone channels the risk of getting severe image distortions due to transmission errors is ubiquitous. To deal with image distortions at decoder side, error concealment is applied. This article presents Motion Compensated Three-Dimensional Frequency Selective Extrapolation, a novel spatio-temporal error concealment algorithm. The algorithm uses fractional-pel motion estimation and compensation as initial step, being followed by the generation of a model of the distorted signal. The model generation is conducted by an enhanced version of Three-Dimensional Frequency Selective Extrapolation, an existing error concealment algorithm. Compared to this existent algorithm, the proposed one yields an improvement in concealment quality of up to 1.64 dB PSNR. Altogether, the incorporation of motion compensation and the improved model generation extends the already high extrapolation quality of the underlying Frequency Selective Extrapolation, resulting in a gain of more than 3 dB compared to other well-known error concealment algorithms. © 2011 Elsevier Inc. All rights reserve
Keywords: Error concealment; Frequency selective extrapolation; Hybrid video coding; Image restoration; Motion compensation; Signal extrapolation; Sparse modeling; Video communication

Motion-Compensating Long-Term Memory Prediction - Wiegand, Thomas; Zhang, Xiaozheng; Girod, Bernd (Proc. IEEE International Conference on Image Processing (ICIP 97), 1997)
LMS::422

Motion Estimation for Fisheye Video Sequences Combining Perspective Projection with Camera Calibration Information - Eichenseer, Andrea; Bätz, Michel; Kaup, André (Proceedings of the IEEE International Conference on Image Processing, 2016)
Fisheye cameras prove a convenient means in surveillance and automotive applications as they provide a very wide field of view for capturing their surroundings. Contrary to typical rectilinear imagery, however, fisheye video sequences follow a different mapping from the world coordinates to the image plane which is not considered in standard video processing techniques. In this paper, we present a motion estimation method for real-world fisheye videos by combining perspective projection with knowledge about the underlying fisheye projection. The latter is obtained by camera calibration since actual lenses rarely follow exact models. Furthermore, we introduce a re-mapping for ultra-wide angles which would otherwise lead to wrong motion compensation results for the fisheye boundary. Both concepts extend an existing hybrid motion estimation method for equisolid fisheye video sequences that decides between traditional and fisheye block matching in a block-based manner. Compared to that method, the proposed calibration and re-mapping extensions yield gains of up to 0.58 dB in luminance PSNR for real-world fisheye video sequences. Overall gains amount to up to 3.32 dB compared to traditional block matchin
Keywords: fisheye lens; camera calibration; perspective projection; motion estimation; motion compensation

Motion Estimation for Fisheye Video with an Application to Temporal Resolution Enhancement - Eichenseer, Andrea; Bätz, Michel; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2018)

Motion Plane Adaptive Motion Modeling for Spherical Video Coding in H.266/VVC - Regensky, Andy; Herglotz, Christian; Kaup, André (IEEE International Conference on Image Processing (ICIP), 2023)
Motion compensation is one of the key technologies enabling the high compression efficiency of modern video coding standards. To allow compression of spherical video content, special mapping functions are required to project the video to the 2D image plane. Distortions inevitably occurring in these mappings impair the performance of classical motion models. In this paper, we propose a novel motion plane adaptive motion modeling technique (MPA) for spherical video that allows to perform motion compensation on different motion planes in 3D space instead of having to work on the - in theory arbitrarily mapped - 2D image representation directly. The integration of MPA into the state-of-the-art H.266/VVC video coding standard shows average Bjøntegaard Delta rate savings of 1.72% with a peak of 3.37% based on PSNR and 1.55% with a peak of 2.92% based on WS-PSNR compared to VTM-14.2.<br
Keywords: 360-degree; omnidirectional; motion model; inter prediction; video coding

Motion Segmentation for Regional-Based Coding - Stuhlmüller, Klaus; Girod, Bernd (Proceedings 6th International Conference on Image Processing and its Applications (IPA 97), 1997)
LMS::399

Motion vector analysis based homography estimation for efficient HEVC compression of 2D and 3D navigation video sequences - Springer, Dominic; Simmet, F.; Niederkorn, D.; Kaup, André (2013 20th IEEE International Conference on Image Processing, ICIP 2013, 2013)
Navigation systems have become complex devices in automobiles nowadays. As part of large in-car infotainment systems, these devices undergo extensive hardware and software tests in order to assert correct system behavior under all circumstances. During field tests, the display output is typically recorded in compressed form for days or weeks, followed by a thorough analysis of the video data. In this paper, we demonstrate how to setup an HEVC-based compression solution specifically designed for navigation sequence content. We show how rotational motion, which is a dominant characteristic, can be estimated and compensated in an efficient way. We avoid any complex feature-based approaches for global motion estimation but find precise motion parameters by analyzing and filtering motion vector sets produced by HEVC during encoding. While achieving compression efficiency similar to a feature-based approach, processing time for global motion estimation can be significantly reduced. © 2013 IEE
Keywords: HEVC; Motion Vector Analysis; RANSAC; Rotational Motion Estimation; Video Compression

Motor data-regularized nonnegative matrix factorization for ego-noise suppression - Schmidt, Alexander; Brendel, Andreas; Haubner, Thomas; Kellermann, Walter (EURASIP Journal on Audio, Speech, and Music Processing, 2020)
Ego-noise, i.e., the noise a robot causes by its own motions, significantly corrupts the microphone signal and severely impairs the robot’s capability to interact seamlessly with its environment. Therefore, suitable ego-noise suppression techniques are required. For this, it is intuitive to use also motor data collected by proprioceptors mounted to the joints of the robot since it describes the physical state of the robot and provides additional information about the ego-noise sources. In this paper, we use a dictionary-based approach for ego-noise suppression in a semi-supervised manner: first, an ego-noise dictionary is learned and subsequently used to estimate the ego-noise components of a mixture by computing a weighted sum of dictionary entries. The estimation of the weights is very sensitive against other signals beside ego-noise contained in the mixture. For increased robustness, we therefore propose to incorporate knowledge about the physical state of the robot to the estimation of the weights. This is achieved by introducing a motor data-based regularization term to the estimation problem which promotes similar weights for similar physical states. The regularization is derived by representing the motor data as a graph and imprints the intrinsic structure of the motor data space onto the dictionary model. We analyze the proposed method and evaluate its ego-noise suppression performance for a large variety of different movements and demonstrate the superiority of the proposed method compared to an approach without using motor data.
CRIS-Team Scopus Importer:2020-08-07
Keywords: Ego-noise; Humanoïd robot; Motor data; Robot audition

Moving Object Detection in the H.264/AVC Compressed Domain - Laumer, Marcus; Amon, P.; Hutter, A.; Kaup, André (APSIPA Transactions on Signal and Information Processing, 2016)
This paper presents a moving object detection algorithm for H.264/AVC video streams that is applied in the compressed domain. The method is able to extract and analyze several syntax elements from any H.264/AVC-compliant bit stream. The number of analyzed syntax elements depends on the mode in which the method operates. The algorithm is able to perform either a spatiotemporal analysis in a single step or a two-step analysis that starts with a spatial analysis of each frame, followed by a temporal analysis of several subsequent frames. Thereby, in each mode either only (sub-)macroblock types and partition modes or, additionally, quantization parameters are analyzed. The evaluation of these syntax elements enables the algorithm to determine a “weight” for each 4×4 block of pixels that indicates the level of motion within this block. A final segmentation after creating these weights segments each frame to foreground and background and hence indicates the positions and sizes of all moving objects. Our experiments show that the algorithm is able to efficiently detect moving objects in the compressed domain and that it is configurable to process a large number of parallel bit streams in real tim

