Context Adaptive Neural Network Based Acoustic Models for Rapid Adaptation - Delcroix, Marc; Kinoshita, Keisuke; Ogawa, Atsunori; Hümmer, Christian; Nakatani, Tomohiro (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2018)

Contributions to Visual Automotive Human Machine Interface Testing - Pöllot, Martin (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
The amount of driver assistance, entertainment, and information features in automobiles is increasing rapidly over the past few years. Human machine interfaces are the means to communicate between car and passengers. With the regard of automated testing of these interfaces, a typical scenario in the development of automotive infotainment systems is revealed. Conventional approaches for testing, based on empirical thresholds, however, are not able to provide reliable detection rates and require a lot of maintenance. This is where model-based testing approaches come into play. By applying model-based approaches, the acquisition of empirical thresholds is abandoned in favor of models learned by algorithms. Relying on an underlying model, the algorithms exhibit higher capabilities in detecting errors that approaches based on empirical thresholds are not taking into account. The advent of powerful machine learning approaches plays an important role in the data-based detection of errors in infotainment systems. Conventional approaches require prior knowledge of errors in order to develop custom-made algorithms for their detections. As many errors pass the development stage without being noticed due to never being observed during this time, a novel approach for automated testing human machine interfaces is advised. This thesis concentrates on automated testing in the context of a model-based end-to-end approach. After introducing the main approaches and components for evaluating and setting up an automated testing environment, a first component for interacting with the infotainment system is provided, which could bring human-like input patterns forward. The main part of this thesis focuses on the detection of irregular motion in the navigation context. By applying a model-based approach that learns the normal behavior of the system, irregularities could be found with relative ease in comparison to stale and fixed threshold-based approaches. Furthermore, in the domain of screen content verification, a deep neural network approach is dedicated to detect and classify icons in a given screen. This approach could speed up the state-of-the-art sliding window pattern matching approach and also be more robust to design changes and noise. All contributions provide extensive experiments on custom data sets and demonstrate the advantage over conventional methods. Three model-based contributions and two prototypes for screen content testing are investigated and adapted to provide improved results over threshold-based approaches. The applications comprise screen input testing, screen content analysis and screen content verification. All methods are superior to their respective reference methods in terms of detection rates, execution time and required maintenance, thus providing benefit in automated test environments. <

Convolutive Blind Source Separation for Noisy Mixtures - Aichner, Robert; Buchner, Herbert; Kellermann, Walter (Proc. of Joint Meeting of the German and the French Acoustical Societies (CFA/DAGA 2004), 2004)
LMS::485

Convolutive Blind Source Separation for NoisyMixtures - Aichner, Robert; Buchner, Herbert; Kellermann, Walter (Speech and Audio Processing in Adverse Environments, 2008)

Copyright Protection in Video Delivery Networks by Watermarking of Pre-Compressed Video - Hartung, Frank; Girod, Bernd (Multimedia Applications, Services and Techniques - ECMAST '97, Springer Lecture Notes in Computer Science, 1997)

Cost-effective multi-camera array for high quality video with very high dynamic range - Keinert, Joachim; Wetzel, Marcus; Schöberl, Michael; Schäfer, Peter; Zilly, Frederik; Bätz, Michel; Foessel, Siegfried; Kaup, André (Digital Photography X, 2014)
Temporal bracketing can create images with higher dynamic range than the underlying sensor. Unfortunately, moving objects cause disturbing artifacts. Moreover, the combination with high frame rates is almost unachievâ-able since a single video frame requires multiple sensor readouts. The combination of multiple synchronized side-by-side cameras equipped with different attenuation filters promises a remedy, since all exposures can be performed at the same time with the same duration using the playout video frame rate. However, a disparity correction is needed to compensate the spatial displacement of the cameras. Unfortunately, the requirements for a high quality disparity correction contradict the goal to increase dynamic range. When using two cameras, disparity correction needs objects to be properly exposed in both cameras. In contrast, a dynamic range inâ-crease needs the cameras to capture different luminance ranges. As this contradiction has not been addressed in literature so far, this paper proposes a novel solution based on a three camera setup. It enables accurate deâ-termination of the disparities and an increase of the dynamic range by nearly a factor of two while still limiting costs. Compared to a two camera solution, the mean opinion score (MOS) is improved by 13.47 units in average for the Middleburry images. © 2014 SPIE-IS and
Keywords: camera array; correlation; disparity correction; ghosting artifacts; High dynamic range; occlusions

Cross-Modal Pre-Aligned Method with Global and Local Information for Remote-Sensing Image and Text Retrieval - Sun, Zengbao; Zhao, Ming; Liu, Gaorui; Kaup, André (IEEE Transactions on Geoscience and Remote Sensing, 2024)
In recent years, remote sensing cross-modal text-image retrieval (RSCTIR) has attracted considerable attention owing to its convenience and information mining capabilities. However, two significant challenges persist: effectively integrating global and local information during feature extraction due to substantial variations in remote sensing imagery, and the failure of existing methods to adequately consider feature pre-alignment prior to modal fusion, resulting in complex modal interactions that adversely impact retrieval accuracy and efficiency. To address these challenges, we propose a cross-modal pre-aligned method with global and local information (CMPAGL) for remote sensing imagery. Specifically, we design the Gswin transformer block, which introduces a global information window on top of the local window attention mechanism, synergistically combining local window self-attention and global-local window cross-attention to effectively capture multi-scale features of remote sensing images. Additionally, our approach incorporates a pre-alignment mechanism to mitigate the training difficulty of modal fusion, thereby enhancing retrieval accuracy. Moreover, we propose a similarity matrix reweighting (SMR) reranking algorithm to deeply exploit information from the similarity matrix during retrieval process. This algorithm combines forward and backward ranking, extreme difference ratio, and other factors to reweight the similarity matrix, thereby further enhancing retrieval accuracy. Finally, we optimize the triplet loss function by introducing an intra-class distance term for matched image-text pairs, not only focusing on the relative distance between matched and unmatched pairs but also minimizing the distance within matched pairs. Experiments on four public remote sensing text-image datasets, including RSICD, RSITMD, UCM-Captions, and Sydney-Captions, demonstrate the effectiveness of our proposed method, achieving improvements over state-of-the-art methods, such as a 2.28% increase in mean Recall (mR) on the RSITMD dataset and a significant 4.65% improvement in R@1. The code is available from https://github.com/ZbaoSun/CMPAGL.
CRIS-Team Scopus Importer:2024-11-15
Keywords: global and local information; optimized triplet loss; pre-alignment; remote sensing cross-modal text-image retrieval (RSCTIR); similarity matrix reweighting(SMR) reranking

Cross Spectral Image Reconstruction Using a Deep Guided Neural Network - Sippel, Frank; Seiler, Jürgen; Kaup, André (2023 IEEE International Conference on Image Processing (ICIP), 2023)

Cycle Slips in the PLL Synchronizer with Limiter Phase Detector in the Presence of Noise and Cochannel Interference - Hasan, Pavel (Proceedings 4th IEEE International Conference on Electronics, Circuits, and Systems, 1997)
LMS::408

Das Montgomery-Interferometer als optisches Zeitfilter - Knuppertz, H.; Jahns, J.; Lohmann, Adolf (105. Jahrestagung der DGaO, Bad Kreuznach, 2004)
LMS::661

Data Compression for Close-Range Radar Imaging - Rückert, Rainer; Ullmann, Ingrid; Vossiek, Martin; Kaup, André; Herglotz, Christian (IEEE Transactions on Radar Systems, 2024)

Data-driven 3-D Rigid Body Motion and Structure Estimation - Steinbach, Eckehard (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2000)

Data-driven Multi-frame 3D Motion Estimation - Steinbach, Eckehard; Chaudhuri, S.; Girod, Bernd (International Conference on Image Processing (ICIP 97), 1997)
LMS::419

DATA-FREE BACKBONE FINE-TUNING FOR PRUNED NEURAL NETWORKS - Holzbock, Adrian; Hegde, Achyut; Dietmayer, Klaus; Belagiannis, Vasileios (European Signal Processing Conference, 2023)
Model compression techniques reduce the computational load and memory consumption of deep neural networks. After the compression operation, e.g. parameter pruning, the model is normally fine-tuned on the original training dataset to recover from the performance drop caused by compression. However, the training data is not always available due to privacy issues or other factors. In this work, we present a data-free fine-tuning approach for pruning the backbone of deep neural networks. In particular, the pruned network backbone is trained with synthetically generated images, and our proposed intermediate supervision to mimic the unpruned backbone's output feature map. Afterwards, the pruned backbone can be combined with the original network head to make predictions. We generate synthetic images by back-propagating gradients to noise images while relying on L1-pruning for the backbone pruning. In our experiments, we show that our approach is task-independent due to pruning only the backbone. By evaluating our approach on 2D human pose estimation, object detection, and image classification, we demonstrate promising performance compared to the unpruned model. Our code is available at https://github.com/holzbock/dfbf.
CRIS-Team Scopus Importer:2023-12-15
Keywords: Data-Free Neural Network Pruning; Data-Free Object Detection Pruning; Data-Free Pose Estimation Pruning

Data Science Education: The Signal Processing Perspective - Gannot, S.; Tan, Zheng-Hua; Haardt, Martin; Chen, Nancy F.; Wai, Hoi-To; Tashev, Ivan; Kellermann, Walter; Dauwels, Justin (IEEE Signal Processing Magazine, 2023)
In the last decade, the signal processing (SP) community has witnessed a paradigm shift from model-based to data-driven methods. Machine learning (ML)—more specifically, deep learning—methodologies are nowadays widely used in all SP fields, e.g., audio, speech, image, video, multimedia, and multimodal/multisensor processing, to name a few. Many data-driven methods also incorporate domain knowledge to improve problem modeling, especially when computational burden, training data scarceness, and memory size are important constraint

Declipping of Speech Signals Using Frequency Selective Extrapolation - Jonscher, Markus; Seiler, Jürgen; Kaup, André (11. ITG Conference on Speech Communication, 2014)
The reconstruction of clipped speech signals is an important task in audio signal processing to achieve an enhanced audio quality for further processing. In this paper, Frequency Selective Extrapolation (FSE), which is commonly used for error concealment or the reconstruction of incomplete image data, is adapted to be able to restore audio signals which are distorted from clipping. For this, FSE generates a model of the signal as an iterative superposition of Fourier basis functions. Clipped samples can then be replaced by estimated samples from the model. The performance of the proposed algorithm is evaluated by using different speech test data sets. Compared to other stateof- the-art declipping algorithms, this leads to a maximum gain in SNR of up to 3.5 dB and an average gain of 1 d

Decoding Energy Estimation of an HEVC Hardware Decoder - Herglotz, Christian; Kaup, André (IEEE International Symposium on Circuits and Systems (ISCAS), 2018)

Decoding Energy Modeling for the Next Generation Video Codec Based on JEM - Herglotz, Christian; Kränzler, Matthias; Kaup, André (Picture Coding Symposium (PCS), 2018)
This paper shows that the processing energy of the decoder software for the next generation video codec can be accurately estimated using a feature based model. Therefore, a model from the literature is taken and extended to account for a high amount of the newly introduced coding modes. It is shown that using a selected set of 60 features, for a large set of more than 800 coded bit streams, a mean estimation error below 5% can be reached. Using the trained parameters of the model, the energy consumption of the decoder can be analyzed in detail such that, e.g., the coding modes consuming most processing energy can be identified. The model can be used inside the encoder for decoding-energy-rate-distortion optimization to generate decoding energy saving bit streams.<br
Keywords: JEM; VVC; decoding energy; model

Decoding Energy Modeling for Versatile Video Coding - Kränzler, Matthias; Herglotz, Christian; Kaup, André (IEEE International Conference on Image Processing (ICIP), 2020)
In previous research, it was shown that the software decoding energy demand of High Efficiency Video Coding (HEVC) can be reduced by 15% by using a decoding-energy-rate-distortion optimization algorithm. To achieve this, the energy demand of the decoder has to be modeled by a bit stream feature-based model with sufficiently high accuracy. Therefore, we propose two bit stream feature-based models for the upcoming Versatile Video Coding (VVC) standard. The newly introduced models are compared with models from literature, which are used for HEVC. An evaluation of the proposed models reveals that the mean estimation error is similar to the results of the literature and yields an estimation error of 1.85% with 10-fold cross-validation.

Decoding Energy Optimal Video Encoding for x265 - Herglotz, Christian; Bader, Marco; Fischer, Kristian; Kaup, André (IEEE 22nd Workshop on Multimedia Signal Processing (MMSP), 2020)
This paper presents optimal x265-encoder configurations and an enhanced optimization algorithm for minimizing the software decoding energy of HEVC-coded videos. We reach this goal with two contributions. First, we perform a detailed analysis on the influence of various encoder settings on the decoding energy. Second, we include an enhanced version of an algorithm called decoding-energy-rate-distortion optimization into x265, which we optimize for fast and efficient encoding. This algorithm introduces the estimated decoding energy as an additional optimization criterion into the rate-distortion cost function. We evaluate the extended encoder in terms of bitrate, distortion, and decoding energy, where we perform energy measurements to prove the superior energy efficiency. We find that the combination of the ‘fastdecoding’ tuning option of x265 with the enhanced decoding-energy-rate-distortion optimization leads to 27.2% and 26.0% of decoding energy savings for OpenHEVC and HM decoding, respectively. At the same time, compression efficiency losses of 4.31% and negligible decreases in encoder runtime of 0.39% can be observed.<br
Keywords: x265; decoder energy; rate-distortion optimization; video

Decoding Energy Optimization for Video Coding Using Model-Driven Gradient Descent - Herglotz, Christian; Kränzler, Matthias; Xu, Bide; Kaup, André (IEEE 26th International Workshop on Multimedia Signal Processing (MMSP), 2024)

Decoding-Energy-Rate-Distortion Optimization for Video Coding - Herglotz, Christian; Heindel, Andreas; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2019)
This paper presents a method for generating coded video bit streams requiring less decoding energy than conventionally coded bit streams. To this end, we propose extending the standard rate-distortion optimization approach to also consider the decoding energy. In the encoder, the decoding energy is estimated during runtime using a feature-based energy model. These energy estimates are then used to calculate decoding-energy-rate-distortion costs that are minimized by the encoder. This ultimately leads to optimal trade-offs between these three parameters. Therefore, we introduce the mathematical theory for describing decoding-energy-rate-distortion optimization and the proposed encoder algorithm is explained in detail. For rate-energy control, a new encoder parameter is introduced. Finally, measurements of the software decoding process for HEVC-coded bit streams are performed. Results show that this approach can lead to up to 30% of decoding energy reduction at a constant visual objective quality when accepting a bitrate increase at the same order of magnitud
Keywords: Codec; Decoding Energy; Rate-Distortion Optimization; Energy Model; HEVC

Deep Clustering for single-channel ego-noise suppression - Briegleb, Annika; Schmidt, Alexander; Kellermann, Walter (International Congress on Acoustics (ICA), 2019)
Keywords: Robot audition; Ego-noise; Deep Clustering

Deep Counting Model Extensions with Segmentation for Person Detection - Ghosh, Sanjukta; Amon, Peter; Hutter, Andreas; Kaup, André (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2019)
Applications like autonomous driving, surveillance, or any application that demands scene analysis requires object detection, semantic segmentation and instance segmentation. In this paper, we focus on the problem of detecting each instance of a specific category of objects, specifically persons. A novel method for object detection is proposed based on a deep counting model. The feature extractor of the deep counting model is extended with additional layers for segmenting specific instances. While the feature extractor of the deep counting model already focuses on the persons in the scene, the segmentation layers help to get a more accurate estimation of the foreground with persons and the instance segmentation is able to estimate separate instances of persons. Our proposed method outperforms other methods on the CUHK08 dataset with an Average Miss Rate (AMR) of 14% and on the PETS09 dataset with an AMR of 41%.
CRIS-Team Scopus Importer:2019-07-23
Keywords: Deep convolutional neural networks; deep counting models; detection; multi-task network; segmentation

Deep Learning Based Cross-Spectral Disparity Estimation For Stereo Imaging - Genser, Nils; Spruck, Andreas; Seiler, Jürgen; Kaup, André (2020 IEEE International Conference on Image Processing (ICIP), 2020)
Recently, cross-spectral stereo-camera setups found their way from special applications to mass market, especially in smartphones, automotive systems, or drones. In the following, a novel concept is introduced to bring stereo cameras and cross-spectral disparity estimation together. So far, either monomodal stereo algorithms exist that are not suitable for cross-spectral image registration, or structural template matching is applied that achieves a low quality. To overcome these limitations, a technique is proposed to synthesize arbitrary spectral components from widely available color stereo databases, and to retrain mono-modal deep learning methods. In this contribution, the estimation of spectral bands based on random processes is shown together with noise models, which also allow for a robust registration of narrowband components. The theoretical examination is completed by an extensive evaluation, including a self-manufactured cross-spectral camera setup. In comparison to state-of-the-art techniques, the end-point error is on average reduced by a factor of seve

Deep learning based F-Mask alternative for Sentinel-2 images in polar regions - Seehaus, Thorsten; Nambiar, Kamal Gopikrishnan; Morgenshtern, Veniamin; Hochreuther, Philipp; Braun, Matthias (EGU General Assembly, 2021)
Screening clouds, cloud shadows, and snow is a critical pre-processing step that needs to be performed before any meaningful analysis can be done on satellite image data. The state of the art 'F-Mask' algorithm, which is based on multiple pixel-level threshold tests, segments the image into clear land, cloud, cloud shadow, snow, and water classes. However, we observe that the results of this algorithm are not very accurate in polar and tundra regions. The unavailability of labeled Sentinel-2 training datasets with these classes makes the traditional supervised machine learning techniques difficult to implement. Experiments with large, noisy training data on standard deep learning classification tasks like CIFAR-10 and ImageNet have shown neural networks learn clean labels faster than noisy labels. We present a multi-level self-learning approach that trains a model to perform semantic segmentation on Sentinel-2 L1C images. We use a large dataset with labels annotated using the F-mask algorithm for the training, and a small human-labeled dataset for validation. The validation dataset contains numerous examples where the F-mask classification would have given incorrect labels. At the first step, a deep neural network with a modified U-Net architecture is trained using a dataset automatically labeled with the F-mask algorithm. The performance on the validation dataset is used to select the best model from the step, which would then be used to generate more training labels from previously unseen data. In each of the subsequent steps, a new model is trained using the labels generated using the model from the previous step. The amount of data used for training increases with each step and the application of techniques like data augmentation and dropout improves the generalization of the trained model. We show that the final model from our approach can outperform its teacher, i.e. F-Mask algorithm.

