arXiv subjects

Walter Kellermann

Publications and source records attributed to Walter Kellermann.

At least 37 records · Page 2Linked to original sources

arXiv2021

Noise-Robust Adaptation Control for Supervised Acoustic System Identification Exploiting A Noise Dictionary

We present a noise-robust adaptation control strategy for block-online supervised acoustic system identification by exploiting a noise dictionary. The proposed algorithm takes advantage of the pronounced spectral structure which characterizes many types of interfering noise signals. We model the noisy observations by a linear Gaussian Discrete Fourier Transform-domain state space model whose parameters are estimated by an online generalized Expectation-Maximization algorithm. Unlike all other state-of-the-art approaches we suggest to model the covariance matrix of the observation probability density function by a dictionary model. We propose to learn the noise dictionary from training data, which can be gathered either offline or online whenever the system is not excited, while we infer the activations continuously. The proposed algorithm represents a novel machine-learning based approach to noise-robust adaptation control which allows for faster convergence in applications characterized by high-level and non-stationary interfering noise signals and abrupt system changes.

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arXiv2020

Misalignment Recognition in Acoustic Sensor Networks using a Semi-supervised Source Estimation Method and Markov Random Fields

In this paper, we consider the problem of acoustic source localization by acoustic sensor networks (ASNs) using a promising, learning-based technique that adapts to the acoustic environment. In particular, we look at the scenario when a node in the ASN is displaced from its position during training. As the mismatch between the ASN used for learning the localization model and the one after a node displacement leads to erroneous position estimates, a displacement has to be detected and the displaced nodes need to be identified. We propose a method that considers the disparity in position estimates made by leave-one-node-out (LONO) sub-networks and uses a Markov random field (MRF) framework to infer the probability of each LONO position estimate being aligned, misaligned or unreliable while accounting for the noise inherent to the estimator. This probabilistic approach is advantageous over naive detection methods, as it outputs a normalized value that encapsulates conditional information provided by each LONO sub-network on whether the reading is in misalignment with the overall network. Experimental results confirm that the performance of the proposed method is consistent in identifying compromised nodes in various acoustic conditions.

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arXiv2020

The LOCATA Challenge: Acoustic Source Localization and Tracking

The ability to localize and track acoustic events is a fundamental prerequisite for equipping machines with the ability to be aware of and engage with humans in their surrounding environment. However, in realistic scenarios, audio signals are adversely affected by reverberation, noise, interference, and periods of speech inactivity. In dynamic scenarios, where the sources and microphone platforms may be moving, the signals are additionally affected by variations in the source-sensor geometries. In practice, approaches to sound source localization and tracking are often impeded by missing estimates of active sources, estimation errors, as well as false estimates. The aim of the LOCAlization and TrAcking (LOCATA) Challenge is an open-access framework for the objective evaluation and benchmarking of broad classes of algorithms for sound source localization and tracking. This article provides a review of relevant localization and tracking algorithms and, within the context of the existing literature, a detailed evaluation and dissemination of the LOCATA submissions. The evaluation highlights achievements in the field, open challenges, and identifies potential future directions.

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arXiv2020

Accelerating Auxiliary Function-based Independent Vector Analysis

Independent Vector Analysis (IVA) is an effective approach for Blind Source Separation (BSS) of convolutive mixtures of audio signals. As a practical realization of an IVA-based BSS algorithm, the so-called AuxIVA update rules based on the Majorize-Minimize (MM) principle have been proposed which allow for fast and computationally efficient optimization of the IVA cost function. For many real-time applications, however, update rules for IVA exhibiting even faster convergence are highly desirable. To this end, we investigate techniques which accelerate the convergence of the AuxIVA update rules without extra computational cost. The efficacy of the proposed methods is verified in experiments representing real-world acoustic scenarios.

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arXiv2020

Extrapolation of Bandlimited Multidimensional Signals from Continuous Measurements

Conventional sampling and interpolation commonly rely on discrete measurements. In this paper, we develop a theoretical framework for extrapolation of signals in higher dimensions from knowledge of the continuous waveform on bounded high-dimensional regions. In particular, we propose an iterative method to reconstruct bandlimited multidimensional signals based on truncated versions of the original signal to bounded regions---herein referred to as continuous measurements. In the proposed method, the reconstruction is performed by iterating on a convex combination of region-limiting and bandlimiting operations. We show that this iteration consists of a firmly nonexpansive operator and prove strong convergence for multidimensional bandlimited signals. In order to improve numerical stability, we introduce a regularized iteration and show its connection to Tikhonov regularization. The method is illustrated numerically for two-dimensional signals.

