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IEEE Transactions on Radar Systems 2024年 2卷 1007-1026页

作者： Toth, Mate Leitinger, Erik Witrisal, Klaus Graz University of Technology Institute of Communication Networks and Satellite Communications Graz Austria Graz University of Technology Laboratory of Signal Processing and Speech Communication Graz Austria

Algorithms for mutual interference mitigation and object parameter estimation are a key enabler for automotive applications of frequency-modulated continuous wave (FMCW) radar. In this paper, we introduce a signal separation method to detect and estimate radar object parameters while jointly estimating and successively canceling the interference signal. The underlying signal model poses a challenge, since both the coherent radar echo and the non-coherent interference influenced by individual multipath propagation channels must be considered. Under certain assumptions, the model is described as a superposition of multipath channels weighted by parametric interference chirp envelopes. Inspired by sparse Bayesian learning (SBL), we employ an augmented probabilistic model that uses a hierarchical Gamma-Gaussian prior model for each multipath channel. Based on this, an iterative inference algorithm is derived using the variational expectation-maximization (EM) methodology. The algorithm is statistically evaluated in terms of object parameter estimation accuracy and robustness, indicating that it is fundamentally capable of achieving the Cramer-Rao lower bound (CRLB) with respect to the accuracy of object estimates and it closely follows the radar performance achieved when no interference is present. © 2023 IEEE.

关键词： Radar interference

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On the Role of Priors in Bayesian Causal Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1439-1445页

作者： Geiger, Bernhard C. Kern, Roman Graz University of Technology Signal Processing and Speech Communication Laboratory Graz8010 Austria Know Center Research GmbH Graz8010 Austria Graz University of Technology Institute of Machine Learning and Neural Computation Graz8010 Austria

In this work, we investigate causal learning of independent causal mechanisms (ICMs) from a Bayesian perspective. Confirming previous claims from the literature, we show in a didactically accessible manner that unlab.led data (i.e., cause realizations) do not improve the estimation of the parameters defining the mechanism. Furthermore, we observe the importance of choosing an appropriate prior for the cause and mechanism parameters, respectively. Specifically, we show that a factorized prior results in a factorized posterior, which resonates with Janzing and Schölkopf's definition of ICMs via the Kolmogorov complexity of the involved distributions and with the concept of parameter independence of Heckerman et al. © 2020 IEEE.

关键词： Contrastive Learning

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Uncertainty prediction for prominence classification with chroma features

Uncertainty prediction for prominence classification with ch...

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2025 IEEE International Conference on Acoustics, speech, and signal processing, ICASSP 2025

作者： Linke, Julian Steger, Sophie Steinwender, Philipp Kubin, Gernot Pernkopf, Franz Schuppler, Barbara Signal Processing and Speech Communication Laboratory Graz University of Technology Austria

ISBN: (纸本)9798350368741

This paper presents methods for prominence classification in conversational speech. Most existing tools rely on prosodic features extracted at syllab.e- or phone-level, performing well on read speech. This is not the case for conversational speech, where the quality of automatic segmentation is significantly worse. We introduce entropy-based chroma features, requiring only word-level segmentations. They perform equally well as a random forest classifier with prosodic features (requiring phone-level segmentation), with accuracies in the range of the human inter-rater agreement. We further use Bayesian deep learning to quantify the epistemic and aleatoric uncertainty of the prediction for prosodic and chroma features. Whereas the aleatoric uncertainty is, as expected, consistent with inter-rater agreement and similarly high for both feature sets, the epistemic uncertainty is lower for the classifier based on chroma features, indicating higher classification consistency across the corpus. © 2025 IEEE.

关键词： Austrian German chromagram conversational speech prosodic prominence uncertainty prediction

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Single Channel Source Separation in the Wild – Conversational speech in Realistic Environments 15

Single Channel Source Separation in the Wild – Conversation...