Deep Learning-Based Joint Control of Acoustic Echo Cancellation, Beamforming and Postfiltering - Haubner, Thomas; Kellermann, Walter (European Signal Processing Conference (EUSIPCO), 2022)

Deep learning based windthrow detection for winter storms - Jaiswal, Nishant; Bucher, Tilman; Seiler, Jürgen; Beckschäfer, Philip; Bhattacharjee, Protim (Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV Konferenz, 2022)

Deep Network Pruning for Object Detection - Ghosh, Sanjukta; Srinivasa, Shashi K.K.; Amon, Peter; Hutter, Andreas; Kaup, André (Proceedings - International Conference on Image Processing, ICIP, 2019)
With the increasing success of deep learning in various applications, there is an increasing need to have deep models that can be used for deployment in real-time and/or resource constrained scenarios. In this context, this paper analyzes the pruning of deep models for object detection in order to reduce the number of weights and hence the number of computations. Very deep networks based on ResNet like architectures, like YOLOv3 have unique challenges when attempting to prune them. This paper proposes a network pruning technique based on agglomerative clustering for the feature extractor and using mutual information for the detector. The performance of the proposed techniques is also compared with that of a relatively shallow network, i.e., YOLOv2. A compression percentage of around 30% results in a 10% drop of mean average precision (mAP) in YOLOv3, whereas in YOLOv2 the drop was around 6% on the COCO dataset.
CRIS-Team Scopus Importer:2019-12-31
Keywords: Clustering; CNN; Deep Learning; Network Pruning; Object Detection

Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Network - Gburrek, Tobias; Schmalenströer, Jörg; Brendel, Andreas; Kellermann, Walter; Haeb-Umbach, Reinhold (European Signal Processing Conference (EUSIPCO), 2020)

DEEP PROBABILISTIC MODEL FOR LOSSLESS SCALABLE POINT CLOUD ATTRIBUTE COMPRESSION - Nguyen, Dat Thanh; Nambiar, Kamal Gopikrishnan; Kaup, André (International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2023, 2023)
In this work, we build an end-to-end multiscale point cloud attribute coding method (MNeT) that progressively projects the attributes onto multiscale latent spaces. The multiscale architecture provides an accurate context for the attribute probability modeling and thus minimizes the coding bitrate with a single network prediction. Besides, our method allows scalable coding that lower quality versions can be easily extracted from the losslessly compressed bitstream. We validate our method on a set of point clouds from MVUB and MPEG and show that our method outperforms recently proposed methods and on par with the latest G-PCC version 14. Besides, our coding time is substantially faster than G-PCC.<br
Keywords: MNeT; G-PCC; Point Cloud

Defect pixel interpolation for lossy compression of camera raw data - Schöberl, Michael; Keinert, J.; Seiler, Jürgen; Foessel, S.; Kaup, André (SPIE Electronic Imaging - Visual Information Processing and Communication II, 2012)
The image processing pipeline of a traditional digital camera is often limited by processing power. A better image quality could be generated only if more complexity was allowed. In a raw data workflow most algorithms are executed off-camera. This allows the use of more sophisticated algorithms for increasing image quality while reducing camera complexity. However, this requires a major change in the processing pipeline: a lossy compression of raw camera images might be used early in the pipeline. Subsequent off-camera algorithms then need to work on modified data. We analyzed this problem for the interpolation of defect pixels. We found that a lossy raw compression spreads the error from uncompensated defects over many pixels. This leads to a problem as this larger error cannot be compensated after compression. The use of high quality, high complexity algorithms in the camera is also not an option. We propose a solution to this problem: Inside the camera only a simple and low complexity defect pixel interpolation is used. This significantly reduces the compression error for neighbors of defects. We then perform a lossy raw compression and compensate for defects afterwards. The high complexity defect pixel interpolation can be used off-camera. This leads to a high image quality while keeping the camera complexity low.© 2012 SPIE-IS&
Keywords: Defect pixel interpolation; Image acquisition; Raw data compression

Deformable Modeling of Facial Tissue for Craniofacial Surgery Simulation - Keeve, Erwin; Girod, Bernd; Kikinis, R. (Computer Aided Surgery, 1998)
LMS::353

Deformable Templates for the Localization of Anatomical Stuctures in Radiologic Images - Sörgel, Wolfgang; Girod, Bernd (Proc. Workshop Bildverarbeitung für die Medizin, 1999)
LMS::302

Demonstration of Rapid Frequency Selective Reconstruction for Image Resolution Enhancement - Genser, Nils; Seiler, Jürgen; Jonscher, Markus; Kaup, André (IEEE International Conference on Image Processing (ICIP), 2017)
We propose to adapt the local statistics estimation, which has been designed for rapid error concealment originally. By merging the approaches, the reconstruction of images is fastened significantly. We will show in this demonstration that several frames per second (fps) can be processed using conventional computers and HD sized image dat

DENESTO: A Tool for Video Decoding Energy Estimation and Visualization - Kränzler, Matthias; Herglotz, Christian; Kaup, André (2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020)
In previous research, it is shown that the decoding energy demand of several video codecs can be estimated accurately by using bit stream feature-based models. Therefore, we show in this paper that the visualization with the Decoding Energy Estimation Tool (DENESTO) can help to improve the understanding of the energy demand of the decoder.<br
Keywords: HEVC; VVC; Energy; Analysis; Model

Denoising-based image reconstruction from pixels located at non-integer positions - Koloda, Jan; Seiler, Jürgen; Kaup, André (IEEE International Conference on Image Processing, ICIP 2015, 2015)
Digital images are commonly represented as regular 2D arrays, so pixels are organized in form of a matrix addressed by integers. However, there are many image processing operations, such as rotation or motion compensation, that produce pixels at non-integer positions. Typically, image reconstruction techniques cannot handle samples at non-integer positions. In this paper, we propose to use triangulation-based reconstruction as initial estimate that is later refined by a novel adaptive denoising framework. Simulations reveal that improvements of up to more than 1.8 dB (in terms of PSNR) are achieved with respect to the initial estimat
Keywords: adaptive denoising; Floating mesh; image reconstruction

Dense lightfield reconstruction from multi aperture cameras - Ziegler, M.; Zilly, F.; Schäfer, P.; Keinert, J.; Schöberl, Michael; Foessel, S. (IEEE International Conference on Image Processing (ICIP), 2014)
Plenoptic cameras based on micro lens arrays as well as multi aperture cameras are able to capture a multitude of images with slightly shifted viewpoints. Although the amount of parallax between adjacent views is limited, precautions have to be taken in order to avoid alias when performing direct lightfield rendering. Against this background, we present an approach for the dense reconstruction of a lightfield based on a sparse lightfield acquired from a multi aperture camera with subsequent disparity estimation and depth image based view interpolation. Results show that the approach is suitable for all-in-focus-renderin
LMS::1442
Keywords: lightfield; multi-aperture camera; plenoptic camera; view rendering

Depth map compression for unstructured lumigraph rendering - Fecker, Ulrich; Guenegues, A.; Scholz, I.; Kaup, André (Visual Communications and Image Processing (VCIP 2006), 2006)
Image-based rendering techniques require capturing an object or scene from many viewpoints. Often, depth maps are used in addition to the original images for rendering new views for novel viewpoints not coinciding with one of the original camera positions. Due to the high amount of data, efficient compression is necessary. When the data is stored or transmitted, it is not only desirable to compress the image data but also the depth information. In this paper, the case of sequences recorded with a single, hand-held camera is investigated. These sequences are used for unstructured lumigraph rendering. In addition, the case of multi-view video sequences is analyzed. For both cases, depth maps are compressed using H.264/AVC, and the achievable data rates are studied. For the case of unstructured lumigraph rendering, the effect of depth map compression on the quality of rendered images is analyzed in a second step.© SPIE<br
Keywords: image-based rendering; light field; lumigraph; depth map; image compression; video coding

Dereverberation and Robust Speech Recognition Using Spatial Coherence Models - Schwarz, Andreas (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
One of the challenges for far-field speech communication and recognition applications is that the acquired speech signal is impacted by reverberation and noise. It is therefore often required to apply signal processing techniques for dereverberation and noise reduction. Particularly effective are techniques which exploit spatial information about the sound field from multichannel microphone signals. One approach for modeling the spatial characteristics of reverberation and noise are spatial coherence functions. These are dependent only on acoustic properties which are relatively similar between different rooms, and require a minimum of assumptions about the acoustic scenario, which provides the motivation for focusing this thesis on signal enhancement approaches exploiting spatial coherence models. As a foundation, the applicability of different spatial coherence models to reverberation, and their dependency on acoustic properties of the room, are investigated. Existing methods for signal enhancement are reviewed, with a focus on spectral enhancement methods which use a short-time coherence estimate to estimate the power ratio between desired coherent and undesired diffuse sound field components. Known spectral enhancement methods are expressed in this framework, and novel estimators are proposed which have both theoretical and practical advantages over existing methods. Based on these estimators, an effective dereverberation system is proposed which can operate without knowledge of the position of the desired source, solely by exploiting the characteristic spatial coherence of reverberation. Furthermore, a more experimental dereverberation system is proposed which additionally accounts for the effect of early signal reflections in the room, showing that this approach can provide promising directions for future research. Finally, the problem of how to effectively use spatial information in an automatic speech recognizer based on a deep neural network acoustic model is investigated. A novel way of exploiting spatial information for reverberation-robust automatic speech recognition is proposed, where a spatial feature vector is extracted from short-time coherence estimates and then supplied as input to the neural network. It is shown that this approach can exceed the improvements that are obtained by the application of signal enhancement methods for dereverberatio
Keywords: Dereverberation; ASR; speech enhancement

Dereverberation using a model for the spatial coherence of decaying reverberant sound fields in rectangular rooms - Nees, Sam; Schwarz, Andreas; Kellermann, Walter (AES 60th Conference on Dereverberation and Reverberation of Audio, Music, and Speech (DREAMS), 2016)
In rooms with nonuniform absorption due to different material properties, the spatial properties of the decaying reverberant sound field were found to be time-variant, violating the common assumption of a diffuse sound field. Recently, we proposed a model for the coherence of such a sound field in a rectangular room, using knowledge of the geometry and surface reflection coefficients. In this paper, we describe a coherence-based dereverberation method based on this model. We conduct an evaluation with impulse responses which were generated for rooms with non-uniform absorption, confirming that the model can be applied for speech dereverberation

Der MPEG-4 Multimedia-Standard und seine Anwendungen im MINT-Projekt - Bauer, S.; Herpel, C.; Kaup, André; Ohm, J.-R.; Spille, J. (Der Fernmelde-Ingenieur, 1998)
LMS::474

Design and Implementation of a Scheduler for Processing Tasks in the LTE Receiver - Singh, Parminder (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)

Design of 2D FIR digital filters with flatness constraints for the error function - Rabenstein, Rudolf (Circuits Systems and Signal Processing, 1994)
A method will be presented for the approximation of a desired two-dimensional frequency response by the frequency response of a two-dimensional finite-impulse-response digital filter. It is possible to match the functional values and an arbitrary number of derivatives of both responses for zero frequency, thus making the error flat up to a desired degree. Remaining degrees of freedom are used for an L-approximation. Closed form design formulae will be given. © 1994 Birkhäuser.

Design of a molecular communication channel by modelling enzyme kinetics - Rabenstein, Rudolf (8th Vienna International Conference on Mathematical Modelling, MATHMOD 2015, 2015)
Molecular communication channels transmit information by the release and detection of chemical substances. Single information bits can be encoded as individual releases of certain amounts of molecules. Intersymbol interference between successive releases can be minimized by enforcing a decay of the substrate concentration through enzyme reactions. A model for the short term behaviour of the enzyme kinetics is employed to derive the achievable symbol transmission rate in terms of the parameters of the enzyme reaction and the sensitivity of the substrate receptor. The resulting relation facilitates the design of the biochemical environment for efficient molecular communicatio
Keywords: Chemical microsensors; Communication channels; Enzyme kinetics; Michaelis-Menten model; Models; Molecular communication

Design of FIR digital filters with flatness constraints for the error function - Rabenstein, Rudolf (Circuits Systems and Signal Processing, 1994)
A method will be presented for the approximation of a desired frequency response by the frequency response of a FIR filter. It is possible to match the functional values and an arbitrary number of derivatives of both responses for zero frequency, thus making the error flat up to a desired degree. Remaining degrees of freedom are used for a weighted L-approximation. Closed form design formulae will be given. © 1994 Birkhäuser.