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arXiv2020

Online Supervised Acoustic System Identification exploiting Prelearned Local Affine Subspace Models

In this paper we present a novel algorithm for improved block-online supervised acoustic system identification in adverse noise scenarios by exploiting prior knowledge about the space of Room Impulse Responses (RIRs). The method is based on the assumption that the variability of the unknown RIRs is controlled by only few physical parameters, describing, e.g., source position movements, and thus is confined to a low-dimensional manifold which is modelled by a union of affine subspaces. The offsets and bases of the affine subspaces are learned in advance from training data by unsupervised clustering followed by Principal Component Analysis. We suggest to denoise the parameter update of any supervised adaptive filter by projecting it onto an optimal affine subspace which is selected based on a novel computationally efficient approximation of the associated evidence. The proposed method significantly improves the system identification performance of state-of-the-art algorithms in adverse noise scenarios.

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arXiv2020

Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Networks

We present an approach to deep neural network based (DNN-based) distance estimation in reverberant rooms for supporting geometry calibration tasks in wireless acoustic sensor networks. Signal diffuseness information from acoustic signals is aggregated via the coherent-to-diffuse power ratio to obtain a distance-related feature, which is mapped to a source-to-microphone distance estimate by means of a DNN. This information is then combined with direction-of-arrival estimates from compact microphone arrays to infer the geometry of the sensor network. Unlike many other approaches to geometry calibration, the proposed scheme does only require that the sampling clocks of the sensor nodes are roughly synchronized. In simulations we show that the proposed DNN-based distance estimator generalizes to unseen acoustic environments and that precise estimates of the sensor node positions are obtained.

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arXiv2020

Spatially Informed Independent Vector Analysis

We present a Maximum A Posteriori (MAP) derivation of the Independent Vector Analysis (IVA) algorithm, a blind source separation algorithm, by incorporating a prior over the demixing matrices, relying on a free-field model. In this way, the outer permutation ambiguity of IVA is avoided. The resulting MAP optimization problem is solved by deriving majorize-minimize update rules to achieve convergence speed comparable to the well-known auxiliary function IVA algorithm. The performance of the proposed algorithm is investigated and compared to a benchmark algorithm using real measurements.

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arXiv2020

A Unified Bayesian View on Spatially Informed Source Separation and Extraction based on Independent Vector Analysis

Signal separation and extraction are important tasks for devices recording audio signals in real environments which, aside from the desired sources, often contain several interfering sources such as background noise or concurrent speakers. Blind Source Separation (BSS) provides a powerful approach to address such problems. However, BSS algorithms typically treat all sources equally and do not resolve uncertainty regarding the ordering of the separated signals at the output of the algorithm, i.e., the outer permutation problem. This paper addresses this problem by incorporating prior knowledge into the adaptation of the demixing filters, e.g., the position of the sources, in a Bayesian framework. We focus here on methods based on Independent Vector Analysis (IVA) as it elegantly and successfully deals with the internal permutation problem. By including a background model, i.e., a model for sources we are not interested to separate, we enable the algorithm to extract the sources of interest in overdetermined and underdetermined scenarios at a low computational complexity. The proposed framework allows to incorporate prior knowledge about the demixing filters in a generic way and unifies several known and newly proposed algorithms using a Bayesian view. For all algorithmic variants, we provide efficient update rules based on the iterative projection principle. The performance of a large variety of representative algorithmic variants, including very recent algorithms, is compared using measured room impulse responses.

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arXiv2019

Active Acoustic Source Tracking Exploiting Particle Filtering and Monte Carlo Tree Search

In this paper, we address the task of active acoustic source tracking as part of robotic path planning. It denotes the planning of sequences of robotic movements to enhance tracking results of acoustic sources, e.g., talking humans, by fusing observations from multiple positions. Essentially, two strategies are possible: short-term planning, which results in greedy behavior, and long-term planning, which considers a sequence of possible future movements of the robot and the source. Here, we focus on the second method as it might improve tracking performance compared to greedy behavior and propose a flexible path planning algorithm which exploits Monte Carlo Tree Search (MCTS) and particle filtering based on a reward motivated by information-theoretic considerations.