Moving refractive optical low-pass filter for digital cameras - Schöberl, Michael; Ernst, Jan; Schnurrer, Wolfgang; Fößel, S.; Kaup, André (SPIE Electronic Imaging - Digital Photography VII, 2011)
As with any sampling process, image acquisition is subject to spatial domain aliasing. Many designs of optical filters have been proposed that reduce the amount of aliasing in image acquisition. Still, the most common systems are based on birefringent optical materials. Unfortunately, these filters are expensive and the actual filter response curve can only partially be influenced. We propose a new refractive optical low-pass filter (ROLPF) based on refraction at a structured glass plate. The transparent plate features a single surface with a defined structure. This leads to refraction and will lead to a modified light distribution on the sensor. The plate is placed in front of the sensor and is moved in the image sensor plane during an exposure. This causes an averaging process and results in a spatial domain filtering. The response curve of this setup can be controlled by designing the gradient of the surface. This filter will be easy to manufacture, it offers a true low-pass characteristic which can be specifically designed and the bandwidth can be adjusted at runtime by variation of the distance to the sensor. © 2011 SPIE-IS&
Keywords: Image acquisition; Optical low-pass filter (OLPF); Spatial domain aliasing

MPEG-4 for Broadband Communications - Schäfer, R.; Kaup, André; Seytter, F.; Panis, S.; Laier, J.; Finger, U. (Multimedia Systems and Applications, SPIE, 1998)
LMS::476

MPEG-H Audio - The New Standard for Universal Spatial / 3D Audio Coding - Herre, Jürgen; Hilpert, Johannes; Kuntz, Achim; Plogsties, Jan (137th AES Convention, 2014)
Recently, a new generation of spatial audio formats were introduced that include elevated loudspeakers and surpass traditional surround sound formats, such as 5.1, in terms of spatial realism. To facilitate high-quality bitrate-efficient distribution and flexible reproduction of 3D sound, the MPEG standardization group recently started the MPEG-H Audio Coding development for the universal carriage of encoded 3D sound from channel-based, object-based, and HOA-based input. High quality reproduction is supported for many output formats from 22.2 and beyond down to 5.1, stereo, and binaural reproduction—independently of the original encoding format, thus overcoming incompatibility between various 3D formats. The paper describes the current status of the standardization project and provides an overview of the system architecture, its capabilities, and performanc
herre_papers_spatial_audio_coding_and_3d_audio_coding

MPEG-H Audio - The New Standard for Universal Spatial/3D Audio Coding - Herre, Jürgen; Hilpert, Johannes; Kuntz, Achim; Plogsties, Jan (Journal of the Audio Engineering Society, 2014)
Recently, a new generation of spatial audio formats were introduced that include elevated loudspeakers and surpass traditional surround sound formats, such as 5.1, in terms of spatial realism. To facilitate high-quality bit-rate-efficient distribution and flexible reproduction of 3D sound, the MPEG standardization group recently started the MPEG-H Audio Coding development for the universal carriage of encoded 3D sound from channel-based, objectbased, and HOA-based input. High quality reproduction is supported for many output formats from 22.2 and beyond down to 5.1, stereo, and binaural reproduction-independently of the original encoding format-thus overcoming incompatibility between various 3D formats. The paper describes the current status of the standardization project and provides an overview of the system architecture, its capabilities, and performanc
herre_books_journals

MPEG-Standards: Techniken und Entwicklungstrends - Kaup, André (Fachzeitschrift für Fernsehen, Film und elektronische Medien, 2001)
LMS::219

MULTI-CAT - ein mobiles Meßsystem zur Bestimmung von Lüftungskenngrößen - Heidt, F. D.; Rabenstein, Rudolf (KI, Luft- und Kaltetechnik, 1994)
LMS::1601

Multicenter evaluation of signal enhancement algorithms for hearing aids - Luts, H.; Eneman, K.; Wouters, J.; Schulte, M.; Vormann, M.; Buechler, M.; Dillier, N.; Houben, R.; Dreschler, W.; Froehlich, M.; Puder, H.; Grimm, G.; et al. (Journal of the Acoustical Society of America, 2010)
In the framework of the European HearCom project, promising signal enhancement algorithms were developed and evaluated for future use in hearing instruments. To assess the algorithms' performance, five of the algorithms were selected and implemented on a common real-time hardware/software platform. Four test centers in Belgium, The Netherlands, Germany, and Switzerland perceptually evaluated the algorithms. Listening tests were performed with large numbers of normal-hearing and hearing-impaired subjects. Three perceptual measures were used: speech reception threshold (SRT), listening effort scaling, and preference rating. Tests were carried out in two types of rooms. Speech was presented in multitalker babble arriving from one or three loudspeakers. In a pseudo-diffuse noise scenario, only one algorithm, the spatially preprocessed speech-distortion-weighted multi-channel Wiener filtering, provided a SRT improvement relative to the unprocessed condition. Despite the general lack of improvement in SRT, some algorithms were preferred over the unprocessed condition at all tested signal-to-noise ratios (SNRs). These effects were found across different subject groups and test sites. The listening effort scores were less consistent over test sites. For the algorithms that did not affect speech intelligibility, a reduction in listening effort was observed at 0 dB SN
LMS::847

Multichannel acoustic echo cancellation in the wave domain with increased robustness to nonuniqueness - Schneider, Martin; Kellermann, Walter (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2016)
Acoustic echo cancellation (AEC) is significantly more challenging in the case of multichannel reproduction compared to the single-channel case. This is mainly due to the socalled nonuniqueness problem, which becomes especially severe for massive multichannel reproduction systems like, e.g., wave field synthesis systems or higher-order ambisonics, where the different loudspeaker signals are often strongly correlated. In this paper, a multichannel wave-domain AEC structure is proposed that maintains robustness without altering the loudspeaker signals, as is commonly required to preserve the reproduction quality. The proposed method exploits the dominance of certain couplings in the wave-domain representation of the loudspeaker-enclosuremicrophone system. In this representation, preferring a strong weight of the dominant couplings in the identified system improves the system identification performance. A modification of the wellknown generalized frequency-domain adaptive filtering algorithm is then presented to implement the proposed approach, including a self-contained description of all necessary components. An experimental evaluation compares the proposed method to competing approaches and shows that the proposed method compares well in terms of convergence behavior while avoiding the performance degradation, which is intrinsic to other approache
Keywords: Acoustic signal processing; Active noise reduction; Adaptive filters; Adaptive signal processing; Echo cancellers; Signal processing

Multichannel Acoustic Signal Extraction for Reverberant Environments - Reindl, Klaus (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2015)
This work aims on finding new and improved approaches for broadband acoustic signal extraction. The signal extraction problem is treated as a specific source separation problem, where a single desired signal is to be separated from all remaining undesired components. For this, a generic framework is exploited, which is based on Shannon’s mutual information measure modified for nonstationary signals, and which also allows for temporal statistical dependencies of the source signals. The use of multivariate nongaussian probability density functions in the cost function also facilitates the exploitation of both the nonwhiteness and the nongaussianity of speech and audio signals. Additionally, the criterion is complemented with linear constraints for the intended separation of the desired components from the sum of all undesired components. A detailed analysis of the resulting update rule allows significant simplifications and leads to a novel Minimum Mutual Information-Generalized Sidelobe Canceler. The actual realization of this MMI-GSC depends only on some coarse prior information of the desired source position, which renders this concept very attractive for practical application

Multichannel Acoustic Signal Processing for Human/Machine Interfaces - Fundamental Problems and Recent Advances - Kellermann, Walter; Buchner, Herbert; Herbordt, Wolfgang; Aichner, Robert (Proc. Int. Conf. on Acoustics (ICA), 2004)
LMS::484