Design of FIR Digital Filters with Prescribed Derivatives at Zero Frequency - Rabenstein, Rudolf (Int. Symposium on Signals, Systems and Electronics, 1989)
LMS::1590

Design of Robust Polynomial Beamformers as a Convex Optimization Problem - Mabande, Edwin; Kellermann, Walter (International Workshop on Acoustic Echo and Noise Control (IWAENC), 2010)
Polynomial broadband beamforming designs enable an easy, smooth, and dynamic steering of the main beam. If these designs aim at constant beamwidth they often lead to superdirective beamformers for low frequencies, if the array size is small relative to the wavelengths. Direct control of the robustness of these beamformers is required if they are to be applied in practice. In [1] we proposed a method which allows full control of the robustness of a data-independent superdirective beamformer design. This concept can be applied directly to polynomial broadband beamforming. In this paper, we present a method which incorporates constraints for the white noise gain into a least-squares polynomial beamformer design and still leads to a convex optimization problem that can be solved directly. The effectiveness of this method in controlling the robustness and providing good spatial selectivity is shown in design example

Design of robust polynomial beamformers for symmetric arrays - Mabande, Edwin; Bürger, Michael; Kellermann, Walter (IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012)
Polynomial broadband beamforming designs enable an easy, smooth, and dynamic steering of the main beam. A number of design methods based on constrained optimization have been proposed recently which allow for the control of the robustness of these designs. Of course, the addition of robustness constraints reduces the number of degrees of freedom of the design. In this paper, we present a method to enhance the spatial selectivity of the robust polynomial beamformer design by exploiting the structure of symmetric arrays while still satisfying the robustness constraints. The effectiveness of this method is shown in design examples for symmetric linear and circular arrays. © 2012 IEE
Keywords: Robust Polynomial Beamformer; Symmetric Arrays

Design of robust superdirective beamformers as a convex optimization problem - Mabande, Edwin; Schad, Adrian; Kellermann, Walter (2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, 2009)
Broadband data-independent beamforming designs aiming at constant beamwidth often lead to superdirective beamformers for low frequencies, if the sensor spacing is small relative to the wavelengths. Superdirective beamformers are extremely sensitive to spatially white noise and to small errors in the array characteristics. These errors are nearly uncorrelated from sensor to sensor and affect the beamformer in a manner similar to spatially white noise. Hence the White Noise Gain (WNG) is a commonly used measure for the robustness of beamformer designs. In this paper, we present a method which incorporates a constraint for the WNG into a least-squares beamformer design and still leads to a convex optimization problem that can be solved directly, e.g. by Sequential Quadratic Programming. The effectiveness of this method is demonstrated by design examples. ©2009 IEE
Keywords: Sequential quadratic programming; Superdirective beamformer; White noise gain

Design of robust two-dimensional polynomial beamformers as a convex optimization problem with application to robot audition - Barfuß, Hendrik; Bachmann, Markus; Bürger, Michael; Schneider, Martin; Kellermann, Walter (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017)
We propose a robust two-dimensional polynomial beamformer design method, formulated as a convex optimization problem, which allows for flexible steering of a previously proposed data-independent robust beamformer in both azimuth and elevation direction. As an exemplary application, the proposed two-dimensional polynomial beamformer design is applied to a twelve-element microphone array, integrated into the head of a humanoid robot. To account for the effects of the robot's head on the sound field, measured head-related transfer functions are integrated into the optimization problem as steering vectors. The two-dimensional polynomial beamformer design is evaluated using signal-independent and signal-dependent measures. The results confirm that the proposed polynomial beamformer design approximates the original fixed beamformer design very accurately, which makes it an attractive approach for robust real-time data-independent beamformin
Keywords: Robust superdirective beamforming; polynomial beamforming; white noise gain; robot audition

Design of Steerable Linear Differential Microphone Arrays With Omnidirectional and Bidirectional Sensors - Luo, Xueqin; Jin, Jilu; Huang, Gongping; Chen, Jingdong; Benesty, Jacob (IEEE Signal Processing Letters, 2023)
This paper is dedicated to the design of fully steerable linear differential microphone arrays (LDMAs). We analyze the steerable ideal spatial responses and explain why conventional LDMAs consisting of only omnidirectional microphones have limited steering ability. In order to circumvent this limitation, we suggest to use both omnidirectional and bidirectional (with a dipole shaped directivity pattern) microphones. We discuss the minimum numbers of omnidirectional and bidirectional sensors required for achieving steerable spatial responses and present a method to design fully steerable differential beamformers with LDMAs through the Jacobi-Anger series expansion. Simulations validate the presented technique and the steering flexibility of the designed LDMAs.
CRIS-Team Scopus Importer:2023-05-05
Keywords: Array signal processing; beam steering; differential beamforming; directivity factor; Image color analysis; Jacobian matrices; Linear microphone arrays; Microphone arrays; Sensor arrays; Sensors; White noise; white noise gain

Design Space Exploration at Frame-Level for Joint Decoding Energy and Quality Optimization in VVC - Stürzenhofäcker, Teresa; Kränzler, Matthias; Herglotz, Christian; Kaup, André (European Signal Processing Conference, 2024)
In the pursuit of a reduced energy demand of VVC decoders, it was found that the coding tool configuration has a substantial influence on the bit rate efficiency and the decoding energy demand. The Advanced Design Space Exploration algorithm as proposed in the literature, can derive coding tool configurations that provide optimal trade-offs between rate and energy efficiency. Yet, some trade-off points in the design space cannot be reached with the state-of-the-art methodology, which defines coding tools for an entire bitstream. This work proposes a novel, granular adjustment of the coding tool usage in VVC. Consequently, the optimization algorithm is adjusted to explore coding tool configurations that operate on frame-level. Moreover, new optimization criteria are introduced to focus the search on specific bit rates. As a result, coding tool configurations are obtained which yield so far inaccessible trade-offs between bit rate efficiency and decoding energy demand for VVC-coded sequences. The proposed methodology extends the design space and enhances the continuity of the Pareto front.
CRIS-Team Scopus Importer:2024-11-15
Keywords: VVC; Energy Efficiency; Optimization; Decoder

Design Techniques for Incremental Non-Regular Image Sampling Patterns - Grosche, Simon; Seiler, Jürgen; Kaup, André (IEEE International Conference on Imaging Systems and Techniques (IST), 2018)

Detecting Closely Spaced and Occluded Pedestrians Using Specialized Deep Models for Counting - Ghosh, Sanjukta; Amon, Peter; Hutter, Andreas; Kaup, André (IEEE Visual Communications and Image Processing (VCIP), 2017)
Pedestrian detection is an important task in surveillance applications and becomes particularly challenging when pedestrians are close together or occluding one another. This paper presents a novel approach to detect pedestrians in such challenging scenarios. A deep convolutional neural network trained for counting is specialized to count one pedestrian. The feature extractor learned thereby is exploited to detect one pedestrian at a time iteratively. For the base counting model and the specialization, extensive annotation efforts are not required since only a single number at the image level is used. Use of our method on pedestrian datasets with occlusion showed an improvement in the average miss rate values as compared to other methods for handling occlusio
Keywords: pedestrian detection; deep convolutional neural network; counting model; occlusion; model specialization

Detection and localization of multiple wideband acoustic sources based on wavefield decomposition using spherical apertures - Teutsch, H.; Kellermann, Walter (2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 2008)
This paper discusses novel methods for detecting and localizing multiple wideband acoustic sources using spherical apertures. In contrast to traditional methods the techniques presented here are not based on processing the output of individual microphones directly. Instead, the microphone signals are used to decompose the wave-field into its spherical harmonics which are subsequently used as a basis for novel frequency-independent multiple-source localization and detection methods. ©2008 IEE
Keywords: Source detection; Source localization; Spherical harmonics; Spherical microphone arrays; Wavefield decomposition

Determination of Low Activities Released by a Decommissioned Fuel Fabrication Plant Through Natural Air Exchange - Sohnius, B.; Anton, R.; Wehner, E.; Heidt, F. D.; Rabenstein, Rudolf (Nuclear Technology, 1992)
LMS::1597

Device Interoperability for Learned Image Compression with Weights and Activations Quantization - Koyuncu, Esin; Solovyev, Timofey; Alshina, Elena; Kaup, André (2022 PICTURE CODING SYMPOSIUM (PCS), 2022)
CRIS-Team WoS Importer:2023-03-03

Die akustische Mensch-Maschine-Schnittstelle - Kuntz, Achim; Spors, Sascha; Teutsch, Heinz (Elektronik, 2004)
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Die Messung des externen und interzonalen Luftaustauschs - Heidt, F. D.; Rabenstein, Rudolf (7. Internationales Sonnenforum, 1990)
LMS::1616

Difference image extrapolation for spectral completion in inter-sequence error concealment - Tröger, Tobias; Kaup, André (36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, 2011)
Mobile reception of digital TV often suffers from lost blocks and slices due to suboptimal channels. Inter-sequence error concealment reconstructs lost image blocks of distorted high-resolution TV signals by inserting corresponding error-free blocks of low-resolution reference signals. It is well-suited for automotive multi-broadcast receivers and can outperform state-of-the-art methods by up to 15 dB PSNRY. In this contribution, a novel spectral completion scheme is proposed which robustly estimates missing spatial frequencies of reconstructed blocks by extrapolation of difference images. Simulation results show that the proposed scheme can increase the reconstruction quality of inter-sequence error concealment by up to 3.2 dB. Also, the subjective quality is enhanced by significantly reducing blurring artifacts. © 2011 IEE
Keywords: error concealment; image alignment; image extrapolation; spectral completion

Differentiable Cost Model for Neural-Network Accelerator Regarding Memory Hierarchy - Conrad, Joschua; Wilhelmstatter, Simon; Asthana, Rohan; Belagiannis, Vasileios; Ortmanns, Maurits (IEEE Transactions on Circuits and Systems I-Regular Papers, 2024)
Dedicated neural-network inference-processors improve latency and power of the computing devices. They use custom memory hierarchies that take into account the flow of operators present in neural networks and convolutional layers. For efficient implementation, such network topologies can greatly benefit from hardware-cost optimization using automated network-architecture search. Thereby, cost functions predict the suitability of a network topology for a given type of inference hardware. A dnas that optimizes both weights and topology in a single training requires cost models to be differentiable in the dimensions of weight and activation matrices. soa differentiable cost models require time-consuming system-level measurements or simulation results, or do not encounter the hardware structure at all. This work presents a simple yet effective procedure for deriving a differentiable neural-network-accelerator cost-model that is suitable for any type of accelerator. It is based on hardware-independent parameterization and a novel differentiable divide-ceil function, as well as hardware-specific modeling. The resulting differentiable model can be reconfigured to the actual hardware size and memory structure to predict the inference energy for an exact network topology. The modeling and prediction are demonstrated for a soa SRAM-based inference-accelerator and for the Eyeriss accelerator, inferring different soa neural networks, resulting in excellent agreement with measured hardware.
CRIS-Team Scopus Importer:2024-11-08
Keywords: differentiable cost model; differentiable network-architecture search; in-memory computing; network/hardware co-design; Neural network

Diffraction and photometric limits in today's miniature digital camera systems - Brückner, A.; Schöberl, Michael (Proc. SPIE 8616, 2013)
Image sensors for digital cameras are built with ever decreasing pixel sizes. The size of the pixels seems to be limited by technology only. However, there are also hard theoretical limits for classical miniature camera systems: During a certain exposure time only a certain number of photons will reach the sensor. The resulting shot noise thus limits the signal-to-noise ratio. On the other hand, diffraction sets another limit for image resolution in case that there is enough brightness in the scene. In this work we show that current sensors are already surprisingly close to these limit
LMS::1294

Diffractive-optical processing of temporal signals - Jahns, J.; Lohmann, Adolf (ICO-20, 2005)
The topic of this presentation is the utilization of time for optical information processing. As clock rates in computing and communication systems increase and reach the THz border, optical techniques for signal filtering, shaping and clock distribution become attractive. We discuss the use of optics in temporal processing and consider in particular diffractive solutions. In this paper, we describe the use of double diffraction for implementing an ultrafast tapped-delay line. © 2006 Chinese Optics Lette
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Diffractive-optical processing of temporal signals, part I: basic principles - Jahns, J.; Lohmann, Adolf (Chinese Optics Letters, 2006)
LMS::653

Diffractive-optical processing of temporal signals, part II: optical tapped delay-line - Jahns, J.; Lohmann, Adolf (Chinese Optics Letters, 2006)
LMS::654

Diffraktiv-refraktive Filterung von kurzen optischen Pulsen - Jahns, J.; Lohmann, Adolf; Bohling, M (107. Jahrestagung der DGaO, Weingarten, 2006)
Ein wenig bekanntes Experiment von H. F. Talbot [1] beschreibt die Entstehung von Interferenzbändern im Wellenlängenspektrum durch Manipulation der Lichtausbreitung mit Hilfe einer geeignet gewählten Phasenplatte. Kurios an dem Experiment ist, daß die Bänder nur in einer, z.B. der +1. Beugungsordnung eines Gitterspektrometers zu beobachten sind, nicht jedoch in der -1. Ordnung. Eine qualitätive Erklärung beruht auf der Darstellung von weißem Licht durch ultrakurze Pulse. Hier bieten wir eine alternative Darstellung an, welche die Talbot-Bänder als diffraktiv-refraktiven Effekt beschreibt. Ausgehend von dem grundlegenden Experiment wurden mehrere Versuche durchgeführt, welche diese Sicht erläuter
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Keywords: Theoretische Grundlagen; Spektroskopie; Optische Informationsverarbeitung

Diffusion maps particle filter - Forster, Lukas; Schmidt, Alexander; Talmon, Ronen; Kellermann, Walter; Shnitzer, Tal (27th European Signal Processing Conference (EUSIPCO), 2019)

Digitale Klangsynthese durch physikalische Modellierung - Rabenstein, Rudolf; Trautmann, Lutz; Petrausch, Stefan (Mitteilungen aus den Arbeitskreisen, Arbeitsgemeinschaft Simulation in der Gesellschaft für Informatik (asim), 2003)
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Digitale Signalverarbeitung 1 - Schüßler, Hans Wilhelm (Springer, 2008)

Digitale Signalverarbeitung 2 - Schüßler, Hans Wilhelm (Springer, 2010)
Der zweite Teil der „Digitalen Signalverarbeitung" von Prof. Hans W. Schüssler gibt eine fundierte und ausführliche Einführung in den Entwurf digitaler Systeme und legt einen wesentlichen Schwerpunkt auf die Analyse und das Design digitaler Filter. Diese Erstausgabe basiert auf dem Manuskript von H. W. Schüssler, das von G. Dehner,R. Rabenstein und P. Steffen bearbeitet wurde. Wie der Band 1 ist auch dieser zweite Band von einer umfangreichen Matlab-Bibliothek begleitet, die im Internet zur Verfügung gestellt wir

Digital neutral density filter for moving picture cameras - Schöberl, Michael; Oberdoerster, Alexander; Foessel, Siegfried; Bloss, Hans; Kaup, André (Computational Imaging VIII, 2010)
Typical image sensors in digital cameras have a fixed sensitivity, and the amount of captured light energy is often controlled by adjusting exposure time and lens aperture. For high end motion imaging these settings are not available as they are used to set motion blur and depth of field, respectively. In many cases a proper exposure is achieved with additional optical filtering, using so called "neutral density" (ND) filters. We propose a digital equivalent of a neutral density filter, which can replace the handling of optical filters for camera systems. It consists of an adjusted sensor readout and in-camera processing of images. Instead of a single long exposure we capture N short exposures. These images are then combined by averaging. The short exposures reduce the sensitivity by a factor of N, while averaging reconstructs motion blur. In addition we also achieve a reduction of both dynamic and fixed pattern noise which leads to an overall increase in dynamic range. The digital ND filter can be used with regular image sensors and does not require hardware modifications. © 2009 Copyright SPIE - The International Society for Optical Engineerin
Keywords: Digital cameras; Image acquisition; Image enhancement; Motion pictures

Digital sound synthesis based on multidimensional system theory - Rabenstein, Rudolf; Trautmann, Lutz (9th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2002, 2002)
The creation of synthetic sounds plays an increasing role in multimedia signal processing. A variety of methods already used in digital musical instruments are now applied for sound rendering of virtual scenes. Most of these methods rely on the description of waveforms in terms of harmonics, attack and decay times, etc. However, these parameters have to be measured in real sounds or they must be derived from suitable models. In practice, their choice is left to the taste of a sound designer and is not based on scientific methods. This paper shows how to apply the theory of multidimensional continuous and discrete systems to derive a parametric description of musical sounds from a physical model of real or virtual musical instruments. Furthermore, it is shown that not only the parameters but also the algorithmic structure of powerful sound synthesis methods can be derived from multidimensional systems theory. © 2002 IEEE.