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15th ITG Conference on speech communication

作者： Berger, Emil Schuppler, Barbara Hagmüller, Martin Pernkopf, Franz Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria

ISBN: (纸本)9783800761654

Recent progress in Single Channel Source Separation (SCSS) using deep neural networks led to impressive performance gains while also increasing the model sizes, requiring tremendous data resources. This demand is covered by artificially composed speech and noise mixtures that do not capture real-life characteristics of conversations taking place in noisy environments. This paper introduces a new dataset containing task-oriented dialogues spoken in a realistic environment and presents experimental results for two SCSS architectures - the Conv-TasNet and the transformer-based MossFormer. Overall, we observe a severe drop in performance of up to 4.3dB (SI-SDR improvement) for the 8kHz variant of the Conv-TasNet. For speaker pairs of homogeneous sex, the difference is even higher of up to 6dB. Only the model using 16kHz sample rate performs on a comparable level for speaker pairs of mixed sex. Our findings illustrate the need of using realistic data for both, training and evaluating. © VDE VERLAG GMBH Berlin Offenbach.

关键词： Deep neural networks

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Reliable Belief Propagation: Recent Theoretical and Practical Advances 33

Reliable Belief Propagation: Recent Theoretical and Practica...

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33rd IEEE International Workshop on Machine Learning for signal processing, MLSP 2023

作者： Knoll, Christian Pernkopf, Franz Graz University of Technology Signal Processing and Speech Communication Laboratory Austria

ISBN: (纸本)9798350324112

Belief propagation (BP) is an effective approximate inference method but lacks theoretical guarantees for loopy graphs. We discuss the optimization landscape and the message dynamics and how this helps to understand the behavior of message passing algorithms. These insights suggest several improvements. Specifically, we consider iterative initialization strategies, optimized message scheduling methods, and structural modifications, to improve the convergence behavior and accuracy while maintaining the model's interpretability. We then evaluate the different modifications on signal detection problems in MIMO systems, which is a particularly challenging application for message passing algorithms. Our experimental results show consistent improvements over standard BP with minimal increase in computational burden. © 2023 IEEE.

关键词： Belief propagation

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Exploring Phonetic Features in Language Embeddings for Unseen Language Varieties of Austrian German 20

Exploring Phonetic Features in Language Embeddings for Unsee...

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20th Conference on Natural Language processing, KONVENS 2024

作者： Gutscher, Lorenz Pucher, Michael Signal Processing and Speech Communication Laboratory Graz University of Technology Austrian Research Institute for Artificial Intelligence Vienna Austria

Vectorized language embeddings of raw audio data improve tasks like language recognition, automatic speech recognition, and machine translation. Although embeddings exhibit high effectiveness in their respective tasks, unraveling explicit information or meaning encapsulated within the embeddings proves challenging. This study investigates a multilingual model’s ability to capture features from phonetic, articulatory, variety, and speaker categories from brief audio segments comprising five consecutive phones spoken by Austrian speakers. Within the employed model for extraction, German serves as one of the pre-trained languages used. However, the manner in which the model processes Austrian varieties presents an intriguing area for investigation. Using a k-nearest neighbor classifier, it is tested whether the encoded features are prominent in the embedding. While characteristics like variety are effectively classified, the accuracy of phone classification is particularly high for specific phones that are characteristic of the respective dialect/sociolect. ©2024 Association for Computational Linguistics.

关键词： Embeddings

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Self-Attention for Enhanced OAMP Detection in MIMO Systems 48

Self-Attention for Enhanced OAMP Detection in MIMO Systems

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48th IEEE International Conference on Acoustics, speech and signal processing, ICASSP 2023

作者： Fuchs, Alexander Knoll, Christian Moghadam, Nima N. Pak, Alexey Huang, Jinliang Leitinger, Erik Pernkopf, Franz Graz University of Technology Signal Processing and Speech Communication Laboratory Austria Huawei Technologies Sweden Ab

ISBN: (纸本)9781728163277

Multiple-Input Multiple-Output (MIMO) systems are essential for wireless communications. Since classical algorithms for symbol detection in MIMO setups require large computational resources or provide poor results, data-driven algorithms are becoming more popular. Most of the proposed algorithms, however, introduce approximations leading to degraded performance for realistic MIMO systems. In this paper, we introduce a neural-enhanced hybrid model, augmenting the analytic backbone algorithm with state-of-the-art neural network components. In particular, we introduce a self-attention model for the enhancement of the iterative Orthogonal Approximate Message Passing (OAMP)-based decoding algorithm. In our experiments, we show that the proposed model can outperform existing data-driven approaches for OAMP while having improved generalization to other SNR values at limited computational overhead. © 2023 IEEE.