Multichannel acoustic signal processing for speech acquisition - Kellermann, Walter; Buchner, Herbert; Aichner, Robert (Conf. Rec. Joint Workshop for Hands-Free Speech Communication and Microphone Arrays (HSCMA), 2005)
LMS::536

Multichannel bin-wise robust frequency-domain adaptive filtering and its application to adaptive beamforming - Herbordt, Wolfgang; Buchner, Herbert; Nakamura, S.; Kellermann, Walter (IEEE Transactions on Audio Speech and Language Processing, 2007)
Least-squares error (LSE) or mean-squared error (MSE) optimization criteria lead to adaptive filters that are highly sensitive to impulsive noise. The sensitivity to noise bursts increases with the convergence speed of the adaptation algorithm and limits the performance of signal processing algorithms, especially when fast convergence is required, as for example, in adaptive beamforming for speech and audio signal acquisition or acoustic echo cancellation. In these applications, noise bursts are frequently due to undetected double-talk. In this paper, we present impulsive noise robust multichannel frequency-domain adaptive filters (MC-FDAFs) based on outlier-robust M-estimation using a Newton algorithm and a discrete Newton algorithm, which are especially designed for frequency bin-wise adaptation control. Bin-wise adaptation and control in the frequency-domain enables the application of the outlier-robust MC-FDAFs to a generalized sidelobe canceler (GSC) using an adaptive blocking matrix for speech and audio signal acquisition. It is shown that the improved robustness leads to faster convergence and to higher interference suppression relative to nonrobust adaptation algorithms, especially during periods of strong interference © 2007 IEE
Keywords: Adaptive filtering; Beamforming; Doubletalk; Frequency domain; Generalized sidelobe canceler; Microphone array; Robust statistics

Multichannel cross-talk cancellation in a call-center scenario using frequency-domain adaptive filtering - Lombard, Anthony; Kellermann, Walter (Proc. Int. Workshop on Acoustic Echo and Noise Control (IWAENC), 2008)
We propose a multichannel residual cross-talk cancellation strategy to operate in environments where each microphone signal is dominated by one of several simultaneously active sound sources, like in a call-center scenario. The method relies on a two-step adaptive filtering scheme derived from an established GSC structure. The filter updates are performed in the frequency domain to allow an effective DFT-bin-wise adaptation control. This new scheme is applicable to an arbitrary number of competing sources and experiments conducted for three and seven speech sources show that it allows to reduce the amount of undesired cross-talk components by about 10dB, without noticeable distortion of the desired signa
LMS::746
Keywords: Interference cancellation; adaptive filtering

Multichannel estimation of the power spectral density of noise for mixtures of nonstationary signals - Herbordt, Wolfgang; Nakamura, S.; Kellermann, Walter (IPSJ SIG Technical Reports, 2004)
LMS::597

Multichannel Frequency-Domain Adaptive Filtering with Application to Acoustic Echo Cancellation - Buchner, Herbert; Benesty, J.; Kellermann, Walter (Adaptive signal processing: Application to real-world problems, 2003)

Multichannel nonnegative matrix factorization for ego-noise suppression - Haubner, Thomas; Schmidt, Alexander; Kellermann, Walter (ITG Conference on Speech Communication, 2018)
A mobile robot generates significant self-induced noise, e.g., by its joints and moving parts of its body, which is referred to as ego-noise. Ego-noise is of crucial impor- tance in robot audition since it massively corrupts the mi- crophone signals and thus degrades the robot’s capability to interact intuitively with its acoustic environment. There- fore, appropriate mechanisms for ego-noise suppression are required. In this paper, we investigate different multi- channel Nonnegative Matrix Factorization (MNMF) algo- rithms with respect to their suitability for ego-noise sup- pression, propose a modified semi-supervised MNMF ap- proach and evaluate its performanc

Multichannel nonnegative matrix factorization with motor data-regularized activations for robust ego-noise suppression - Schmidt, Alexander; Kellermann, Walter (ICAS 2021 - 2021 IEEE International Conference on Autonomous Systems, Proceedings, 2021)
The suppression of ego-noise is often addressed using dictionary-based methods where the characteristic spectral structure of ego-noise is approximated by a linear combination of dictionary entries. A blind, entirely audio data-based selection of the dictionary entries is, however, challenging and reacts sensitive against other signals besides ego-noise in a mixture. For a more robust behavior, we propose a motor data-dependent regularization term which promotes similar activations for similar physical states of the robot. The proposed regularization term is added to a multichannel nonnegative matrix factorization (MNMF)-based signal model and according update rules are derived. We analyze the proposed method for a challenging ego-noise scenario and demonstrate the efficacy of the method compared to an approach for which no motor data is used.
CRIS-Team Scopus Importer:2021-10-29
Keywords: Ego-noise; Motor data; Multichannel nonnegative matrix factorization

Multichannel parameter estimation - Markovich-Golan, Shmulik; Kellermann, Walter; Gannot, Sharon (Audio Source Separation and Speech Enhancement, 2018)

Multichannel parametric speech enhancement - Srinivasan, S.; Aichner, Robert; Kleijn, B.; Kellermann, Walter (IEEE Signal Processing Letters, 2006)
We present a parametric model-based multichannel approach for speech enhancement. By employing an autoregressive model for the speech signal and using a trained codebook of speech linear predictive coefficients, minimum mean square error estimation of the speech signal is performed. By explicitly accounting for steering errors in the signal model, robust estimates are obtained. Experiments show that the proposed method results in significant performance gains. © 2006 IEE
Keywords: Acoustic noise; Acoustic signal processing; Array signal processing; Autoregressive processes; Speech enhancement; Speech processing

Multichannel signal enhancement algorithms for assisted listening devices - Doclo, Simon; Kellermann, Walter; Makino, Shoji; Nordholm, Sven (IEEE Signal Processing Magazine, 2015)
In everyday environments, we are frequently immersed by unwanted acoustic noise and interference while we want to listen to acoustic signals, most often speech. Technology for assisted listening is then desired to increase the efficiency of speech communication, reduce listener fatigue, or just allow for enjoying undisturbed sounds (e.g., music). For people with normal hearing, assisted listening devices (ALDs) mainly aim to achieve hearing protection or increase listening comfort; however, for hearing-impaired individuals, as the most prominent user group so far, further progress of assisted listening technology is crucial for better inclusion into our world of pervasive acoustic communicatio

Multichannel Signal Processing at Bell Labs Acoustics Research - Sampled by a Postdoc - Kellermann, Walter (Conf. Rec. 75th Anniversary Meeting, 2004)
LMS::525

Multi-channel source separation preserving spatial information - Aichner, Robert; Buchner, Herbert; Zourub, Meray; Kellermann, Walter (2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, 2007)
In this paper we propose two novel methods for preserving the spatial information in source separation algorithms. Our approach is applicable to any source separation algorithm and is based on an additional supervised adaptive filtering with the reference signals generated by the source separation system. If a special constrained optimization,scheme is applied to derive the source separation algorithm, then the novel approach.can be simplified. The quality of the spatial representation and the separation performance of both methods and two state-of-the-art approaches from the literature have been evaluated by a MUSHRA listening test according to the relevant ITU recommendation showing that the novel methods clearly outperform the state-of-the-art approaches. © 2007 IEE
Keywords: Hearing aids; Source separation; Spatial auditory displays; Spatial information; Spatialization