Digital Sound Synthesis Based on Transfer Function Models - Trautmann, Lutz; Rabenstein, Rudolf (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 99), 1999)
LMS::303

Digital Sound Synthesis by Block-Based Physical Modeling - Rabenstein, Rudolf; Petrausch, Stefan (2nd International Symposium on Communications, Control and Signal Processing (ISCCSP'06), 2006)
Different methods have been proposed for digital sound synthesis by physical modeling. Since musical instru- ments may consist of several components with quite different physical nature, there is no single best modeling paradigm. Choosing the optimal model for each component requires a common interconnection strategy, which leads to a computable algorithm with faithful sound reproduction. This contribution describes such an interconnection strategy on the basis of the wave digital principle. Examples are given for string, membrane, and brass instruments. I. I NTRODUCTION Digital sound synthesis by physical modeling attempts to model the physical sound production process itself rather than only to recreate a certain waveform. A number of dif- ferent techniques have been developed so far. However, since many musical instruments consist of different components with different physical nature, there is no single best meth od for physical modeling. Choosing the optimal model for each component requires a common interconnection strategy, which leads to a meaningful discrete-time approximation of the total system. Such a strategy is provided by the well-known wave digital principle. The use of wave variables for the interconnection network avoids the formation of delay free loops and yields computable algorithms. This contribution describes the wave digital principle as a starting point for the more general id ea of block-based physical modeling. It has been developed as a joint effort by the consortium of the European project ALMA (1). Section II presents the classical wave digital principle for lumped and distributed parameter networks. Section III introduces block-based modeling with examples for string and membrane instruments in Section III-B, and brass instruments in Section III-
LMS::623

Digital Sound Synthesis by Physical Modeling of Musical Instruments using Functional Transformation Models - Trautmann, Lutz (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2002)

Digital Sound Synthesis by Physical Modeling Using the Functional Transformation Method - Trautmann, Lutz; Rabenstein, Rudolf (Kluwer Academic/Plenum Publishers, 2003)
LMS::452

Digital sound synthesis by physical modelling - Rabenstein, Rudolf; Trautmann, Lutz (Proc. Int. Symposium on Image and Signal Processing and Analysis (ISPA 01), 2001)
After recent advances in coding of natural speech and audio signals, also the synthetic creation of musical sounds is gaining importance. Various methods for waveform synthesis are currently used in digital instruments and software synthesizers. A family of new synthesis methods is based on physical models of vibrating structures (string, drum, etc.) rather than on descriptions of the resulting waveforms. This article describes various approaches to digital sound synthesis in general and discusses physical modelling methods in particular. Physical models in the form of partial differential equations are presented. Then it is shown, how to derive discrete-time models which are suitable for real-time DSP implementation. Applications to computer music are given as examples.

Digital Sound Synthesis of Brass Instruments by Physical Modeling - Krach, B.; Petrausch, Stefan; Rabenstein, Rudolf (7th International Conference on Digital Audio Effects (DAFx'04), 2004)
LMS::513

Digital sound synthesis of string instruments with the functional transformation method - Rabenstein, Rudolf; Trautmann, Lutz (Signal Processing, 2003)
The theory of multidimensional continuous and discrete systems is applied to derive a parametric description of musical sounds from a physical model of real or virtual string instruments. The mathematical representation of this model is given by a partial differential equation for a vibrating string. Suitable functional transformations with respect to time and space turn this partial differential equation into a multidimensional transfer function. It is the starting point for the derivation of a discrete-time system by classical analog to discrete transformations. The coefficients of this discrete model depend explicitly on the geometric properties and material constants of the underlying physical model. This ensures a meaningful behaviour of the discrete system under varying conditions and allows for an intuitive control by the user. Furthermore, the performance of real-time implementations is discussed. Finally, several extensions of this synthesis method for computer music applications are presented. © 2003 Elsevier Science B.V. All rights reserved.
Keywords: Digital musical instruments; Digital sound synthesis; Multidimensional systems; Real-time implementation

Digital sound synthesis of string vibrations with physical and psychoacoustic models - Rabenstein, Rudolf; Müller, Alexander (2008 3rd International Symposium on Communications, Control, and Signal Processing, ISCCSP2008, 2008)
The digital synthesis of musical sounds is a field with many applications in digital audio, computer music, and virtual environments. One family of sound synthesis methods is physical modeling, which is based on digital implementations of vibrating bodies. The physical model is expressed by partial differential equations which describe the dynamic behavior of strings, membranes, plates, and other resonators. Various methods can be used to convert a partial differential equation into a discrete-time model. The so-called functional transformation method expands the differential operators into their eigenfunctions and expresses the effect of each eigenfunction by a simple digital model. The numerical expense grows linearly with the number of considered eigenfunctions and can be quite large for 2D or 3D vibrating bodies. This contribution describes a method to reduce the number of basic digital models by considering only a representative subset of eigenfunctions. The selection of the eigenfunctions is based on the well-known concept of critical bands. Audio examples show that the number of relevant eigenfunctions can be considerably reduced without impairing the timbral quality of the synthesized sounds. ©2008 IEE

Digital Watermarking and Fingerprinting of Uncompressed and Compressed Video - Hartung, Frank (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2000)

Digital Watermarking Facing Attacks by Amplitude Scaling and Additive White Noise - Eggers, Joachim; Bäuml, R.; Girod, Bernd (4th International ITGConference on Source and Channel Coding, 2002)
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Digital Watermarking of 3D Head Model Animation Parameters - Hartung, Frank; Eisert, Peter; Girod, Bernd (10th IEEE Workshop on Image and Multidimensional Signal Processing 1998, 1998)
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Digital Watermarking of Chemical Structure Sets - Eggers, Joachim; Ihlenfeldt, W.-D.; Girod, Bernd (Proceedings of the 4th Information Hiding Workshop, 2001)
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Digital Watermarking of MPEG-2 Coded Video in the Bitstream Domain - Hartung, Frank; Girod, Bernd (Proceedings ICASSP 97, 1997)
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Digital Watermarking of MPEG-4 facial animation parameters - Hartung, Frank; Eisert, Peter; Girod, Bernd (Computers & Graphics, 1998)
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Digital Watermarking of Text, Image, and Video Documents - Su, Jonathan; Hartung, Frank; Girod, Bernd (Computers & Graphics, 1998)
LMS::359

Dimensioning of optical birefringent anti-alias filters for digital cameras - Schöberl, Michael; Schnurrer, Wolfgang; Oberdörster, Alexander; Fößel, Siegfried; Kaup, André (2010 17th IEEE International Conference on Image Processing, ICIP 2010, 2010)
A digital camera samples the continuous real world. As with any sampling process, questions of aliasing for certain sampling frequencies and the prevention thereof arise. In this paper we will discuss the spatial domain sampling and prevention of aliasing in digital cameras. We focus on the widely used birefringent anti alias filters that are often called optical low pass filters (OLPF). We show 2D models for all contributions to spatial domain sampling and derive optimum filter parameters for minimum aliasing and best possible image sharpness. Compared to previously used selection rules, we can show that the optimum selection of filter parameters can easily deliver more sharpness and reduce aliasing by a factor of 2. The simulated results are finally confirmed in real world experiments. © 2010 IEE
Keywords: Cameras; Image generation; Imaging; Modeling

Direct Computation of Soft-Tissue Deformation in Craniofacial Surgery Simulation - Teschner, Matthias (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2001)

Direct Propagation Speed Estimation for Delay-Based Acoustic Applications - Annibale, Paolo (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
Spatially distributed acoustic sensors increasingly find many new applications in speech- based human-machine interfaces. One well researched topic is the localization of sound sources from Time Of Arrival (TOA) and Time Difference Of Arrivals (TDOA) measurements. Typically, the propagation speed of sound waves is considered as a known constant, although in a real scenario its value is known only up to some uncertainty due to air temperature variations. In general, when the conditions of the propagation medium are unknown, the propagation speed has to be considered as an additional unknown of the localization problem. Typical examples are underwater communications or seismology. In these cases the propagation speed of the waves emitted by the source has to be estimated jointly with the source position. Surprisingly, the direct estimation of the propagation speed from the same delay measurements used for localizing the target source is considered only in very few works. This thesis focuses on joint localization and propagation speed estimation by means of efficient algorithms that benefit from closed form solutions. After a review of some basic knowledge on wave propagation and time delay estimation, the state of the art techniques are described. Thereafter, a novel approach for the direct estimation of the propagation speed from delay measurements is presented. Such a new method is computationally efficient and benefits from a general formulation that holds for both TOAs and TDOAs. Its mathematical derivation is followed by several simulations where the proposed method shows significant performance improvements over the standard methods. As a benchmark for the estimation accuracy the Cramer-Rao Bound (CRB) was derived. Furthermore, experimental results using both simulated and real data demonstrate the feasibility of the proposed method. In particular, the proposed method was tested in conjunction with typical delay-based audio applications that normally assume a fixed value for the speed of sound. The so obtained results highlight the improvements led by the direct estimation of the speed of sound from both TOAs and TDOAs even for small temperature variations. Against a small increase in the computational cost, the accuracy of localization tasks turns out to be dramatically improved and virtually independent of temperature variations. Moreover, we show also an innovative application of direct speed estimation as a mean to assist a challenging task such as the disambiguation of TDOAs in multi-source reverberant environment

Direct Surface Parameter Estimation Using an Enhanced Line-to-Curve Mapping Approach - Sörgel, Wolfgang; Schalkoff, R. J. (Optical Engineering, 1997)
LMS::421

Disambiguation in multidimensional tracking of multiple acoustic sources using a gaussian likelihood criterion - Teng, Pengxiao; Lombard, Anthony; Kellermann, Walter (2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, 2010)
The TDOA-based acoustic source localization approaches suffer from a pairing problem in the case of multiple dimensions and multiple sources. To resolve this problem, usually three or more microphone pairs are required. In this paper, we propose a new solution based on a Gaussian likelihood function to pair TDOAs, which allows us to localize multiple moving acoustic sources in several dimensions using a minimum number of microphone pairs. Moreover, the proposed method is combined with a particle filter for tracking multiple moving sources. Experimental results demonstrate the effectiveness of the proposed scheme. ©2010 IEE
Keywords: Acoustic source localization; Multiple moving sources; Particle filter; TDOA estimation; Tracking

Discrete Models for Multidimensional Systems - Rabenstein, Rudolf (Proceedings of the VIII European Signal Processing Conference, 1996)
Multidimensional continuous systems arising from physical applications with distributed parameters are conventionally modelled by partial differential equations. This paper presents an alternate description by transfer functions based on suitably chosen functional transformations. Signal processing techniques lead to discrete simulation models which are suitable for computer implementation. Numerical results show considerable savings in computer time over existing numerical methods.

Discrete Simulation Methods for Multidimensional Systems Based on Functional Transformations - Rabenstein, Rudolf (Mathematics in Signal Processing IV, 1998)
A method for the discrete simulation of continuous multidimensional systems is presented. It is based on a transfer function description of the continuous system, which is obtained by application of appropriate functional transformations for each independent variable. Digital signal processing analog-to-discrete transformations turn the transfer function into a discrete simulation model. It is suitable for computer implementation and provides simulation algorithms, which are competitive with customary numerical methods. A comparison with a commercial software package demonstrates the advantage of the proposed simulation models.

Discrete simulation of a class of distributed systems using functional analytic methods - Dymkou, Vitali; Rabenstein, Rudolf; Steffen, Peter (Multidimensional Systems and Signal Processing, 2006)
This paper investigates the discrete simulation of the solution of initial-boundary-value problems that typically arise in technical areas. Since many of them lead to unbounded and non-self-adjoint differential operators, we have to use a rather general theory as a mathematical basis. For the class of scctorial operators with a compact resolvent operator, the solution of initial-boundary-value problem can be represented by means of a certain holomorphic semigroup. It is shown that the solution can be expanded with respect to the canonical system of the considered operator. Such an expansion corresponds to a multi-dimensional functional transformation in the frequency domain. This fact leads to simple structures for the realization of the resulting system. Computationally efficient numerical algorithms can be derived by proper methods well-known from the theory of digital signal processin
Keywords: Frequency domain; Multi-dimensional systems; Multi-functional transformations; Partial differential equations; Spectral theory

Discrete simulation of dynamical boundary value problems - Rabenstein, Rudolf (Proceedings of the EUROSIM Conference, EUROSIM '95, 1995)

Discrete simulation of linear multidimensional continuous systems - Rabenstein, Rudolf (Multidimensional Systems and Signal Processing, 1994)
This paper presents the theoretical foundations of a new method for the discrete simulation of multidimensional systems, which are described by linear partial differential equations with constant coefficients. It is based on methods customary in linear systems theory and digital signal processing and uses a frequency-domain representation of the continuous system to be simulated. The selection of appropriate functional transformations for each variable yields an exact treatment of initial and boundary conditions. The heat-flow equation is treated as an example. For this case, a realizing structure for the simulating discrete system is given along with simulation examples. © 1994 Kluwer Academic Publishers.
Keywords: Continuous systems simulation; digital signal processing; numerical methods; partial differential equations

Discrete simulation of uniform transmission lines by multidimensional digital filters - Rabenstein, Rudolf; Krauss, Herbert (International Journal of Numerical Modelling-Electronic Networks Devices and Fields, 1996)
Recently, a new method has been presented for the discrete simulation of multidimensional systems, which are described by linear partial differential equations with constant coeffecients. It is based on methods customary in linear systems theory and digital signal processing and uses a frequency-domain representation of the continuous system to be simulated. A proper choice of functional transformations for each independent variable allows us to treat the influence of initial conditions, boundary conditions and excitation functions separately by suitable transfer functions. From these, corresponding discrete transfer functions and the structure of a discrete system for the simulation of the continuous problem are derived. The application of this method to wave propagation problems on uniform transmission lines is presented here. At first, the continuous problem is characterized by transfer functions; then the derivation of a discrete system is shown, and finally, some simulation results and a comparison with other methods are given. © 1996 by John Wiley & Sons, Ltd.

Disparity-based Segmentation of Stereoscopic foreground/background Image Sequences - Aach, T.; Kaup, André (IEEE Transactions on Communications, 1994)
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Disparity-Based Segmentation of Stereoscopic Images - Aach, T.; Kaup, André (2nd Workshop on Stereoscopic Television, Darmstadt, 1990)
LMS::1005

Disparity Estimation for Fisheye Images with an Application to Intermediate View Synthesis - Eichenseer, Andrea; Bätz, Michel; Kaup, André (IEEE International Workshop on Multimedia Signal Processing (MMSP), 2017)
To obtain depth information from a stereo camera setup, a common way is to conduct disparity estimation between the two views; the disparity map thus generated may then also be used to synthesize arbitrary intermediate views. A straightforward approach to disparity estimation is block matching, which performs well with perspective data. When dealing with non-perspective imagery such as obtained from ultra wide-angle fisheye cameras, however, block matching meets its limits. In this paper, an adapted disparity estimation approach for fisheye images is introduced. The proposed method exploits knowledge about the fisheye projection function to transform the fisheye coordinate grid to a corresponding perspective mesh. Offsets between views can thus be determined more accurately, resulting in more reliable disparity maps. By re-projecting the perspective mesh to the fisheye domain, the original fisheye field of view is retained. The benefit of the proposed method is demonstrated in the context of intermediate view synthesis, for which both objectively evaluated as well as visually convincing results are provide

Distance Estimation for Molecular Communication - Marzouk, Mokhtar (2013)

Distance estimation of acoustic sources using the coherent-to-diffuse power ratio based on distributed training - Brendel, Andreas; Kellermann, Walter (International Workshop on Acoustic Signal Enhancement (IWAENC), 2018)
A range estimation method relying on distributed training in an Acoustic Sensor Network (ASN) is proposed. The relation between the estimated Coherent-to-Diffuse Power Ratio (CDR) which is used as feature and the range of an acoustic source is learned by Gaussian Process (GP) regression. To this end multiple sensor nodes, each equipped with two microphones are distributed over the area of interest delivering multiple observations of the feature and extending the amount of training data significantly compared to the single node case. However, the computational power of the sensor nodes in an ASN is usually limited and a transmission of the data to a fusion center is infeasible due to constraints of the transmit power and because such a system would fail if transmission drop outs occur or the fusion center breaks. Hence, we aim at a completely distributed algorithm which is as exact as a corresponding centralized version, computationally simple and update-based, i.e., all sensor nodes have to fulfill exactly the same role in the algorithm. The efficacy of the proposed method is shown by a simulation study.
Keywords: Acoustic range estimation; Acoustic sensor networks; Online Gaussian process regression

Distant-talking continuous speech recognition based on a novel reverberation model in the feature domain - Sehr, Armin; Zeller, Marcus; Kellermann, Walter (INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP, 2006)
A novel approach for automatic speech recognition in highly reverberant environments, proposed in [1] for isolated word recognition, is extended to continuous speech recognition (CSR) in this paper. The approach is based on a combined acoustic model consisting of a network of clean speech HMMs and a reverberation model. Because the grammatical information and the information about the acoustic environment are strictly separated in the combined model, a high degree of flexibility for adapting the system to new tasks and new environments is attained. We show that virtually all known CSR search algorithms can be used for decoding the proposed combined model if a few extensions are added. In a simulation of a connected digit recognition task, the proposed method achieves more than 40 % reduction of the word error rate compared to a conventional HMM-based system trained on rever-berant speech, at the cost of an increased decoding complexit
Keywords: Dereverberation; Distant-talking speech recognition; Robust speech recognition