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Angle-Equivariant Convolutional Neural Networks for Interference Mitigation in Automotive Radar 20

Angle-Equivariant Convolutional Neural Networks for Interfer...

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20th European Radar Conference, EuRAD 2023

作者： Oswald, Christian Toth, Mate Meissner, Paul Pernkopf, Franz Graz University of Technology Signal Processing and Speech Communication Laboratory Austria Infineon Technologies AG Austria

ISBN: (纸本)9782874870743

In automotive applications, frequency modulated continuous wave (FMCW) radar is an established technology to determine the distance, velocity and angle of objects in the vicinity of the vehicle. The quality of predictions might be seriously impaired if mutual interference between radar sensors occurs. Previous work processes data from the entire receiver array in parallel to increase interference mitigation quality using neural networks (NNs). However, these architectures do not generalize well across different angles of arrival (AoAs) of interferences and objects. In this paper we introduce fully convolutional neural network (CNN) with rank-three convolutions which is able to transfer learned patterns between different AoAs. Our proposed architecture outperforms previous work while having higher robustness and a lower number of trainable parameters. We evaluate our network on a diverse data set and demonstrate its angle equivariance. © 2023 European Microwave Association (EuMA).

关键词： Convolution

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Towards Improving ASR Outputs of Spontaneous speech with LLMs 20

Towards Improving ASR Outputs of Spontaneous Speech with LLM...

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20th Conference on Natural Language processing, KONVENS 2024

作者： Karner, Manuel Linke, Julian Kröll, Mark Schuppler, Barbara Geiger, Bernhard C. Know-Center GmbH Sandgasse 34 Graz8010 Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Inffeldgasse 16c Graz8010 Austria

This paper presents ongoing work towards an initial understanding of how large language models (LLMs) can assist automatic speech recognition (ASR) tasks. More concretely, we investigate if LLMs can improve hypotheses obtained from ASR systems, and if so, which patterns in the hypothesis allow for a correction. Our results show that LLMs can mainly correct syntax errors or errors caused by ASR systems splitting long words. We further find that in the majority of cases the word error rates with respect to the human annotation increase when an LLM is applied, while the semantic similarity with the human annotation improves. ©2024 Association for Computational Linguistics.

关键词： Semantics

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Investigation of the sound field in an edge of a reverberation chamber 24

Investigation of the sound field in an edge of a reverberati...

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24th International Congress on Acoustics, ICA 2022

作者： Kurzw, Eric Koch, Julian Weselak, Werner Kubin, Gernot Signal Processing and Speech Communication Laboratory Graz University of Technology Austria

Edge absorbers, commonly known as bass traps, are known for their excellent properties in absorbing low-frequency sound energy and thus in reducing low-frequency reverberation. In the simplest case, an edge absorber is a porous absorber that is placed in an edge of a room. A conventional approach is to determine the equivalent absorption area of such an absorber arrangement by means of a measurement following ISO 354. Since such a measurement usually determines equivalent absorber areas that are larger than the actual absorber surface, the absorber must be scaled accordingly when used in a conventional room acoustic simulation. Unfortunately, this approach only allows rough estimation of the reverberation reduction due to edge absorbers. A more promising and physically correct approach is to investigate the influence of the edge absorber on interference patterns as well as the modal sound field in more detail. This work is therefore devoted to the detailed investigation of sound pressure and particle velocity distributions in the edge of a reverberation chamber for a stationary modal sound field and the influence of the edge absorber on these. © 2022 Proceedings of the International Congress on Acoustics. All Rights Reserved.

关键词： Acoustic fields

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