Multi-conditioned Graph Diffusion for Neural Architecture Search - Asthana, Rohan; Conrad, Joschua; Dawoud, Youssef; Ortmanns, Maurits; Belagiannis, Vasileios (Transactions on Machine Learning Research, 2024)
Neural architecture search automates the design of neural network architectures usually by exploring a large and thus complex architecture search space. To advance the architecture search, we present a graph diffusion-based NAS approach that uses discrete conditional graph diffusion processes to generate high-performing neural network architectures. We then propose a multi-conditioned classifier-free guidance approach applied to graph diffusion networks to jointly impose constraints such as high accuracy and low hardware latency. Unlike the related work, our method is completely differentiable and requires only a single model training. In our evaluations, we show promising results on six standard benchmarks, yielding novel and unique architectures at a fast speed, i.e. less than 0.2 seconds per architecture. Furthermore, we demonstrate the generalisability and efficiency of our method through experiments on ImageNet dataset.<br
Keywords: deep learning; neural architecture search; diffusion models

Multidimensional localization of multiple sound sources using averaged directivity patterns of blind source separation systems - Lombard, Anthony; Rosenkranz, Tobias; Buchner, Herbert; Kellermann, Walter (2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, 2009)
In this paper, we propose a versatile acoustic source localization framework exploiting the self-steering capability of Blind Source Separation (BSS) algorithms. We provide a way to produce an acoustical map of the scene by computing the averaged directivity pattern of BSS demixing systems. Since BSS explicitly accounts for multiple sources in its signal propagation model, several simultaneously active sound sources can be located using this method. Moreover, the framework is suitable to any microphone array geometry, which allows application for multiple dimensions, in the near field as well as in the far field. Experiments demonstrate the efficiency of the proposed scheme in a reverberant environment for the localization of speech sources. ©2009 IEE
Keywords: Acoustic source localization; Blind source separation; Microphone arrays

Multidimensional localization of multiple sound sources using blind adaptive MIMO system identification - Lombard, Anthony; Buchner, Herbert; Kellermann, Walter (2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI, 2006)
The TDOA-based acoustic source localization approach is a powerful and widely-used method which can be applied for one source in several dimensions or several sources in one dimension. However the localization turns out to be more challenging when multiple sound sources should be localized in multiple dimensions, due to a spatial ambiguity phenomenon which requires to perform an intermediate step after the TDOA estimation and before the calculation of the geometrical source positions. In order to obtain the required set of TDOA estimates for the multidimensional localization of multiple sound sources, we apply a recently presented TDOA estimation method based on blind adaptive multiple-input-multiple-output (MIMO) system identification. We demonstrate that this localization method also provides valuable side information which allows us to resolve the spatial ambiguity without any prior knowledge about the source positions. Furthermore we show that the blind adaptive MIMO system identification allows a high spatial resolution. Experimental results for the localization of two sources in a two-dimensional plane show the effectiveness of the proposed scheme. © 2006 IEE

Multidimensional Signals and Systems - Rabenstein, Rudolf; Schäfer, Maximilian (Springer, 2023)
Comprehensive introduction to multidimensional signals and systems with more than 70 problems with solutions. From initial-boundary-value problems to discrete-time algorithms.<br

Multidimensional String Models - Trautmann, Lutz; Rabenstein, Rudolf (First Online Symposium for Electronics Engineers (OSEE), 2000)
LMS::205

Multidimensional System Simulation with Functional Transformations - Rabenstein, Rudolf (International Conference on Acoustics, Speech and Signal Processing, 1996)
The application of methods from multidimensional systems theory and digital signal processing opens new ways for the efficient simulation of time and space dependent problems like wave propagation or heat and mass transfer. The basic idea, is the application of suitably chosen functional transformations for the time and space coordinates. Initial and boundary conditions are considered in a systematic way. This leads to the structure of a discrete system which closely models the behaviour of the given continuous system and which is suitable for computer implementation. Numerical results show considerable savings in computer time over conventional finite difference methods.

Multidimensional transfer function models - Rabenstein, Rudolf; Trautmann, Lutz (IEEE Transactions on Circuits and Systems I-Regular Papers, 2002)
Transfer functions are a standard description of one-dimensional linear and time-invariant systems. They provide an alternative to the conventional representation by ordinary differential equations and are suitable for computer implementation. This article extends that concept to multidimensional (MD) systems, normally described by partial differential equations (PDEs). Transfer function modeling is presented for scalar and for vector PDEs. Vector PDEs contain multiple dependent output variables, e.g., a potential and a flux quantity. This facilitates the direct formulation of boundary and interface conditions in their physical context. It is shown how carefully constructed transformations for the space variable lead to transfer function models for scalar and vector PDEs. They are the starting point for the derivation of discrete models by standard methods for one-dimensional systems. The presented functional transformation approach is suitable for a number of technical applications, like electromagnetics, optics, acoustics and heat and mass transfer.
Keywords: Functional transformation; Multidimensional system; Partial differential equation; Transfer function

Multi-Exciter Panel Compensation for Wave Field Synthesis - Spors, Sascha; Seuberth, D.; Rabenstein, Rudolf (Proc. Annual German Conf. on Acoustics (DAGA), 2005)
LMS::546

Multiframe echo suppression based on orthogonal signal decompositions - Huang, Hai; Hofmann, Christian; Kellermann, Walter; Chen, Jingdong; Benesty, Jacob (ITG Conference on Speech Communication, 2016)
Acoustic echo arises due to the acoustic coupling between a loudspeaker and a microphone in a full-duplex voice communication device. Recently, the use of only acoustic echo suppression (AES) has been proposed without precise echo path estimation. In this paper, we propose an extension to the scheme of the multiframe Wiener filter. 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. Based on the orthogonal signal decompositions, we discuss how to form different echo suppression filters by optimizing different cost functions. We cover the design of the Wiener, MVDR, tradeoff, and LCMV filters. From a theoretical point of view, the Wiener and tradeoff filters are identical to the MVDR filter up to a scaling factor. Experimental results demonstrate that the developed filters can efficiently attenuate the undesired echo and the estimate near-end signal with little distortio

Multi-Frame Motion-Compensated Prediction for Video Transmission - Wiegand, Thomas (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2000)

Multi-Hypothesis Disparity-Compensated Light Field Compression - Ramanathan, P.; Flierl, Markus; Girod, Bernd (Proc. IEEE International Conference on Image Processing, 2001)
LMS::198

Multihypothesis Motion-Compensated Prediction with Forward-Adaptive Hypothesis Switching - Girod, Bernd (Proc. Picture Coding Symposium, 2001)
LMS::196

Multihypothesis Motion Estimation for Video Coding - Girod, Bernd (Proc. IEEE Data Compression Conference, 2001)
LMS::194

Multihypothesis Pictures for H.26L - Flierl, Markus; Wiegand, Thomas; Girod, Bernd (Proc. IEEE International Conference on Image Processing, 2001)
LMS::200

Multi-hypothesis, Volumetric Reconstruction of 3-D Objects from Multiple Calibrated Camera Views - Eisert, Peter; Girod, Bernd (IEEE Signal Processing Magazine, 1999)
LMS::300

Multi-Image Super-Resolution for Fisheye Video Sequences Using Subpixel Motion Estimation Based on Calibrated Re-Projection - Bätz, Michel; Eichenseer, Andrea; Kaup, André (European Signal Processing Conference (EUSIPCO), 2016)
Super-resolution techniques are a means for reconstructing a higher spatial resolution from low resolution content, which is especially important for automotive or surveillance systems. Furthermore, being able to capture a large area with a single camera can be realized by using ultra-wide angle lenses, as employed in so-called fisheye cameras. However, the underlying non-perspective projection function of fisheye cameras introduces significant radial distortion, which is not considered by conventional super-resolution techniques. In this paper, we therefore propose the integration of a fisheye-adapted motion estimation approach that is based on a calibrated re-projection into a multi-image super-resolution framework. The proposed method is capable of taking the fisheye characteristics into account, thus improving the reconstruction quality. Simulation results show an average gain in luminance PSNR of up to 0.3 dB for upscaling factors of 2 and 4. Visual examples substantiate the objective result