Distributed Coding of Multiview Sparse Sources with Joint Recovery - van Luong, Huynh; Deligiannis, Nikos; Forchhammer, Soren; Kaup, André (2016)
In support of applications involving multiview sources in distributed object recognition using lightweight cameras, we propose a new method for the distributed coding of sparse sources as visual descriptor histograms extracted from multiview images. The problem is challenging due to the computational and energy constraints at each camera as well as the limitations regarding inter-camera communication. Our approach addresses these challenges by exploiting the sparsity of the visual descriptor histograms as well as their intra- and inter-camera correlations. Our method couples distributed source coding of the sparse sources with a new joint recovery algorithm that incorporates multiple side information signals, where prior knowledge (low quality) of all the sparse sources is initially sent to exploit their correlations. Experimental evaluation using the histograms of shift-invariant feature transform (SIFT) descriptors extracted from multiview images shows that our method leads to an average bit-rate saving of 30.7% compared to the state-of-the-art distributed compressed sensing method with independent encoding of the source
LMS::1687
Keywords: distributed source coding; compressed sensing with side information; distributed object recognition

Distributed Parallel Image Signal Extrapolation Framework using Message Passing Interface - Seiler, Jürgen; Kaup, André (European Signal Processing Conf. (EUSIPCO), 2016)
This paper introduces a framework for distributed parallel image signal extrapolation. Since high-quality image signal processing often comes along with a high computational complexity, a parallel execution is desirable. The proposed framework allows for the application of existing image signal extrapolation algorithms without the need to modify them for a parallel processing. The unaltered application of existing algorithms is achieved by dividing input images into overlapping tiles which are distributed to compute nodes via Message Passing Interface. In order to keep the computational overhead low, a novel image tiling algorithm is proposed. Using this algorithm, a nearly optimum tiling is possible at a very small processing time. For showing the efficacy of the framework, it is used for parallelizing a high-complexity extrapolation algorithm. Simulation results show that the proposed framework has no negative impact on extrapolation quality while at the same time offering good scaling behavior on compute cluster

Distributed Source Localization in Acoustic Sensor Networks using the Coherent-to-Diffuse Power Ratio - Brendel, Andreas; Kellermann, Walter (IEEE Journal of Selected Topics in Signal Processing, 2019)

Distribution of Modal Damping in Absorptive Shoebox Rooms - Schäfer, Maximilian; Prawda, Karolina; Rabenstein, Rudolf; Schlecht, Sebastian J. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2023)
The image-source method is widely applied to compute room impulse responses (RIRs) of shoebox rooms with arbitrary damping. However, with increasing RIR lengths, the number of image sources grows rapidly, leading to slow computation. We propose a method to estimate the damping density of a damped shoebox room, which in turn can provide the energy decay necessary to model the stochastic late reverberation. The damping density is derived from a modal decomposition that is compliant with the ISM solution. We show that the proposed method gives a more accurate estimate of the energy decay than previous methods and can be efficiently computed regardless of the RIR lengths. While we focus on the derivation and evaluation, the main practical applications of the proposed model include, e.g., the faster synthesis of late reverb and the analysis of multi-slope decays.
CRIS-Team Scopus Importer:2023-10-13
Keywords: Damping Density; Image Source Method; Modal Decomposition; Reverberation; Shoebox Rooms

Domain Adaptation for Unknown Image Distortions in Instance Segmentation - Gruber, Maximiliane; Brand, Fabian; Mosebach, Alina; Seiler, Jürgen; Kaup, André (IEEE International Conference on Image Processing (ICIP), 2022)

Downlink Performance Improvements in (A)synchronous GPRS Networks using Single/Multiple Antenna Interference Cancellation Techniques - Popova, Larissa; Meyer, Raimund; Gerstacker, Wolfgang; Koch, Wolfgang (12th European Wireless Conference 2006 - Enabling Technologies for Wireless Multimedia Communications, 2011)
The aim of this paper is to assess GPRS data throughput of GSM networks for single and multiple antenna interference cancellation (SAIC/MAIC) receivers in the terminals. This is done by using a two-step top-down simulation methodology. Both synchronous and asynchronous networks with traditional European reuses of 1/3 and 3/9 are considered. It turns out that SAIC/MAIC receivers are very beneficial also for European network planning. The results indicate a substantial gain in data throughput in the order of 20-35% for SAIC compared to terminals with a conventional receiver. A further dramatic increase in throughput up to 120% can be achieved by terminals using an MAIC receiver. In spite of the widespread view that a maximum gain will be achieved for a synchronized network, where the amount of overlap between desired signal and interference can be controlled, our results show that the performance of both network types is very simila

Drahtlose ATM-Systeme - Franz, W.; Ben Younes, Khaled; Sinner, C.; Sigle, R. (NTZ, Telekommunikation und Informationstechnik, 1997)

DSP-Based Compression of Four-Color Printed Images - Kaup, André; Aach, T. (International Conference on DSP Applications and Technology, Berlin, 1991)
LMS::1012

Dynamic Image Sampling Using a Novel Variance Based Probability Mass Function - Grosche, Simon; Koller, Michael; Seiler, Jürgen; Kaup, André (IEEE Transactions on Computational Imaging, 2020)

Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction - Jonscher, Markus; Seiler, Jürgen; Lanz, Daniela; Schöberl, Michael; Bätz, Michel; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2018)

Dynamikerweiterung durch Weißabgleich im Bildsensor - Schöberl, Michael; Schnurrer, Wolfgang; Foessel, S.; Kaup, André (16. Farbworkshop der German Color Group, Ilmenau, 2010)
Heutige Kameras besitzen eine feste spektrale Empfindlichkeit und können bei Beleuchtung ungünstiger Farbtemperatur nur einen reduzierten Dynamikbereich aufnehmen. Diese Einschränkung kann über ein neues Verfahren deutlich reduziert werden: Über ein digitales Neutraldichtefilte(ND-Filter) wird eine selektive Reduktion der Sensorempfindlichkeit für manche Farbkanäle erreicht. Dies führt zu einer Angleichung der Farbkanäle und damit einer Erweiterung des nutzbaren Dynamikumfangs. Die Anpassungen können digital schaltbar realisiert werden bei gleichzeitigem Erhalt der Blende, Bewegungsunschärfe und Sensorempfindlichkei

EB-ESPRIT: 2D localization of multiple wideband acoustic sources using eigen-beams - Teutsch, Heinz; Kellermann, Walter (2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05, 2005)
This paper is concerned with the problem of localizing multiple wideband acoustic sources. In contrast to existing techniques, this method takes the physics of wave propagation into account. 2D wave fields are decomposed using cylindrical harmonics as basis functions by a circular array mounted into a rigid cylindrical baffle. The obtained wave field representation is then used to serve as a basis for high-resolution subspace beamforming methods, most notably ESPRIT. It is shown that acoustic source localization based on wave field decomposition has the potential to unambiguously localize multiple simultaneously active wideband sources in the array's full 360 degrees field-of-view. ©2005 IEE

Echo Cancellation and Suppression - Kellermann, Walter (Handbook of Signal Processing in Acoustics, 2008)

Echoes and Noise with Seamless Acoustic Man-Machine Interfaces - The Challenge persists - Kellermann, Walter (Conf. Rec. of the Sixth International Workshop on Acoustic Echo and Noise Control, 1999)
LMS::292

Echtzeitsimulation kontinuierlicher Systeme mit verteilten Parametern - Rabenstein, Rudolf (Bericht des ASIM-Arbeitstreffens Simulation technischer Systeme, Heft 24, 1993)

Edge modeling prediction for computed tomography images - Weinlich, Andreas; Amon, P.; Hutter, A.; Kaup, André (2012 IEEE Visual Communications and Image Processing, VCIP 2012, 2012)
Predictive coding is applied in many state-of-the-art lossless image compression algorithms like JPEG-LS, CALIC, or least-squares-based methods. We propose a new approach for accurate intensity prediction in pixel-predictive coding of computed tomography (CT) images. Exploiting their particular edge characteristic, the method only relies on a small twelve-pixel context. It does neither require adaptation to larger-region image characteristics nor the transmission of side-information and therefore may be particularly suitable for compression of small images like in region-of-interest coding. While applying simple linear prediction with fixed weights in homogeneous regions, a Gauss error model-function is fit to given contexts in edge regions and then sampled at the position corresponding to the pixel to be predicted in order to obtain prediction values. By the example of CALIC, it is shown that for CT data the edge modeling prediction (EMP) approach can yield an even smaller prediction error than other methods relying on context modeling. © 2012 IEE
Keywords: Edge modeling prediction; Gauss error function fitting; lossless medical image compression; predictive coding; X-ray computed tomography

Editorial to the Special Section on Optimized Image/Video Coding Based on Deep Learning - Chen, Zhibo; Herglotz, Christian; Cagnazzo, Marco (IEEE Open Journal of Circuits and Systems, 2021)

Effective Rank-Based Estimation of the Coherent-To-Diffuse Power Ratio - Löllmann, Heinrich; Brendel, Andreas; Kellermann, Walter (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021)
Many algorithms for speech dereverberation and noise reduction rely on an estimate of the coherent-to-diffuse power ratio (CDR). Such systems typically operate in very diverse acoustic conditions, and CDR estimators relying on very weak model assumptions about the acoustic sound field of the desired speech and interfering noise are hence desirable. A CDR estimator whose design is based on this premise is devised in this contribution. The proposed non-iterative CDR estimator exploits the effective rank of the covariance matrix of the recorded input signals by assuming it to be of lower rank for a coherent sound field than for a diffuse sound field. In addition to this weak assumption, related methods usually require information about, e.g., the array geometry, direction-of-arrival (DOA) of the desired source or rely on a coherence model for the desired signal or background noise, which is not required for the proposed method. Despite the use of little a priori information about the acoustic sound field, the new estimator achieves a significantly higher estimation accuracy for the CDR in comparison to related state-of-the-art approaches which use explicit coherence models.<br
Keywords: CDR estimation; diffuseness estimation; multichannel speech enhancement; dereverberation

Efficient 3D Simulation of Wave Propagation with the Functional Transformation Method - Petrausch, Stefan; Rabenstein, Rudolf (18th Symposium on Simulation Technique, Arbeitsgemeinschaft Simulation in der Gesellschaft für Informatik (ASIM), 2005)
The functional transformation method (FTM) is a elegant mathematical approach for the solution of partial differential equations (PDEs) and has been so far successfully applied in digital sound synthesis for the simulation of string and percussion (drums for instance) instruments. Based on suitable integral transformations, the problem is solved analytically in both, time and space frequency domain. Discretization and inverse transformation yields a discrete algorithm, implementable in any computer hardware. In this paper the FTM is used for the simulation of three-dimensional (3D) wave propagation. With the help of a multi-dimensional fast Fourier transform (FFT), the complete wave field can be evaluated and visualized very efficiently. A program is demonstrated which simulates a 90m 3 room with an audio bandwidth of 5kHz at 5 images per second on current PC-hardware. The algorithm is compared with classical simulation techniques both in computational complexity and accurac
LMS::580

Efficient Active Listening Room Compensation for Wave Field Synthesis - Spors, Sascha; Buchner, Herbert; Rabenstein, Rudolf (116th AES Convention, 2004)
LMS::497

Efficient adaptive DFT-domain volterra filters using an automatically controlled number of quadratic kernel diagonals - Zeller, Marcus; Azpicueta-Ruiz, Luis A.; Arenas-Garcia, Jeronimo; Kellermann, Walter (2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, 2010)
This paper presents a method for estimating the optimum number of second-order kernel diagonals of an adaptive Volterra filter in system identification tasks. To this end, a recently proposed time-domain mechanism is carried over to the very efficient partitioned-block DFT-domain Volterra filtering technique. The size of the non-linear memory is controlled by monitoring the performance of an adaptive combination scheme with two differently-sized quadratic kernels. Subsequently, an efficient version is derived, requiring only minor additional computations as compared to a single Volterra filter. The effectiveness of the outlined estimation procedure is demonstrated by various simulations with real nonlinear systems and both noise and speech inputs in an acoustic echo cancellation scenario. ©2010 IEE
Keywords: Adaptive volterra filter; Convex combination; Nonlinear system identification; Structure selection

Efficient Coding Of 360° Videos Exploiting Inactive Regions in Projection Formats - Herglotz, Christian; Jamali, Mohammadreza; Coulombe, Stéphane; Vazquez, Carlos; Vakili, Ahmad (IEEE International Conference on Image Processing (ICIP), 2019)
Keywords: 360 degree; video coding; inactive areas; HEVC; octahedron; icosahedron; segmented sphere; rotatedsphere; cubemap; projection

Efficient coding of video sequences by non-local in-loop denoising of reference frames - Wige, Eugen; Yammine, Gilbert; Amon, P.; Hutter, A.; Kaup, André (2011 18th IEEE International Conference on Image Processing, ICIP 2011, 2011)
The compression efficiency of coding noisy image sequences is highly dependent on the noise itself and on the used quantization parameter. For very high quality near lossless compression a promising approach to save bitrate is to introduce an in-loop denoising filter in the codec. In this paper, we enhance our in-loop denoising scheme by a low complexity noise variance estimation algorithm. Additionally, we compare three different algorithms for denoising of the reference frame for medium to high quality compression of noisy videos. We use a low complexity adaptive Wiener filter, the NonLocal Means algorithm, and the BM3D algorithm for denoising of the reference frame only. We show that maximum bitrate savings up to 18% can be achieved using a sophisticated algorithm like the BM3D and up to 12% using a low complexity algorithm like the adaptive Wiener filter. © 2011 IEE
Keywords: high quality compression; in-loop denoising; inter frame prediction; near lossless compression

Efficient Design of Digital 3-D Cone-Shaped FIR Fan-Filters Using Transformation - Runze, Gerhard (10th IEEE Workshop on Image and Multidimensional Signal Processing 1998, 1998)

Efficient frequency-domain realization of robust generalized sidelobe cancellers - Herbordt, Wolfgang; Kellermann, Walter (2001 IEEE fourth Workshop on Multimedia Signal Processing, 2001)
This paper deals with hands-free acoustical front-ends for human/machine interaction in competing talker situations using efficient frequency-domain implementations of a robust Generalized Sidelobe Canceller (GSC). A new scheme is devised which leads to a factor of three of computational savings. The GSC tracking behavior is not impaired as the block lengths are kept sufficiently small.

Efficient Implementation of Digital 3-D IIR Filters for Separation of Velocity Components in Data-Streams - Runze, Gerhard (Proceedings 2nd IMACS International Conference on Circuits, Systems and Computers, 1998)

Efficient Implementation of Multidimensional Systems using the Functional Transformation Method and Polyphase-Filterbanks - Petrausch, Stefan; Rabenstein, Rudolf (Proc. Int. Workshop on Spectral Methods and Multirate Signal Processing (SMMSP 03), 2003)
LMS::448

Efficient Learned Wavelet Image and Video Coding - Meyer, Anna; Prativadibhayankaram, Srivatsa; Kaup, André (2024 IEEE International Conference on Image Processing (ICIP), 2024)

Efficient lossless coding of highpass bands from block-based motion compensated wavelet lifting using JPEG 2000 - Schnurrer, Wolfgang; Tröger, Tobias; Richter, Thomas; Seiler, Jürgen; Kaup, André (IEEE Int. Conf. on Visual Communications and Image Processing (VCIP), 2014)
Lossless image coding is a crucial task especially in the medical area, e.g., for volumes from Computed Tomography or Magnetic Resonance Tomography. Besides lossless coding, compensated wavelet lifting offers a scalable representation of such huge volumes. While compensation methods increase the details in the lowpass band, they also vary the characteristics of the wavelet coefficients, so an adaption of the coefficient coder should be considered. We propose a simple invertible extension for JPEG 2000 that can reduce the filesize for lossless coding of the highpass band by 0.8% on average with peak rate saving of 1.1
Keywords: Adaptive Coding; Computed Tomography; Lossless Image Coding; Signal Analysis; Wavelet Lifting

Efficient ML-Estimator for Blind Reverberation Time Estimation - Löllmann, Heinrich; Brendel, Andreas; Kellermann, Walter (European Signal Processing Conference (EUSIPCO), 2018)
A new maximum likelihood (ML) estimator for the blind estimation of the reverberation time (RT) is derived. In contrast to previously proposed ML-based reverberation time estimators, the RT estimate is obtained by a simple closed-form expression, which leads to significant computational savings. Moreover, it is shown that the new estimator is unbiased and reaches the Cramer-Rao lower bound. The proposed RT estimator achieves a similar estimation accuracy but involves a significantly lower computational complexity compared to an ML-based RT estimator that scored among the best at the ACE Challenge.
Keywords: Reverberation time; blind estimation; ML estimation

Efficient Multichannel Nonlinear Acoustic Echo Cancellation Based on a Cooperative Strategy - Halimeh, Mhd Modar; Kellermann, Walter (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020)

Efficient nonlinear acoustic echo cancellation by partitioned-block significance-aware Hammerstein group models - Hofmann, Christian; Günther, Michael; Hümmer, Christian; Kellermann, Walter (European Signal Processing Conf. (EUSIPCO), 2016)
A powerful and efficient model for nonlinear echo paths of hands-free communication systems is given by the recently proposed Significance-Aware Hammerstein Group Model (SA-HGM). Such a model learns memoryless loudspeaker nonlinearities on a small temporal support of the echo path (preferably the direct-sound region) and extrapolates the nonlinearities for the entire echo path afterwards. In this contribution, an efficient frequency-domain realization of the significance-aware concept for nonlinear acoustic echo cancellation is proposed. The proposed method exploits the benefits of partitioned-block frequency-domain adaptive filtering and will therefore be referred to as Partitioned-Block Significance-Aware Hammerstein Group Model (PBSA-HGM). This allows to efficiently model a long nonlinear echo path by a linear partitioned-block frequency-domain adaptive filter after a parametric memoryless nonlinear preprocessor, the parameters of which are estimated via a nonlinear Hammerstein Group Model (HGM) with the short temporal support of a single block only.<br

Efficient one-pass frame level rate control for H.264/AVC - Li, Xiang; Hutter, Andreas; Kaup, André (Journal of Visual Communication and Image Representation, 2009)
In this paper, an efficient one-pass frame level rate control algorithm is proposed for H.264/AVC, where the two essential problems in rate control, i.e., the budget allocation (BA) and the quantization parameter determination (QPD) are both considered. First, an efficient BA scheme is designed with special consideration of the inter-frame dependency. Accordingly, the error propagation caused by improper QP assignment in the motion compensation process is reduced and the total distortion is kept at a low level. Second, a better QPD method is developed based on an accurate rate model and a second feedback mechanism so that a high rate accuracy is guaranteed. Simulations verified the performance of the proposed algorithm. Compared with the fixed QP coding and the two recommended rate control algorithms (G012-MB and G012-Frame) in H.264/AVC reference software, up to 1.50, 1.12, and 0.94 dB were achieved with a higher rate accuracy in the coding efficiency test defined by ITU-T VCEG. Particularly, a more significant improvement was observed in slow movement scenarios: compared with G012-MB, a gain of 0.80 dB on average and 1.43 dB at maximum was obtained with 66.0% reduction of average rate mismatch. Compared with G012-Frame, the average and maximum gains are 0.34 and 1.06 dB, respectively. While at the same time, the average rate mismatch was reduced by 90.4%. Considering the low computational cost, the proposed algorithm is quite appealing to real-time video applications. © 2009 Elsevier Inc. All rights reserve
Keywords: Budget allocation; H.264/AVC; Hybrid video coding; Inter-frame dependency; One-pass video coding; Quantization parameter determination; Rate control; Rate-distortion optimization

Efficient Prediction of Uncovered Background in Interframe Coding Using Spatial Extrapolation - Kaup, André; Aach, T. (IEEE International Conference on Acoustics, Speech, and Signal Processing, Adelainde, 1994)
LMS::1024

Efficient receivers for GSM MUROS downlink transmission - Meyer, Raimund; Gerstacker, Wolfgang; Obernosterer, Frank; Ruder, Michael; Schober, Robert (2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, 2010)
Currently, Multiple Users Reusing One Slot (MUROS) is discussed in 3GPP GERAN as an extension of the GSM standard. In MUROS, two overlaid GMSK signals are transmitted in the same time slot and at the same frequency resource. By this, capacity of existing GSM networks in principle can be doubled and up to four half rate voice users can share one time slot. In this paper, channel estimation and detection is investigated for the MUROS downlink. Two novel channel estimation algorithms are presented, taking into account the specifics of the MUROS downlink. For detection, a joint MLSE of both user signals can be applied in case of noise limited scenarios. For interference limited environments, it turns out that approaches based on the mono interference cancellation (MIC) algorithm for single antenna interference cancellation (SAIC) are more favorable. It is shown that the standard MIC algorithm performs sufficiently well and could be used for a fast introduction of MUROS in existing GSM networks. For enhanced performance, a novel algorithm based on MIC along with successive interference cancellation is proposed. The presented results demonstrate that an even better performance as the GSM reference performance before introduction of SAIC can be obtained for MUROS if well-designed receivers are use

Efficient System Identification using Parallel Connections and Cascades - Hofmann, Christian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
This thesis is dedicated to efficient System Identification (SysId) and Acoustic Echo Cancellation (AEC) for two classes of applications of outstanding practical relevance. Class 1 is SysId for single-channel systems with nonlinearly distorting playback equipment (e.g., miniaturized amplifiers and loudspeakers of smartphones in hands-free mode). Class 2 is SysId for the linear Multiple-Input/Multiple-Output (MIMO) systems emerging as Loudspeaker-Enclosure-Microphone Systems (LEMS) between the loudspeakers of a premium sound reproduction system (an object-based rendering system like a Wave Field Synthesis (WFS) system) and a microphone array for voice control. The concept of Significance-Aware (SA) filtering is introduced as efficient solution for class 1. SA filtering gains efficiency by decomposing the identification of the nonlinear system into synergetically interacting adaptive subsystems: a long but efficient linear system capturing the dispersive component and a structurally more complex nonlinear system, which nevertheless causes low computational effort due to its short memory. For class 2, the concept of Source-Specific System Identification (SSSysId) is proposed. SSSysId employs reference information from an object-based rendering system (virtual source signals and the driving filters distributing the virtual source signals to the loudspeakers). SSSysId gains computational efficiency by identifying the serial connection of the driving filters with the LEMS instead of the LEMS itself and using these identified source-specific systems to track the actual LEMS in a second stage. Unlike in SA filtering, efficiency is not caused by shortening the temporal support but by reducing the number of channels. The presented computational complexity analyses and experimental results confirm both efficiency and efficacy of SA filtering and SSSysId. Finally, a brief outlook on the generalization of SA filtering and SSSysId to nonlinear MIMO systems is provide
Keywords: system identification; acoustic echo cancellation

Efficient target activity detection based on recurrent neural networks - Gerber, Daniel; Meier, Stefan; Kellermann, Walter (Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2017)
This paper addresses the problem of Target Activity Detection (TAD) for binaural listening devices. TAD denotes the problem of robustly detecting the activity of a target speaker in a harsh acoustic environment, which comprises interfering speakers and noise (‘cocktail party scenario’). In previous work, it has been shown that employing a Feed-forward Neural Network (FNN) for detecting the target speaker activity is a promising approach to combine the advantage of different TAD features (used as network inputs). In this contribution, we exploit a larger context window for TAD and compare the performance of FNNs and Recurrent Neural Networks (RNNs) with an explicit focus on small network topologies as desirable for embedded acoustic signal processing systems. More specifically, the investigations include a comparison between three different types of RNNs, namely plain RNNs, Long Short-Term Memories, and Gated Recurrent Units. The results indicate that all versions of RNNs outperform FNNs for the task of TA
Keywords: voice activity detection; target activity detection; recurrent neural networks; binaural listening devices

Efficient training of acoustic models for reverberation-robust medium-vocabulary automatic speech recognition - Sehr, Armin; Barfuß, Hendrik; Hofmann, Christian; Maas, Roland; Kellermann, Walter (Joint Workshop on Hands-Free Speech Communication and Microphone Arrays (HSCMA), 2014)
A recently proposed concept for training reverberation-robust acoustic models for automatic speech recognition using pairs of clean and reverberant data is extended from word models to tied-state triphone models in this paper. The key idea of the concept, termed ICEWIND, is to use the clean data for the temporal alignment and the reverberant data for the estimation of the emission densities. Experiments with the 5000-word Wall Street Journal corpus confirm the benefits of ICEWIND with tied-state triphones: While the training time is reduced by more than 90%, the word accuracy is improved at the same time, both for room-specific and multi-style hidden Markov models. Since the acoustic models trained with ICEWIND need less Gaussian components for the emission densities to achieve comparable recognition rates as Baum-Welch acoustic models, ICEWIND also allows for a reduced decoding complexity. © 2014 IEE
Keywords: distant-talking ASR; reverberation; robust speech recognition; stereo data

Ego-noise reduction using a motor data-guided multichannel dictionary - Schmidt, Alexander; Deleforge, Antoine; Kellermann, Walter (IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016)
We address the problem of ego-noise reduction, i.e., suppressing the noise a robot causes by its own motions. Such noise degrades the recorded microphone signal massively such that the robot’s auditory capabilities suffer. To suppress it, it is intuitive to use also motor data, since it provides additional information about the robot’s joints and thereby the noise sources. We propose to fuse motor data to a recently proposed multichannel dictionary algorithm for ego-noise reduction. At training, a dictionary is learned that captures spatial and spectral characteristics of ego-noise. At testing, nonlinear classifiers are used to efficiently associate the current robot’s motor state to relevant sets of entries in the learned dictionary. By this, computational load is reduced by one third in typical scenarios while achieving at least the same noise reduction performance. Moreover, we propose to train dictionaries on different microphone array geometries and use them for ego-noise reduction while the head to which the microphones are mounted is moving. In such scenarios, the motor guidedapproach results in significantly better performance values

Eigen-Beam Processing for Direction-of-Arrival Estimation Using Spherical Apertures - Teutsch, Heinz; Kellermann, Walter (Conf. Rec. Joint Workshop for Hands-Free Speech Communication and Microphone Arrays (HSCMA), 2005)
In [1] a method of decomposing a 3D wave field into spherical harmonics (’eigen-beams’) by using spherical microphone arrays was introduced. It was further shown how the eigen-beams can be used for beamforming applications by means of a modal beamformer structure. This paper introduces a method for the unambiguous localization of multiple wideband acoustic sources in 3D space using the eigen-beams of [1]. It was shown in [1] that the response of a continuous spherical aperture of radius R to a plane wave impinging from (θ, φ), can be expressed as, F m n (kR, θ, φ) = i n bn(kR)Y m n (θ, φ) ∗ = i n bn(kR) where (·) ∗ denotes the conjugate complex operation, i 2 = −1, k is the wavenumber, Y m n n and degree m, P m n (·) is the associated Legendre function of order n and degree m, and ⎨ jn(kR) bn(kR) = jn(kR) − h(1) ′ n j ′ n (kR) h(1)
LMS::544

Eigenspace adaptive filtering for efficient pre-equalization of acoustic MIMO systems - Spors, Sascha; Buchner, Herbert; Rabenstein, Rudolf (14th European Signal Processing Conference, EUSIPCO 2006, 2006)
Pre-equalization of MIMO systems is required for a wide variety of applications, e. g. in channel equalization and spatial sound reproduction. However, traditional adaptive algorithms fail for channel numbers of some ten or more. It is shown that the problem becomes tractable by decoupling of the MIMO adaptation problem, e. g. by a generalized singular value decomposition. This method is called eigenspace adaptive filtering. The required singular vectors depend on the unknown system response to be equalized. An reasonable approximation by data-independent transformations is derived for the example of listening room compensation yielding the approach of wave-domain adaptive filterin

Ein approximationstheoretischer Ansatz zur effizienten Texturanalyse - Kaup, André; Aach, T. (Mustererkennung 1992, Serie Informatik Aktuell, Berlin, Springer Verlag, 1992)
LMS::1016

Einbettung digitaler Wasserzeichen in MPEG-2 codierte Videosequenzen - Hartung, Frank; Girod, Bernd (Proceedings 7. Dortmunder Fernsehseminar, 1997)
LMS::412

Einbettung von Informationen und digitale Wasserzeichen als Kommunikation mit Seiteninformation - Eggers, Joachim (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2002)

Einführung in die Systemtheorie, 2. Auflage - Girod, Bernd; Rabenstein, Rudolf; Stenger, Alexander (Teubner Verlag, 2003)
Dieses Buch bietet eine leicht zugängliche Einführung in die Systemtheorie für Studierende der Elektrotechnik, Elektronik, Informationstechnik und verwandter Gebiete. Es legt die Grundlagen für das Studium anwendungsorientierter Fächer wie Multimediakommunikation, Nachrichtenübertragung und Signalverarbeitung. Der Schwerpunkt liegt auf der Vermittlung von systemorientiertem Denken und der Fähigkeit, Probleme zu lösen. Dazu tragen auch die 200 Aufgaben bei, deren Lösungen vollständig enthalten sind. Die 2. Auflage wurde um einen neuen Abschnitt zum Thema Regelkreise erweitert; es finden sich begriffliche Präzisierungen, zahlreiche kleinere Korrekturen sowie verbesserte Abbildungen.
LMS::453

Einführung in die Systemtheorie, 3. Auflage - Girod, Bernd; Rabenstein, Rudolf; Stenger, Alexander (Teubner Verlag, 2005)
LMS::674

Einführung in die Systemtheorie, 4. Auflage - Girod, Bernd; Rabenstein, Rudolf; Stenger, Alexander (Teubner-Verlag, 2007)
Dieses Lehrbuch bietet eine leicht zugängliche Einführung in die Systemtheorie für Studierende der Elektrotechnik, Elektronik, Informationstechnik und verwandter Gebiete. Es legt die Grundlagen für das Studium anwendungsorientierter Fächer wie Multimediakommunikation, Nachrichtenübertragung und Signalverarbeitung. Der Schwerpunkt liegt auf der Vermittlung von systemorientiertem Denken und der Fähigkeit, Probleme zu lösen. Dazu tragen auch 200 Aufgaben bei, deren Lösungen mit der 4. Auflage vollständig im Internet unter http://app.gwv-fachverlage.de/tu/8j zu finden sind. Der Lehrstoff wurde für die 4. Auflage durchgesehen und aktualisiert.
LMS::730

Einführung in die Systemtheorie - Girod, Bernd; Rabenstein, Rudolf; Stenger, Alexander (B.G. Teubner, 1997)
Die Systemtheorie ist dabei, die Netzwerktheorie als klassisches Grundlagenfach der Elektrotechnik abzulösen. Nicht mehr das Zusammenwirken einzelner Bauteile, sondern die Beziehungen zwischen verschiedenen Teilsystemen bilden die Basis für den Entwurf und die Realisierung technischer Geräte und Anlagen. Das im Oktober 1997 erschienene Buch Einführung in die Systemtheorie bietet eine leicht verständliche Darstellung der Theorie kontinuierlicher und diskreter Systeme führt schrittweise in die Beschreibung von Signalen und Systemen im Zeit- und Frequenzbereich ein. Eine große Zahl von Zeichnungen, Beispielen und gelösten Übungsaufgaben erleichtert das Selbststudium. Die Lösung von Differentialgleichungen, der Umgang mit Übertragungsfunktionen, die Berechnung von Laplace-, Fourier- und anderen Transformationen, die Durchführung der Faltung und die Abtastung kontinuierlicher Signale werden ausführlich und an vielen Beispielen erklärt. Dabei wird keine vertiefte Vorkenntnis der Ingenieurmathematik vorausgesetzt. Die Behandlung von Zufallssignalen orientiert sich an technischen Anwendungen.
LMS::384

Ein Modell zur Kompensation örtlicher Wahrnehmungslücken beim menschlichen Sehen basierend auf Lokaler Spektralanalyse - Kaup, André; Heinen, S. (Mustererkennung 1994, Wien, 1994)
LMS::1001

Encoder Complexity Control in SVT-AV1 by Speed-Adaptive Preset Switching - Eichermüller, Lena; Chaudhari, Gaurang; Katsavounidis, Ioannis; Lei, Zhijun; Tmar, Hassene; Kaup, André; Herglotz, Christian (IEEE International Conference on Image Processing (ICIP), 2023)