Multi-Image Super-Resolution Using a Dual Weighting Scheme Based on Voronoi Tessellation - Bätz, Michel; Eichenseer, Andrea; Kaup, André (IEEE International Conference on Image Processing (ICIP), 2016)
Increasing spatial resolution is often required in many applications such as entertainment systems or video surveillance. Apart from using higher resolution sensors, it is also possible to apply superresolution algorithms to realize an increased resolution. Those methods can be divided into approaches that rely on only a single low resolution image or on multiple low resolution video frames. While incorporating more frames into the super-resolution is beneficial for the resolution enhancement in principle, it is also likely to introduce more artifacts from inaccurate motion estimation. To alleviate this problem, various weightings have been proposed in the literature. In this paper, we propose an extended dual weighting scheme for an interpolation-based super-resolution method based on Voronoi tessellation that relies on both a motion confidence weight and a distance weight. Compared to non-weighted super-resolution, the proposed method yields an average gain in luminance PSNR of up to 1.29 dB and 0.61 dB for upscaling factors of 2 and 4, respectively. Visual comparisons substantiate the objective result
Keywords: resolution enhancement; image processing; multi-image super-resolution; voronoi tessellation

Multi-Image Super-Resolution Using a Locally Adaptive Denoising-Based Refinement - Bätz, Michel; Koloda, Jan; Eichenseer, Andrea; Kaup, André (IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), 2016)
Spatial resolution enhancement is of particular interest in many applications such as entertainment, surveillance, or automotive systems. Besides using a more expensive, higher resolution sensor, it is also possible to apply super-resolution techniques on the low resolution content. Super-resolution methods can be basically classified into single-image and multi-image super-resolution. In this paper, we propose the integration of a novel locally adaptive de noising-based refinement step as an intermediate processing step in a multi-image super-resolution framework. The idea is to be capable of removing reconstruction artifacts while preserving the details in areas of interest such as text. Simulation results show an average gain in luminance PSNR of up to 0.2 dB and 0.3 dB for an up scaling of 2 and 4, respectively. The objective results are substantiated by the visual impressio

Multi-Image Techniques for the Resolution Enhancement of Images and Video Se-quences - Bätz, Michel (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2018)
For many signal processing applications, images and videos with a high spatial resolution, a high frame rate, as well as a high dynamic range are desirable. Increasing the spatial, temporal, and radiometric resolution can be achieved either by using better, more expensive hardware or by applying suitable signal processing methods. This thesis focuses on multi-image approaches for the resolution enhancement of images and video sequences, from which video surveillance, automotive, and entertainment applications may benefit. To that end, various novel techniques for super-resolution, frame rate up-conversion, and high dynamic range imaging are presented that show promising results for both simulated and real-world data.

Multilevel Codes in Lattice-Reduction-Aided Equalization - Fischer, Robert F.H.; Huber, Johannes; Stern, Sebastian; Guter, Paulus (International Zurich Seminar on Information and Communication (IZS 2018), Zurich, Switzerland,, 2018)
The application of multilevel codes in lattice-reduction-aided equalization is considered, i.e., the decoding of multilevel codes when (Gaussian) integer linear combinations of the codewords in signal space are present. Typically, multilevel codes do not generate lattice codes, hence arbitrary integer linear combinations are not directly decodable. We show that this lattice property is not required, which relaxes the constraints on the component codes significantly. A generalized version of multistage decoding which incorporates a “carry correction” is proposed; it circumvents the lattice property and any integer linear combinations are decodable. Numerical simulations are given to cover the performance of the proposed method.<br

Multimedia Messaging mit MPEG-7 - Kaup, André; Heuer, J.; Rauschenbach, U. (Fachzeitschrift für Fernsehen, Film und elektronische Medien, 2002)
LMS::177

Multimedia Messaging Technologien auf der Basis von MPEG-7 - Heuer, J.; Kaup, André; Rauschenbach, U. (ITG-Fachbericht, 2001)

Multi-mode kernel-based minimum mean square error estimator for accelerated image error concealment - Koloda, Jan; Seiler, Jürgen; Peinado, A.; Kaup, André (Data Compression Conference (DCC), 2016)
Summary form only given. In this paper, we propose a novel multi-mode error concealment algorithm that aims at obtaining high quality reconstructions with reduced computational burden. Block-based coding schemes in packet loss-environment are considered. The proposed technique exploits the excellent reconstructing abilities of the kernel-based minimum mean square error (K-MMSE) estimator [1]. The complexity of our technique is dynamically adapted to the visual complexity of the area being reconstructed. The technique outperforms other state of the art algorithms and produces high quality reconstructions, equivalent to K-MMSE, while requiring less than one fourth of its computational tim

Multi-Model Motion Prediction for 360-Degree Video Compression - Regensky, Andy; Herglotz, Christian; Kaup, André (IEEE Access, 2023)
Efficient video compression is fundamental for enabling today’s highly interactive multimedia landscape. With the trend towards virtual reality, efficient storage and transmission of 360-degree video content becomes increasingly important. Recent works in this area addressed the design and investigation of 360-degree-specific motion models for improved compression efficiency. We investigate the strengths and limitations of these motion models and show that a single model cannot sufficiently cover all motion scenarios. A video codec that combines the strengths of multiple models in the motion compensation procedure is expected to achieve notable gains in compression efficiency. However, the additional motion model signaling and switching costs quickly outweigh the gains achieved by improved motion compensation. In this paper, we address this challenge by proposing advanced motion prediction and coding schemes that significantly reduce the resulting side information overhead. A novel multi-model motion vector prediction technique ensures seamless cooperation between different motion models by generalizing the motion modeling concept through forward and backward passes, and yields significant improvements in the overall compression efficiency. Our proposed hierarchical coding scheme is broadly applicable and is shown to be the most effective among comparable coding schemes. Experimental results demonstrate the performance of the proposed multi-model coding framework with average Bjøntegaard Delta rate savings of 3.20% with a peak of 4.49% based on PSNR and 2.76% with a peak of 4.04% based on WS-PSNR compared to the state-of-the-art H.266/VVC video coding standard.<br
Keywords: Three-dimensional displays; Solid modeling; Predictive models; Visualization; Video compression; Cameras; Encoding; Motion control; Video coding

Multi-Objective Design Space Exploration for the Optimization of the HEVC Mode Decision Process - Herglotz, Christian; Rosales, Rafael; Glaß, Michael; Teich, Jürgen; Kaup, André (Picture Coding Symposium (PCS), 2016)
Finding the best possible encoding decisions for compressing a video sequence is a highly complex problem. In this work, we propose a multi-objective Design Space Exploration (DSE) method to automatically find HEVC encoder implementations that are optimized for several different criteria. The DSE shall optimize the coding mode evaluation order of the mode decision process and jointly explore early skip conditions to minimize the four objectives a) bitrate, b) distortion, c) encoding time, and d) decoding energy. In this context, we use a SystemC-based actor model of the HM test model encoder for the evaluation of each explored solution. The evaluation that is based on real measurements shows that our framework can automatically generate encoder solutions that save more than 60% of encoding time or 3% of decoding energy when accepting bitrate increases of around 3