Encoding Time and Energy Model for SVT-AV1 based on Video Complexity - Eichermüller, Lena; Chaudhari, Gaurang; Katsavounidis, Ioannis; Lei, Zhijun; Tmar, Hassene; Herglotz, Christian; Kaup, André (IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2024, 2024)
The share of online video traffic in global carbon dioxide emissions is growing steadily. To comply with the demand for video media, dedicated compression techniques are continuously optimized, but at the expense of increasingly higher computational demands and thus rising energy consumption at the video encoder side. In order to find the best trade-off between compression and energy consumption, modeling encoding energy for a wide range of encoding parameters is crucial. We propose an encoding time and energy model for SVT-AV1 based on empirical relations between the encoding time and video parameters as well as encoder configurations. Furthermore, we model the influence of video content by established content descriptors such as spatial and temporal information. We then use the predicted encoding time to estimate the required energy demand and achieve a prediction error of 19.6 % for encoding time and 20.9 % for encoding energy.
Keywords: SVT-AV1; encoding energy; encoding time; video complexity

End-To-End Deep Learning-Based Adaptation Control for Frequency-Domain Adaptive System Identification - Haubner, Thomas; Brendel, Andreas; Kellermann, Walter (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022)
We present a novel end-to-end deep learning-based adaptation control algorithm for frequency-domain adaptive system identification. The proposed method exploits a deep neural network to map observed signal features to corresponding step-sizes which control the filter adaptation. The parameters of the network are optimized in an end-to-end fashion by minimizing the average normalized system distance of the adaptive filter. This avoids the need of explicit signal power spectral density estimation as required for model-based adaptation control and further auxiliary mechanisms to deal with model inaccuracies. The proposed algorithm achieves fast convergence and robust steady-state performance for scenarios characterized by high-level, non-white and non-stationary additive noise signals, abrupt environment changes and additional model inaccuracies.<br
Keywords: System Identification; Adaptation Control; Deep Learning; Step-Size Estimation; Acoustic Echo Cancellation

End-to-End Deep Learning-Based Adaptation Control for Linear Acoustic Echo Cancellation - Haubner, Thomas; Brendel, Andreas; Kellermann, Walter (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2023)
The attenuation of acoustic loudspeaker echoes remains to be one of the open challenges to achieve pleasant full-duplex hands free speech communication. In many modern signal enhancement interfaces, this problem is addressed by a linear acoustic echo canceler which subtracts a loudspeaker echo estimate from the recorded microphone signal. To obtain precise echo estimates, the parameters of the echo canceler, i.e., the filter coefficients, need to be estimated quickly and precisely from the observed loudspeaker and microphone signals. For this a sophisticated adaptation control is required to deal with high-power double-talk and rapidly track time-varying acoustic environments which are often faced with portable devices. In this paper, we address this problem by end-to-end deep learning. In particular, we suggest to infer the step-size for a least mean squares frequency-domain adaptive filter update by a Deep Neural Network (DNN). Two different step-size inference approaches are investigated. On the one hand broadband approaches, which use a single DNN to jointly infer step-sizes for all frequency bands, and on the other hand narrowband methods, which exploit individual DNNs per frequency band. The discussion of benefits and disadvantages of both approaches leads to a novel hybrid approach which shows improved echo cancellation while requiring only small DNN architectures. Furthermore, we investigate the effect of different loss functions, signal feature vectors, and DNN output layer architectures on the echo cancellation performance from which we obtain valuable insights into the general design and functionality of DNN-based adaptation control algorithms.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Acoustic echo cancellation; Acoustics; adaptation control; Adaptation models; Adaptive filters; DNN; double-talk detection; Echo cancellers; Estimation; Loudspeakers; Microphones; step-size control; system identification

END-TO-END LEARNED LOSSY DYNAMIC POINT CLOUD ATTRIBUTE COMPRESSION - Nguyen, Dat Thanh; Zieger, Daniel; Stamminger, Marc; Kaup, André (Proceedings - International Conference on Image Processing, ICIP, 2024)
Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with 38.1% Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding.
CRIS-Team Scopus Importer:2025-04-01
Keywords: Deep Learning; Dynamic Point Cloud; G-PCC; RAHT

Energy Efficiency in Video Compression - Herglotz, Christian; Kränzler, Matthias; Ramasubbu, Geetha; Eichermüller, Lena; Kaup, André (IEEE MMTC Communications - Frontiers, 2023)

Energy Efficient Video Decoding - Herglotz, Christian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2017)
In the past years, video streaming services for mobile applications have become a popular application for consumers all over the world. Streaming services like YouTube or Netflix provide the possibility to watch movies, short films, or television (TV) programs anywhere and anytime. As mobile devices depend on batteries for energy supply, it is highly desirable to have energy efficient software and hardware such that the operating time is as high as possible. In this work, the energy needed for decoding a given coded video is addressed in detail. Measurements indicate that during streaming and playback, the major part of the energy the device consumes is needed for the decoding process if a software decoder is used. Hence, research aiming at reducing this energy is a worthwhile task. In this respect, three major topics are addressed in this work. The first topic deals with the energy analysis of practical decoding systems. Therefore, a measurement setup is constructed and discussed which is able to perform real energy measurements for various decoders and codecs. The results are used to compare the energy efficiency such that optimal decoders can be found in state-of-the-art decoder devices and implementations. The second topic addresses the modeling of this decoding energy. Therefore, the coded videos are analyzed for properties like resolution, bitrate, and employed compression tools. Then, the relation between these properties and the decoding energy is evaluated. As a result, it is shown that these properties can be used to accurately estimate the complete decoding energy of a given coded bit stream. In a practical application, these estimations can be used to give an early feedback on the remaining operating time of the device. Furthermore, these models can be used to determine the energy consumption that is related to certain compression tools. With this information, the energy efficiency of the tools can be compared. Finally, the third topic deals with another application for such models. It is shown that they can be used to encode bit streams that consume less energy on the decoder side. Therefore, an encoder is developed that takes the decoding energy as estimated by the models into account. As a result, it is shown that at the expense of a bitrate increase of 15%, the decoding energy can be reduced by 15% on average. Highest savings are shown to reach 25%.
Keywords: Videlbearbeitung; Energieeffizienz

Energy Efficient Video Decoding for VVC Using a Greedy Strategy Based Design Space Exploration - Kränzler, Matthias; Herglotz, Christian; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2022)
IP traffic has increased significantly in recent years, and it is expected that this progress will continue. Recent studies report that the viewing of online video content accounts for a share of 1% of the global greenhouse gas emissions. To reduce the data traffic of video streaming, the new standard Versatile Video Coding (VVC) has been finalized in 2020. In this paper, the energy efficiency of two different VVC decoders is analyzed in detail. Furthermore, we propose a design space exploration that uses an algorithm based on a greedy strategy to derive coding tool profiles that optimize the energy demand of the decoder. We show that the algorithm derives optimal coding tool profiles for a subset of coding tools. Additionally, we propose profiles that reduce the energy demand of VVC decoders and provide energy savings of more than 50% for sequences with 4K resolution. Thereby, we will also show that the proposed profiles can have a lower decoding energy demand than comparable HEVC-encoded bit streams while also having a significantly lower bit rat
Keywords: Complexity; Optimization; Energy Efficiency; Video; Coding Tools; VVC; HEVC

Enhanced adaptive Volterra filtering by automatic attenuation of memory regions and its application to acoustic echo cancellation - Azpicueta-Ruiz, Luis A.; Zeller, Marcus; Figueiras-Vidal, Anibal R.; Kellermann, Walter; Arenas-Garcia, Jeronimo (IEEE Transactions on Signal Processing, 2013)
This paper presents a novel scheme for nonlinear acoustic echo cancellation based on adaptive Volterra Filters with linear and quadratic kernels, which automatically prefers those diagonals contributing most to the output of the quadratic kernel with the goal of minimizing the overall mean-square error. In typical echo cancellation scenarios, not all coefficients will be equally relevant for the modeling of the nonlinear echo, but coefficients close to the main diagonal of the second-order kernel will describe most of the nonlinear echo distortions, such that not all diagonals need to be implemented. However, it is difficult to decide the most appropriate number of diagonals a priori, since there are many factors that influence this decision, such as the energy of the nonlinear echo, the shape of the room impulse response, or the step size used for the adaptation of kernel coefficients. Our proposed scheme incorporates adaptive scaling factors that control the influence of each group of adjacent diagonals contributing to the quadratic kernel output. An appropriate selection of these factors serves to emphasize or neglect diagonals of the model as required by the present situation. We provide adaptation rules for these factors based on previous works on combination of adaptive filters, and comprehensive simulations showing the reduced gradient noise reached by the new echo canceller. © 1991-2012 IEE
Keywords: Combination of filters; combination of kernels; nonlinear acoustic echo cancellation; Volterra filters

Enhanced chroma feature extraction from HE-AAC encoder - Fink, Marco; Biswas, Arijit; Kellermann, Walter (Audio Engineering Society Convention, 2012)
A perceptually enhanced chroma feature extraction during the HE-AAC audio encoding process is proposed. Extraction of chroma features from the MDCT-domain spectra of the encoder and its further enhancement utilizing the perceptual model of the encoder is investigated. The main advantage of such a scheme is a reduced computational complexity when both chroma feature extraction and encoding is desired. Specifically, the system is designed to produce reliable chroma features irrespective of the block switching decision of the encoder. Three methods are discussed to circumvent the poor frequency resolution during short blocks. All proposed enhancements are evaluated systematically within a well-known state-of-the-art chord recognition framewor

Enhanced Color Palette Modeling for Lossless Screen Content Compression - Och, Hannah; Uddehal, Shabhrish Reddy; Strutz, Tilo; Kaup, André (2024 IEEE International Conference on Acoustics, Speech and Signal Processing, 2024)

Enhanced Image Reconstruction From Quarter Sampling Measurements Using an Adapted Very Deep Super Resolution Network - Grosche, Simon; Fischer, Kristian; Brand, Fabian; Seiler, Jürgen; Kaup, André (IEEE International Conference on Image Processing (ICIP), 2020)

Enhanced robot audition by dynamic acoustic sensing in moving humanoids - Tourbabin, Vladimir; Barfuß, Hendrik; Rafaely, Boaz; Kellermann, Walter (IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2015)
Auditory systems of humanoid robots usually acquire the surrounding sound field by means of microphone arrays. These arrays can undergo motion related to the robot's activity. The conventional approach to dealing with this motion is to stop the robot during sound acquisition. This approach avoids changing the positions of the microphones during the acquisition and reduces the robot's ego-noise. However, stopping the robot can interfere with the naturalness of its behaviour. Moreover, the potential performance improvement due to motion of the sound acquiring system can not be attained. This potential is analysed in the current paper. The analysis considers two different types of motion: (i) rotation of the robot's head and (ii) limb gestures. The study presented here combines both theoretical and numerical simulation approaches. The results show that rotation of the head improves the high-frequency performance of the microphone array positioned on the head of the robot. This is complemented by the limb gestures, which improve the low-frequency performance of the array positioned on the torso and limbs of the robo
Keywords: acoustic sensing; DoA estimation; Humanoid robots; robomorphic array; robot audition

Enhancement Layer Coding for Chroma Sub-Sampled Screen Content Video - Heindel, Andreas; Prestele, Benjamin; Gehlert, Alexander; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2021)
Prevalent video codec implementations deployed in the field often solely support chrominance sub-sampled video data represented by the YCbCr 4:2:0 format. For certain applications like screen sharing, however, chroma sub-sampling leads to disturbing artifacts, especially for text or graphics with thin lines. It is desirable to reduce these artifacts while maintaining compatibility with all user devices. For this reason, an enhancement layer coding framework for chroma-format scalable coding with specific focus on screen content is proposed in this paper. Based on an analysis of screen content data characteristics, the enhancement layer codec is optimized specifically for this content class, is of low algorithmic complexity, and applicable with any image or video codec for base layer compression in a joint coding system. The system is intentionally not designed as a general purpose lossless YCbCr 4:4:4 coding scheme, but instead chooses to close a quality gap that prevalent video codecs do not address. Experimental analysis reveals an average BD-PSNR gain in the RGB domain of 1.0 dB comparing the proposed two-layer scalable coding approach to single layer compression using base layer coding only. In relation to simulcast coding, an average BDR RGB difference of 8.1% is observed.
CRIS-Team Scopus Importer:2021-04-19
Keywords: chroma sub-sampling; Decoding; Encoding; enhancement layer; Hardware; HEVC; Image coding; Receivers; scalable coding; Standards; Video codecs; Video coding

Entropy-Constrained Design of Quadtree Video Coding Schemes - Wiegand, Thomas; Flierl, Markus; Girod, Bernd (Proceedings 6th International Conference on Image Processing and its Applications (IPA 97), 1997)
LMS::394

Entropy-Constrained Linear Vector Prediction for Motion-Compensated Video Coding - Wiegand, Thomas; Flierl, Markus; Girod, Bernd (International Symposium on Information Theory, 1998)
LMS::343

Entwurf dreidimensionaler digitaler Geschwindigkeitsfilter zur Separation unterschiedlicher Geschwindigkeitskomponenten eines Signals - Runze, Gerhard (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2005)

Equalization for FBMC/OQAM Transmission over Frequency-Selective MIMO Channels - Oriold, Daniel (2014)

Error Concealment for SNR Scalable Video Coding in Wireless Communication - Kaup, André (Visual Communications and Image Processing 2000, SPIE, 2000)
LMS::464

Error control and concealment of JPEG2000 coded image data in error prone environments - Meisinger, Katrin; Garbas, Jens-Uwe; Kaup, André (Picture Coding Symposium 2004, 2004)
In this contribution we investigate the error robustness tools offered by the JPEG2000 coding standard. Error robustness is supported on entropy coding level and packet level. A synchronization loss caused by a packet loss can be prevented if marker segments are inserted into the code stream. However, a packet loss and the related loss of wavelet coefficients causes a severe visual distortion. Therefore, a frequency selective extrapolation technique of the correctly received surrounding wavelet coefficients is investigated in order to conceal the effect of data loss successfully. The resulting decoder is robust against packet losses and allows visually convincing concealment of distortions.

Erste Erfahrungen mit dem Energy Kernel System zur energetischen Gebäudesimulation - Rabenstein, Rudolf (Tagungsband des Dritten Nationalen Symposiums Thermische Solarenergie, 1993)

Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies - Hümmer, Christian; Hofmann, Christian; Maas, Roland; Kellermann, Walter (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2018)
I this paper, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach to estimate the statistics of a latent state vector capturing the relevant information of a nonlinear system. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary elitist-particle selection scheme which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this paper, we propose two advancements of the previously published elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter: First, we consider the univariate nonstationary growth model with time-variant latent state variable to evaluate the tracking capabilities of the EPFES for instantaneously calculated particle weights. This is followed by addressing the problem of single-channel nonlinear acoustic echo cancellation as a challenging benchmark task for identifying an unknown system of large search space: the nonlinear acoustic echo path is modeled by a cas cade of a parameterized preprocessor (to model the loudspeaker signal distortions) and a linear FIR filter (to model the sound wave propagation and the microphone). By using long-term information, we highlight the efficacy of the well-generalizing EPFES in estimating the preprocessor parameters for a simulated scenario and a real smartphone recording. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of researc
Keywords: State-space model; nonlinear parameter estimation; elitist particles; particle filter; evolutionary strategies

Estimating Software and Hardware Video Decoder Energy Using Software Decoder Profiling - Kränzler, Matthias; Kaup, André; Herglotz, Christian (2023 36th SBC/SBMicro/IEEE/ACM Symposium on Integrated Circuits and Systems Design (SBCCI), 2023)

Estimating The HEVC Decoding Energy Using High-Level Video Features - Herglotz, Christian; Kaup, André (Proc. of European Signal Processing Conference (EUSIPCO), 2015)
This paper shows how the decoding energy of HEVC software decoders can be estimated when using high-level features of a coded bit stream. The investigated features comprise number of frames, resolution, bitrate, QP, and encoder configuration, where the proposed model reaches an average estimation error of 10%. While establishing this model, we closely investigated the influence of these high-level features on the decoding energy. Mathematical relations are derived that can, e.g., be exploited to control the decoding energy from the encoder side. To show the validity of our research, evaluations are performed on two different hardware devices and three different software solution
UnivIS-Import:2015-10-26:Pub.2015.tech.IE.LEN.estima_5
Keywords: GRK-1773

Estimating the HEVC Decoding Energy Using the Decoder Processing Time - Herglotz, Christian; Walencik, Elisabeth; Kaup, André (Proc. of IEEE Int. Symp. on Circuits and Systems (ISCAS), 2015)
This paper presents a method to accurately estimate the required decoding energy for a given HEVC software decoding solution. We show that the decoder's processing time as returned by common C++ and UNIX functions is a highly suitable parameter to obtain valid estimations for the actual decoding energy. We verify this hypothesis by performing an exhaustive measurement series using different decoder setups and video bit streams. Our findings can be used by developers and researchers in the search for new energy saving video compression algorithm
UnivIS-Import:2015-10-26:Pub.2015.tech.IE.LEN.estima_7
Keywords: GRK-1773