Multiple description coding with randomly and uniformly offset quantizers - Meng, L.; Liang, Jie; Samarawickrama, U.; Zhao, Y.; Bai, H.; Kaup, André (IEEE Transactions on Image Processing, 2014)
In this paper, two multiple description coding schemes are developed, based on prediction-induced randomly offset quantizers and unequal-deadzone-induced near-uniformly offset quantizers, respectively. In both schemes, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. In the first method, due to predictive coding, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped. The optimal reconstruction is obtained by finding the intersection of all received bins. In the second method, joint dequantization is also used, but near-uniform offsets are created among different low-rate quantizers by quantizing the predictions and by employing unequal deadzones. By generalizing the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained for the first method, and a lower bound is obtained for the second method. The schemes are then applied to lapped transform-based multiple description image coding. The closed-form expressions enable the optimization of the lapped transform. An iterative algorithm is also developed to facilitate the optimization. Theoretical analyzes and image coding results show that both schemes achieve better performance than other methods in this category. © 1992-2012 IEEE.
Keywords: deadzone quantization; Multiple description coding; predictive coding; random quantization

Multiple description coding with randomly offset quantizers - Meng, L.; Liang, Jie; Samarawickrama, U.; Zhao, Y.; Bai, H.; Kaup, André (2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013, 2013)
A multiple description coding scheme based on prediction-induced randomly offset quantizers is proposed, where each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. Due to the prediction, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped with each others. The optimal reconstruction is obtained by finding the intersection of all received quantization bins. Using the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained. The proposed scheme is then applied to lapped transform-based multiple-description image coding, and an iterative optimization scheme is developed to find the optimal lapped transform. Experimental results show that the proposed scheme achieves better performance than other methods in this category. © 2013 IEE
Keywords: Multiple description coding; predictive coding; random quantization

Multiple LDPC decoding using bitplane correlation for Transform Domain Wyner-Ziv video coding - van Luong, Huynh; Huang, X.; Forchhammer, S. (2011)
Distributed video coding (DVC) is an emerging video coding paradigm for systems which fully or partly exploit the source statistics at the decoder to reduce the computational burden at the encoder. This paper considers a Low Density Parity Check (LDPC) based Transform Domain Wyner-Ziv (TDWZ) video codec. To improve the LDPC coding performance in the context of TDWZ, this paper proposes a Wyner-Ziv video codec using bitplane correlation through multiple parallel LDPC decoding. The proposed scheme utilizes inter bitplane correlation to enhance the bitplane decoding performance. Experimental results show that the proposed scheme reduces the bit rate up to 3.9% and improves the rate-distortion (RD) performance of TDW
LMS::1540
Keywords: Wyner-Ziv video coding; multiple decoders; bitplane correlation

Multiple Reference Picture Video Coding Using Polynomial Motion Models - Wiegand, Thomas; Steinbach, Eckehard; Stensrud, Axel; Girod, Bernd (Proc. SPIE Symposium on Visual Communications and Image Processing (VCIP'98), 1998)
LMS::320

Multiple selection approximation for improved spatio-temporal prediction in video coding - Seiler, Jürgen; Kaup, André (2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, 2010)
In this contribution, a novel spatio-temporal prediction algorithm for video coding is introduced. This algorithm exploits temporal as well as spatial redundancies for effectively predicting the signal to be encoded. To achieve this, the algorithm operates in two stages. Initially, motion compensated prediction is applied on the block being encoded. Afterwards this preliminary temporal prediction is refined by forming a joint model of the initial predictor and the spatially adjacent already transmitted blocks. The novel algorithm is able to outperform earlier refinement algorithms in speed and prediction quality. Compared to pure motion compensated prediction, the mean data rate can be reduced by up to 15% and up to 1.16 dB gain in PSNR can be achieved for the considered sequences. ©2010 IEE
Keywords: Prediction; Signal extrapolation; Video coding

Multiple Selection Extrapolation for Spatial Error Concealment - Seiler, Jürgen; Kaup, André (2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09, 2009)
This contribution introduces a novel signal extrapolation algorithm and its application to image error concealment. The signal extrapolation is carried out by iteratively generating a model of the signal suffering from distortion. Thereby, the model results from a weighted superposition of two-dimensional basis functions whereas in every iteration step a set of these is selected and the approximation residual is projected onto the subspace they span. The algorithm is an improvement to the Frequency Selective Extrapolation that has proven to be an effective method for concealing lost or distorted image regions. Compared to this algorithm, the novel algorithm is able to reduce the processing time by a factor larger than three, by still preserving the very high extrapolation quality. © 2009 IEE

Multirate Implementation of Aliasing-free Adaptive Volterra Filters by Interpolation of Higher-Order Kernel Inputs - Zeller, Marcus; Kellermann, Walter (ITG-Fachtagung Sprachkommunikation, 2010)
This paper discusses a novel aliasing-free implementation of adaptive Volterra filters. Since any above-Nyquist frequencies generated by a continuous-time nonlinearity are typically removed by the anti-alising filter in the A/D conversion, the nonlinear system cannot be fully identified by a discrete-time model operating at the input rate. Therefore, we propose a multirate structure of adaptive Volterra filters employing interpolated higher-order kernel inputs so as to low-pass filter the corresponding outputs and retain only baseband contributions just as in the analog domain. In particular, the implications on the Volterra kernel sizes as well as an efficient polyphase structure are discussed. The conventional and the multirate implementation are compared for a nonlinear acoustic echo cancellation scenario with regard to convergence behaviour and implementation issue

Multirate simulations of string vibrations including nonlinear fret-string interactions using the functional transformation method - Trautmann, Lutz; Rabenstein, Rudolf (EURASIP J APPL SIG P, 2004)
The functional transformation method (FTM) is a well-established mathematical method for accurate simulations of multidimensional physical systems from various fields of science, including optics, heat and mass transfer, electrical engineering, and acoustics. This paper applies the FTM to real-time simulations of transversal vibrating strings. First, a physical model of a transversal vibrating lossy and dispersive string is derived. Afterwards, this model is solved with the FTM for two cases: the ideally linearly vibrating string and the string interacting nonlinearly with the frets. It is shown that accurate and stable simulations can be achieved with the discretization of the continuous solution at audio rate. Both simulations can also be performed with a multirate approach with only minor degradations of the simulation accuracy but with preservation of stability. This saves almost 80% of the computational cost for the simulation of a six-string guitar and therefore it is in the range of the computational cost for digital waveguide simulations.
Keywords: Functional transformation; Multidimensional system; Multirate approach; Non-linear; Partial differential equation; Vibrating string

Multiresolution Coding of Image and Video Signals - Girod, Bernd; Hartung, Frank; Horn, Uwe (Proceedings EUSIPCO 1998, 1998)
LMS::318

Multiresolution point cloud compression for real-time visualization and streaming of large 3D datasets - Nguyen, Dat Thanh; Zieger, Daniel; Gambietz, Markus; Koelewijn, Anne; Stamminger, Marc; Kaup, André (Asilomar Conferense on Signals, Systems, and Computers, 2024)

Multiscale Augmented Normalizing Flows for Image Compression - Windsheimer, Marc; Brand, Fabian; Kaup, André (International Conference on Acoustics, Speech and Signal Processing, 2024)
Most learning-based image compression methods lack efficiency for high image quality due to their non-invertible design. The decoding function of the frequently applied compressive autoencoder architecture is only an approximated inverse of the encoding transform. This issue can be resolved by using invertible latent variable models, which allow a perfect reconstruction if no quantization is performed. Furthermore, many traditional image and video coders apply dynamic block partitioning to vary the compression of certain image regions depending on their content. Inspired by this approach, hierarchical latent spaces have been applied to learning-based compression networks. In this paper, we present a novel concept, which adapts the hierarchical latent space for augmented normalizing flows, an invertible latent variable model. Our best performing model achieves significant rate savings of more than 7% over comparable single-scale models.<br

Multiscale deep context modeling for lossless point cloud geometry compression - Nguyen, Dat Thanh; Quach, Maurice; Valenzise, Giuseppe; Duhamel, Pierre (2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2021)
We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MPEG G-PCC codec. Our previous work based on autoregressive models (VoxelDNN [1]) has a fast training phase, however, inference is slow as the occupancy probabilities are predicted sequentially, voxel by voxel. In this work, we employ a multiscale architecture which models voxel occupancy in coarse-to-fine order. At each scale, MSVoxelDNN divides voxels into eight conditionally independent groups, thus requiring a single network evaluation per group instead of one per voxel. We evaluate the performance of MSVoxelDNN on a set of point clouds from Microsoft Voxelized Upper Bodies (MVUB) and MPEG, showing that the current method speeds up encoding/decoding times significantly compared to the previous VoxelDNN, while having average rate saving over G-PCC of 17.5%. The implementation is available at https://github.com/Weafre/MSVoxelDNN.<br

Multi-shelf graphic equalizer - Schlecht, Sebastian; Sinjanakhom, Tantep; Välimäki, Vesa (Signal Processing, 2025)
A graphic equalizer (GEQ) is a standard tool in audio production and effect design. Adjustable gain control frequencies are fixed along the logarithmic frequency axis, and an automatic design method matches the magnitude response to them whenever target gains are changed. Most commonly, the GEQ comprises a set of peak filters centered an octave apart, possibly with a shelving filter at the bottom and top of the frequency range. While accurate designs were proposed, the dynamic range is typically limited to 24 dB. In this paper, we propose two innovations. First, we introduce a GEQ based on shelving filters only, which can cover an extensive dynamic range of over 60 dB. Secondly, we introduce an order-switching technique that combines shelf filters of different order. We demonstrate the performance and advantages of the proposed filter with design examples. The proposed shelf-filter-based GEQ offers a wider dynamic range and a smoother magnitude response than traditional peak-filter-based GEQ designs.
Created from Fastlane, Scopus look-up
Keywords: Audio systems; Digital filters; Filtering algorithms; IIR filters; Optimization

Multispectral Image Compression Based on HEVC Using Pel-Recursive Inter-Band Prediction - Meyer, Anna; Genser, Nils; Kaup, André (IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), 2020)
Recent developments in optical sensors enable a wide range of applications for multispectral imaging, e.g., in surveillance, optical sorting, and life-science instrumentation. Increasing spatial and spectral resolution allows creating higher quality products, however, it poses challenges in handling such large amounts of data. Consequently, specialized compression techniques for multispectral images are required. High Efficiency Video Coding (HEVC) is known to be the state of the art in efficiency for both video coding and still image coding. In this paper, we propose a cross-spectral compression scheme for efficiently coding multispectral data based on HEVC. Extending intra picture prediction by a novel inter-band predictor, spectral as well as spatial redundancies can be effectively exploited. Dependencies among the current band and further spectral references are considered jointly by adaptive linear regression modeling. The proposed backward prediction scheme does not require additional side information for decoding. We show that our novel approach is able to outperform state-of-the-art lossy compression techniques in terms of rate-distortion performance. On different data sets, average Bjøntegaard delta rate savings of 82 % and 55 % compared to HEVC and a reference method from literature are achieved, respectivel

Multi-Style Reverberation Models and Efficient Model Adaptation for Robust Distant-Talking Speech Recognition with REMOS - Maas, Roland; Sehr, Armin; Kellermann, Walter (ITG Conference on Speech Communication, 2010)
To further increase the flexibility of the REMOS (REverberation MOdeling for Speech recognition) concept for distant-talking speech recognition, multi-style reverberation models (RVMs) trained on data from different rooms as well as simplified RVMs are analyzed in this contribution. If the multi-style probability density functions (pdfs) used for score calculation are adapted to the reverberation conditions of the current room, a remarkable improvement in recognition performance can be achieved. Evaluations of a very efficient reverberation model adaptation scheme by connected digit recognition experiments show that REMOS can be adjusted to different reverberation conditions with minimal effor

Multi-style training of HMMS with stereo data for reverberation-robust speech recognition - Sehr, Armin; Hofmann, Christian; Maas, Roland; Kellermann, Walter (2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays, HSCMA'11, 2011)
A novel training algorithm using data pairs of clean and reverberant feature vectors for estimating robust Hidden Markov Models (HMMs), introduced in [1] for matched training, is employed in this paper for multi-style training. The multi-style HMMs are derived from well-trained clean-speech HMMs by aligning the clean data to the clean-speech HMM and using the resulting state-frame alignment to estimate the Gaussian mixture densities from the reverberant data of several different rooms. Thus, the temporal alignment is fixed for all reverberation conditions contained in the multi-style training set so that the model mismatch between the different rooms is reduced. Therefore, this training approach is particularly suitable for multi-style training. Multi-style HMMs trained by the proposed approach and adapted to the current room condition using maximum likelihood linear regression significantly outperform the corresponding adapted multi-style HMMs trained by the conventional Baum-Welch algorithm. In strongly reverberant rooms, the proposed adapted multi-style HMMs even outper-form Baum-Welch HMMs trained on matched data. © 2011 IEE
Keywords: distant-talking speech recognition; Multi-style HMMtraining; reverberation; robust ASR; stereo data

Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs - Ülger, Osman; Wiederer, Julian; Ghafoorian, Mohsen; Belagiannis, Vasileios; Mettes, Pascal (BMVC 2022 - 33rd British Machine Vision Conference Proceedings, 2022)
Graph neural networks have shown to learn effective node representations, enabling node-, link-, and graph-level inference. Conventional graph networks assume static relations between nodes, while relations between entities in a video often evolve over time, with nodes entering and exiting dynamically. In such temporally-dynamic graphs, a core problem is inferring the future state of spatio-temporal edges, which can constitute multiple types of relations. To address this problem, we propose MTD-GNN, a graph network for predicting temporally-dynamic edges for multiple types of relations. We propose a factorized spatio-temporal graph attention layer to learn dynamic node representations and present a multi-task edge prediction loss that models multiple relations simultaneously. The proposed architecture operates on top of scene graphs that we obtain from videos through object detection and spatio-temporal linking. Experimental evaluations on ActionGenome and CLEVRER show that modeling multiple relations in our temporally-dynamic graph network can be mutually beneficial, outperforming existing static and spatio-temporal graph neural networks, as well as state-of-the-art predicate classification methods. Code is available at https://github.com/ozzyou/MTD-GNN.
CRIS-Team Scopus Importer:2024-12-16

Multitask Learning for SAR Ship Detection with Gaussian-Mask Joint Segmentation - Zhao, Ming; Zhang, Xin; Kaup, André (IEEE Transactions on Geoscience and Remote Sensing, 2023)
Detecting ships from synthetic aperture radar (SAR) images is inherently subject to the limitations of SAR's imaging mechanism. SAR object detection technology has rapidly advanced in recent years due to deep-learning-based techniques for detecting objects from optical images. SAR ship detection still faces some challenges due to the strong speckle noise, complex surroundings, and a variety of scales. This article proposes a multitask learning framework for object detection (MLDet) detect ships in SAR images. The proposed end-to-end framework consists of the object detection task, speckle suppression task, and target segmentation task. First, an angle classification loss with aspect ratio weighting (ARW) is explored during object detection to improve the accuracy by making the detector sensitive to the periodicity of angular and the aspect ratio of objects. Second, the speckle suppression task uses a dual-feature fusion attention (DFA) mechanism to suppress noisy background information and fuse shallow features and denoising features, which helps MLDet be more robust to speckle noise. Third, the target segmentation task with rotated Gaussian mask is explored to further help the detection network to extract the regions of intersect from the cluttered background, as well as improving the detection efficiency through pixel-by-pixel prediction. The rotated Gaussian mask for ship modeling ensures that the center of a ship has the highest probabilities to be labeled as an object, and the probabilities of the remaining regions are gradually reduced under a Gaussian distribution. Assisted by these two subtasks, the shallow-level features are robust to speckle noise and reliably support deep-level feature learning. In addition, the weighted rotated boxes' fusion (WRBF) strategy is adopted to combine the predictions of multidirection anchors for rotated objects and eliminate the anchors beyond the boundary and the anchors with high overlap rates but low scores. A large number of experiments and comprehensive evaluations on the SAR ship detection datasets SSDD+ and HRSID have shown the effectiveness and superiority of the proposed method.
CRIS-Team Scopus Importer:2023-09-22
Keywords: Multitask learning; ship detection; synthetic aperture radar (SAR)