Estimating the Number of Sources for Frequency-Domain Blind Source Separation - Sawada, H.; Winter, Stefan; Mukai, R.; Araki, S.; Makino, Shoji (Proc. ICA - Independent Component Analysis and Blind Source Separation, 2004)
LMS::529

Estimating Video Decoding Energies And Processing Times Utilizing Virtual Hardware - Berschneider, Sebastian; Herglotz, Christian; Reichenbach, Marc; Fey, Dietmar; Kaup, André (3PMCES Workshop. Design, Automation & Test in Europe (DATE), 2014)
The market for embedded devices increases permanently. Especially cell- and smartphones, which are substantial tools for many people, become more and more complex and serve nowadays as portable computers. An important problem to these devices is the energy efficiency. The accumulator battery can be discharged within a few hours, especially when a smartphone processes computationally intensive tasks like video decoding. Therefore, modern devices tend to include power efficient processors. But not only power efficient hardware effects the overall power consumption, also the design of algorithms regarding energy efficient programming is an important task. Usually, energy efficient development is done using real hardware, where programs are executed and power consumption is measured. This process is highly costly and error prone. Moreover, expensive hardware equipment is necessary. Therefore, in this work we present a design methodology that enables to run the application software on virtual hardware (CPU) that counts the instructions and memory accesses. By multiplying a priorly measured energy and time per instruction to these counts, energy and time estimations are possible, without having to run the target application on real hardware. As a result, we present a methodology for writing embedded applications with immediate feedback about these non-functional propertie

Estimation of acoustic reflection coefficients through pseudospectrum matching - Markovic, D.; Kowalczyk, Konrad; Antonacci, Fabio; Hofmann, Christian; Sarti, A.; Kellermann, Walter (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2014)
Estimating the geometric and reflective properties of the environment is important for a wide range of applications of space-time audio processing, from acoustic scene analysis to room equalization and spatial audio rendering. In this manuscript, we propose a methodology for frequency-subband in-situ estimation of the reflection coefficients of planar surfaces. This is a rather challenging task, as the reflection coefficients depend on the frequency and the angle of incidence and their estimate is highly sensitive to background noise and interfering sources. Our method is based on the assumption that we know the geometry of the reflectors; the position and the radiation pattern of the source; the position and the spatial response of the array. Applying beamforming algorithms on a single set of measured sensor data, we estimate the angular distribution of the acoustic energy (angular pseudospectrum) that impinges on a microphone array. We then apply a two-step iterative estimation technique based on an Expectation-Maximization (EM) algorithm. The first step estimates the scaling factors. The second one infers the reflection coefficients from the scaling factors. Under the assumption of additive white Gaussian noise, we finally determine the reflection coefficients with a Maximum Likelihood (ML) estimation method. The effectiveness and the accuracy of the proposed technique are assessed through experiments based on measured dat
Keywords: algorithm; beamforming; expectation maximization; Microphone array; pseudospectrum matching; reflection coefficient; reverberation

Estimation Of Amplitude Modifications before SCS Watermark Detection - Eggers, Joachim; Bäuml, R.; Girod, Bernd (Proceedings of SPIE: Electronic Imaging 2002, Security and Watermarking of Multimedia Contents IV, 2002)
LMS::132

Estimation of Motion Vector Fields for Multiscale Motion Compensation - Horn, Uwe; Girod, Bernd (Picture Coding Symposium (PCS 97), 1997)
LMS::432

Estimation of Non-functional Properties for Embedded Hardware with Application to Image Processing - Herglotz, Christian; Seiler, Jürgen; Kaup, André; Hendricks, A.; Reichenbach, Marc; Fey, Dietmar (22nd Reconfigurable Architectures Workshop (RAW) on the 29th Annual International Parallel & Distributed Processing Symposium (IPDPS), 2015)
In recent years, due to a higher demand for portable devices, which provide restricted amounts of processing capacity and battery power, the need for energy and time efficient hard and software solutions has increased. Preliminary estimations of time and energy consumption can thus be valuable to improve implementations and design decisions. To this end, this paper presents a method to estimate the time and energy consumption of a given software solution, without having to rely on the use of a traditional Cycle Accurate Simulator (CAS). Instead, we propose to utilize a combination of high-level functional simulation with a mechanistic extension to include non-functional properties: Instruction counts from virtual execution are multiplied with corresponding specific energies and times. By evaluating two common image processing algorithms on an FPGA-based CPU, where a mean relative estimation error of 3% is achieved for cache less systems, we show that this estimation tool can be a valuable aid in the development of embedded processor architectures. The tool allows the developer to reach well-suited design decisions regarding the optimal processor hardware configuration for a given algorithm at an early stage in the design proces
Keywords: CPU; Energy; non-functional properties; OVP; Sparc LEON3; Time

Estimation of Physical Disparity Fields and their Discontinuities for Stereoscopic Images - Aach, T.; Kaup, André (European Workshop on 3D-TV Signal Processing: From Capture to Display, Rennes, 1992)
LMS::1017

Eurasip Journal on Advances in Signal Processing: Digital signal processing for hearing instruments - Goeckler, Heinz G.; Puder, Henning; Fastl, Hugo; Nordholm, Sven Erik; Dau, Torsten; Kellermann, Walter (EURASIP Journal on Advances in Signal Processing, 2009)

Evaluation of a high dynamic range video camera with non-regular sensor - Schöberl, Michael; Keinert, J.; Ziegler, M.; Seiler, Jürgen; Niehaus, M.; Schuller, G.; Kaup, André; Foessel, S. (SPIE Electronic Imaging - Digital Photography IX, 2013)
Although there is steady progress in sensor technology, imaging with a high dynamic range (HDR) is still difficult for motion imaging with high image quality. This paper presents our new approach for video acquisition with high dynamic range. The principle is based on optical attenuation of some of the pixels of an existing image sensor. This well known method traditionally trades spatial resolution for an increase in dynamic range. In contrast to existing work, we use a non-regular pattern of optical ND filters for attenuation. This allows for an image reconstruction that is able to recover high resolution images. The reconstruction is based on the assumption that natural images can be represented nearly sparse in transform domains, which allows for recovery of scenes with high detail. The proposed combination of non-regular sampling and image reconstruction leads to a system with an increase in dynamic range without sacrificing spatial resolution. In this paper, a further evaluation is presented on the achievable image quality. In our prototype we found that crosstalk is present and significant. The discussion thus shows the limits of the proposed imaging syste
LMS::1287

Evaluation of Signal Enhancement Algorithms for Hearing Instruments - Eneman, K.; Luts, H.; Wouters, J.; Büchler, M.; Dillier, N.; Dreschler, W.; Froehlich, M.; Grimm, G.; Hohmann, V.; Houben, R.; Leijon, A.; Lombard, Anthony; et al. (16th European Signal Processing Conference, 2008)
In the frame of the HearCom1 project five promising signal enhancement algorithms are validated for future use in hearing instrument devices. To assess the algorithm performance solely based on simulation experiments, a number of physical evaluation measures have been proposed that incorporate basic aspects of normal and impaired human hearing. Additionally, each of the algorithms has been implemented on a common real-time hardware/software platform, which facilitates a profound subjective validation of the algorithm performance. Recently, a multicenter study has been set up across five different test centers in Belgium, the Netherlands, Germany and Switzerland to perceptually evaluate the selected signal enhancement approaches with normally hearing and hearing impaired listener
LMS::749

Evaluation of Video Coding for Machines Without Ground Truth - Fischer, Kristian; Hofbauer, Markus; Kuhn, Christopher; Steinbach, Eckehard; Kaup, André (2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022)
In the emerging field of video coding for machines, video datasets with pristine video quality and high-quality annota-tions are required for a comprehensive evaluation. However, existing video datasets with detailed annotations are severely limited in size and video quality. Thus, current methods have to either evaluate their codecs on still images or on already compressed data. To mitigate this problem, we propose an evaluation method based on pseudo ground-truth data from the field of semantic segmentation to the evaluation of video coding for machines. Through extensive evaluation, this paper shows that the proposed ground-truth-agnostic evaluation method results in an acceptable absolute measurement error below 0.7 percentage points on the Bjøntegaard Delta Rate compared to using the true ground truth for midrange bitrates. We evaluate on the three tasks of semantic segmentation, instance segmentation, and object detection. Lastly, we utilize the ground-truth-agnostic method to measure the coding performances of the VVC compared against HEVC on the Cityscapes sequences. This reveals that the coding position has a significant influence on the task performance. <br
Keywords: Video Coding for Machines; VVC; Semantic/Instance Segmentation; Pseudo Ground Truth

Evolutionary adaptive filtering based on competing filter structures - Zeller, Marcus; Kellermann, Walter (19th European Signal Processing Conference, EUSIPCO 2011, 2011)
This paper presents a novel filtering scheme that realizes a general, fully adaptive structure where both coefficients and required memory size are identified automatically. In particular, no distinction between linear or nonlinear models is made, since the filter structure can evolve into either a linear or a second-order Volterra filter. This is achieved by monitoring the mixing variables of various combinations where differently-sized competing filters are used. Using a set of intuitive rules along with desired step sizes for memory size changes, a dynamically growing/shrinking model structure is realized. The effectiveness of the approach for a fast-converging identification of arbitrary unknown systems is shown by means of an acoustic echo cancellation task where realistic linear and nonlinear systems as well as stationary and nonstationary input signals are considered. © 2011 EURASI

Evolutionary resampling for multi-target tracking using probability hypothesis density filter - Halimeh, Mhd Modar; Brendel, Andreas; Kellermann, Walter (European Signal Processing Conference (EUSIPCO), 2018)
A resampling scheme is proposed for use with Sequential Monte Carlo (SMC)-based Probability Hypothesis Density(PHD) filters. It consists of two steps, first, regions of interest are identified, then an evolutionary resampling is applied for each region. Applying resampling locally corresponds to treating each target individually, while the evolutionary resampling introduces a memory to a group of particles, increasing the robustness of the estimation against noise outliers. The proposed approach is compared to the original SMC-PHD filter for tracking multiple targets in a deterministically moving targets scenario, and a noisy motion scenario. In both cases, the proposed approach provides more accurate estimates.
Keywords: Multi-target tracking; nonlinear systems; Particle filters

Excitation-dependent stepsize control of adaptive Volterra filters for acoustic echo cancellation - Contan, Cristian; Zeller, Marcus; Kellermann, Walter; Topa, Marina (20th European Signal Processing Conference, EUSIPCO 2012, 2012)
This paper proposes a new updating technique for adaptive Volterra kernels employed as nonlinear models for the identification of unknown feedback paths in acoustic nonlinear echo cancellation tasks. Considering that nonlinear distortions are mainly introduced for high input levels, the effective step size is shaped according to the instantaneous envelope of the excitation signal. The loudspeaker-enclosure-microphone setup is modeled using measured linear and quadratic Volterra kernels. The efficiency of the proposed method is compared to the one of the second-order Volterra filter that uses only the Normalized Least-Mean-Square algorithm for kernel adaptation in terms of Echo Return Loss Enhancement. Simulations for different input signals show a superior behavior of the new approach in terms of convergence speed, considering the same steady-state error. © 2012 EURASI
Keywords: acoustic echo cancellation; adaptive algorithms; nonlinear distortions; Volterra filters

Experimente zur Montgomery-Selbstabbildung - Knuppertz, H.; Jahns, J.; Lohmann, Adolf (104. Jahrestagung der DGaO, Münster, 2003)
LMS::658

Exploiting microphone array symmetry for robust two-dimensional polynomial beamforming - Barfuß, Hendrik; Bachmann, Markus; Hümmer, Christian; Kellermann, Walter (International Workshop on Acoustic Signal Enhancement (IWAENC), 2018)
Polynomial beamforming is an effective approach for flexible beam steering of a data-independent beamformer. To this end, the beamformer is designed for a finite set of predefined Prototype Look Directions (PLDs) and its main beam is steered by polynomial interpolation between those PLDs. In this work, the complexity of a recently proposed two-dimensional robust polynomial beamformer is significantly decreased by exploiting symmetry planes of the microphone array to reduce the number of PLDs used for the beamformer design. This concept is experimentally verified for a planar uniform circular microphone array by means of signal-independent measure
Keywords: Robust superdirective beamforming; polynomial beamforming; array symmetry; white noise gain

Exploiting Narrowband Efficiency for Broadband Convolutive Blind Source Separation - Aichner, Robert; Buchner, Herbert; Kellermann, Walter (EURASIP Journal on Advances in Signal Processing, 2006)
Based on a recently presented generic broadband blind source separation (BSS) algorithm for convolutive mixtures, we propose in this paper a novel algorithm combining advantages of broadband algorithms with the computational efficiency of narrowband techniques. By selective application of the Szegö theorem which relates properties of Toeplitz and circulant matrices, a new normalization is derived as a special case of the generic broadband algorithm. This results in a computationally efficient and fast converging algorithm without introducing typical narrowband problems such as the internal permutation problem or circularity effects. Moreover, a novel regularization method for the generic broadband algorithm is proposed and subsequently also derived for the proposed algorithm. Experimental results in realistic acoustic environments show improved performance of the novel algorithm compared to previous approximation
LMS::635

Exploiting spatial information with the informed complex-valued spatial autoencoder for target speaker extraction - Briegleb, Annika; Halimeh, Mhd Modar; Kellermann, Walter (2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023)
In conventional multichannel audio signal enhancement, spatial and spectral filtering are often performed sequentially. In contrast, it has been shown that for neural spatial filtering a joint approach of spectro-spatial filtering is more beneficial. In this contribution, we investigate the spatial filtering performed by such a time-varying spectro-spatial filter. We extend the recently proposed complex-valued spatial autoencoder (COSPA) for the task of target speaker extraction by leveraging its interpretable structure and purposefully informing the network of the target speaker’s position. We show that the resulting informed COSPA (iCOSPA) effectively and flexibly extracts a target speaker from a mixture of speakers. We also find that the proposed architecture is well capable of learning pronounced spatial selectivity patterns and show that the results depend significantly on the training target and the reference signal when computing various evaluation metrics.<br
Keywords: speaker extraction; spectro-spatial filtering; training targets; DNN

Exploiting the Self-Steering Capability of Blind Source Separation to Localize Two or More Sound Sources in Adverse Environments - Lombard, Anthony; Rosenkranz, T.; Buchner, Herbert; Kellermann, Walter (Proc. ITG Conference on Speech Communication, 2008)
Blind Source Separation (BSS) algorithms have often been interpreted as a set of blind adaptive beamformers. Although this interpretation does not entirely hold under realistic conditions, it gives some useful insights on the self-steering capacity of BSS techniques. Actually, while accurate source location information is usually necessary to steer a beamformer, BSS offers the possibility to recover the original source signals from a reverberant sound mixture without this prior knowledge. Intuitively, this self-steering capacity should therefore imply that the BSS demixing filters contain some useful information on the location of each source. In this paper, we discuss the relations between BSS and beamforming techniques, based on the general form of the ideal separation solution. Two possible ways to extract the location information from the BSS demixing system are then presented. Experiments using a broadband BSS algorithm based on the TRINICON framework demonstrate the efficiency of the proposed methods in a highly reverberant and noisy environment, for up to six simultaneously active speech source
LMS::744

Exploring sauna impulse responses - Prawda, Karolina; Meyer-Kahlen, Nils; Schlecht, Sebastian (INTER-NOISE and NOISE-CON Congress and Conference Proceedings, 2024)
Sauna is an important element of Finnish tradition and culture, and its properties and features deserve preservation and archiving. Additionally, extreme temperature and humidity in a sauna comprise a unique environment for researching room acoustics in unusual atmospheric conditions. In the present study, we publish a dataset of room impulse response (RIR) measurements of a sauna during the process of warming up and throwing water on the stove. We explore several features of the dataset, such as the impact of the temperature and humidity distributions on the time-of-arrival of reflections, the atmospheric absorption of sound, and spectral coloration of measured RIRs. The results of this research show the influence of environmental factors on RIRs and allow an insight into the distinct room acoustics of a sauna while heating u

Extended Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data - Wokusch, Daniela (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2021)