Multiuser Scheduling for Simultaneous Wireless Information and Power Transfer Systems - Chynonova, Maryna (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)

Multiview Super-Resolution Using High-Frequency Synthesis in Case of Temporally Subsampled Depth Information - Richter, Thomas; Kaup, André (2012 IEEE Visual Communications and Image Processing, VCIP 2012, 2012)
Increasing the image sharpness of low-resolution views is a key issue in the multiview image and video processing domain. Thereby, a low-resolution view gets refined by high-frequency content that can either be obtained from temporally or spatially adjacent highresolution reference images. We propose a refined super-resolution algorithm for multiview images that is robust to the usage of temporally highly misaligned depth maps. The temporal misalignment may be caused either by a temporal subsampling or a lower framerate of the depth camera with respect to the image cameras. Our refinement step is based on a blockwise low-frequency registration in order to efficiently adapt the high-frequency content of the highresolution reference to the low-resolution destination view. The simulation results show that our proposed algorithm leads to a peak PSNR gain of up to 1.21 dB with respect to a comparable unrefined super-resolution approach. On average, our approach outperforms the unrefined super-resolution algorithm by 0.61 dB. For all considered scenarios the improvement of visual quality is also convincingly. © 2012 IEE
Keywords: displacement compensation; mixed-resolution (MR); multiview; superresolution (SR)

Multizone sound field synthesis based on the joint optimization of the sound pressure and particle velocity vector on closed contours - Bürger, Michael; Maas, Roland; Löllmann, Heinrich; Kellermann, Walter (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2015)
In this paper, we propose a novel approach to multizone sound reproduction, which is motivated by the Kirchhoff-Helmholtz integral equation and aims at the simultaneous optimization of the sound pressure and particle velocity vector on contours around multiple local listening areas. The benefit of this approach is that the control points do not need to be distributed within the local listening areas, as is typically the case for previous multipoint approaches, but on surrounding contours only. This is especially desirable when it comes to practical realizations using real microphones as control points. A closed-form solution is presented for determining the loudspeaker weights in the frequency domain, and the effectiveness of the proposed method, which we refer to as Joint Pressure and Velocity Matching (JPVM), is validated by simulation result
Keywords: Multizone Sound; Particle Velocity Vector; Pressure Matching; Reproduction; Sound Field Synthesis

Multizone sound reproduction in reverberant environments using an iterative least-squares filter design method with a spatiotemporal weighting function - Bürger, Michael; Hofmann, Christian; Frankenbach, Cornelius; Kellermann, Walter (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017)
A previously proposed iterative filter design procedure, referred to as Iterative DFT-domain Inversion (IDI), is applied in the context of multizone sound reproduction in reverberant environments. The IDI approach aims at iteratively solving a least-squares problem, where the true reproduction error in the time domain is considered rather than narrowband errors in the frequency domain. In this paper, a spatiotemporal weighting function, which can be tailored to the problem at hand, is applied to the reproduction error in order to flexibly control the behavior of the designed rendering system, where a constraint is imposed on the energy of the impulse responses of the loudspeaker prefilters. The efficacy of the proposed spatiotemporal weighting is verified experimentally. In particular, it is shown that the acoustic contrast between the local listening areas can be significantly increased by allowing for a certain amount of reverberation in the bright zone. At the same time, the accuracy of the first wave front synthesized in the bright zone remains unaffected. Furthermore, undesired pre-echoes—which occur prior to the desired wave front and may impair the perceived location of the synthesized source, but also the crispness of impulsive sounds—can be reduced
Keywords: Inverse Filtering; Personal Audio; Multizone Sound; Weighted Least-Squares; Pressure Matching

"Natuerliche" Akustische Mensch/Maschine-Schnittstellen - Eine Herausforderung fuer die digitale Signalverarbeitung (plenary speech) - Kellermann, Walter (Conf. Rec. 33. Jahrestagung fuer Akustik (DAGA 2007), 2007)
LMS::724

Near-lossless compression of computed tomography images using predictive coding with distortion optimization - Weinlich, Andreas; Amon, P.; Hutter, Andreas; Kaup, André (SPIE Medical Imaging 2013: Image Processing, 2013)
This paper presents a method for iterative minimization of combined residual and prediction error for near-lossless compression of medical computed tomography acquisitions using pixel-wise least-squares prediction. While most other lossy state-of-the-art image compression systems like JPEG 2000 make use of transform-based coding, in lossless coding higher compression ratios can be achieved with plain predictive algorithms like JPEG-LS because of their non-linear data adaptive energy reduction. Yet, applying these algorithms in lossy coding, simple quantization usually leads to error propagation and therefore serious quality loss or rate increase, as prediction accuracy of a pixel value and thus data rate depends on the previously reconstructed image region. The proposed minimization approach modifies the original image to be coded in a way such that the edge-directed prediction method from literature may achieve better predictions while introducing only a minimum amount of distortion. Compared to transform-based coding methods, the distortion introduced by the proposed scheme mostly consists in noise reduction instead of blurring or the introduction of artificial structures. The method also prevents error propagation due to the consideration of all pixel dependencies of the prediction. It is shown that, combined with a context-adaptive arithmetic coder, in high-fidelity coding (i. e., PSNR higher than 55 dB) the proposed method can achieve higher compression ratios than the transform-based approaches JPEG 2000, H.264/AVC, and HEVC intra coding. © 2013 SPI

Network-Aware Optimal Microphone Channel Selection in Wireless Acoustic Sensor Networks - Günther, Michael; Afifi, Haitham; Brendel, Andreas; Karl, Holger; Kellermann, Walter (2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021)

Neural Networks Sequential Training Using Variational Gaussian Particle Filter - Halimeh, Mhd Modar; Brendel, Andreas; Kellermann, Walter (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019)
In this paper, we propose a sequential training algorithm for feed-forward neural networks based on particle filtering. The proposed algorithm uses variational learning to tailor a proposal density by minimizing the variational energy. This density is then incorporated into the Gaussian particle filter framework. The proposed algorithm and an extension to it using evolutionary resampling are compared to training a neural network using a random walk-based particle filter, an extended Kalman filter, the use of variational learning only, and the backpropagation algorithm, using a synthetic dataset generated by a time-varying random process and a real dataset, where the proposed approach resulted in a moderately lower training and testing errors and a better convergence behavior, rendering the algorithm attractive for uses such as neural networks pre-trainin
Keywords: Particle filter; Bayesian estimation; neural networks; learning; online estimation

New, efficient Fourier-reconstruction method for approximate image reconstruction in spiral cone-beam CT at small cone angles - Schaller, Stefan; Flohr, T.; Steffen, Peter (SPIE Medical Imaging, 1997)
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