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arXiv2019

Proceedings of the LOCATA Challenge Workshop -- a satellite event of IWAENC 2018

Algorithms for acoustic source localization and tracking provide estimates of the positional information about active sound sources in acoustic environments and are essential for a wide range of applications such as personal assistants, smart homes, tele-conferencing systems, hearing aids, or autonomous systems. The aim of the IEEE-AASP Challenge on sound source localization and tracking (LOCATA) was to objectively benchmark state-of-the-art localization and tracking algorithms using an open-access data corpus of recordings for scenarios typically encountered in audio and acoustic signal processing applications. The challenge tasks ranged from the localization of a single source with a static microphone array to the tracking of multiple moving sources with a moving microphone array.

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arXiv2018

Open(G)PIAS: An open source solution for the construction of a high-precision Acoustic-Startle-Response (ASR) setup for tinnitus screening and threshold estimation in rodents

The acoustic startle reflex (ASR) that can be induced by a loud sound stimulus can be used as a versatile tool to, e.g., estimate hearing thresholds or identify subjective tinnitus percepts in rodents. These techniques are based on the fact that the ASR amplitude can be suppressed by a pre-stimulus of lower, non-startling intensity, an effect named pre-pulse inhibition (PPI). For hearing threshold estimation, pure tone pre-stimuli of varying amplitudes are presented before an intense noise burst serving as startle stimulus. The amount of suppression of the ASR amplitude as a function of the pre-stimulus intensity can be used as a behavioral correlate to determine the hearing ability. For tinnitus assessment, the pure-tone pre-stimulus is replaced by a gap of silence in a narrowband noise background, a paradigm termed GPIAS (gap-pre-pulse inhibition of the acoustic startle response). A proper application of these paradigms depend on a reliable measurement of the ASR amplitudes, an exact stimulus presentation in terms of frequency and intensity. Here we introduce a novel open source solution for the construction of a low-cost ASR setup for the above mentioned purpose. The complete software for data acquisition and stimulus presentation is written in Python 3.6 and is provided as an anaconda package. Furthermore, we provide a construction plan for the sensory system based on low-cost hardware components. Exemplary data show that the ratios (1-PPI) of the pre and post trauma ASR amplitudes can be well described by a lognormal distribution being in good accordance to previous studies with already established setups. Hence, the open access solution described here will help to further establish the ASR method in many laboratories and thus facilitate and standardize research in animal models of tinnitus or hearing loss.

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arXiv2017

Broadband Multizone Sound Rendering by Jointly Optimizing the Sound Pressure and Particle Velocity

In this paper, a recently proposed approach to multizone sound field synthesis, referred to as Joint Pressure and Velocity Matching (JPVM), is investigated analytically using a spherical harmonics representation of the sound field. The approach is motivated by the Kirchhoff-Helmholtz integral equation and aims at controlling the sound field inside the local listening zones by evoking the sound pressure and particle velocity on surrounding contours. Based on the findings of the modal analysis, an improved version of JPVM is proposed which provides both better performance and lower complexity. In particular, it is shown analytically that the optimization of the tangential component of the particle velocity vector, as is done in the original JPVM approach, is very susceptible to errors and thus not pursued anymore. The analysis furthermore provides fundamental insights as to how the spherical harmonics used to describe the 3D variant sound field translate into 2D basis functions as observed on the contours surrounding the zones. By means of simulations, it is verified that discarding the tangential component of the particle velocity vector ultimately leads to an improved performance. Finally, the impact of sensor noise on the reproduction performance is assessed.

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arXiv2017

Robust coherence-based spectral enhancement for speech recognition in adverse real-world environments

Speech recognition in adverse real-world environments is highly affected by reverberation and nonstationary background noise. A well-known strategy to reduce such undesired signal components in multi-microphone scenarios is spatial filtering of the microphone signals. In this article, we demonstrate that an additional coherence-based postfilter, which is applied to the beamformer output signal to remove diffuse interference components from the latter, is an effective means to further improve the recognition accuracy of modern deep learning speech recognition systems. To this end, the recently updated 3rd CHiME Speech Separation and Recognition Challenge (CHiME-3) baseline speech recognition system is extended by a coherence-based postfilter and the postfilter's impact on the word error rates is investigated for the noisy environments provided by CHiME-3. To determine the time- and frequency-dependent postfilter gains, we use a Direction-of-Arrival (DOA)-dependent and a DOA-independent estimator of the coherent-to-diffuse power ratio as an approximation of the short-time signal-to-noise ratio. Our experiments show that incorporating coherence-based postfiltering into the CHiME-3 baseline speech recognition system leads to a significant reduction of the word error rate scores for the noisy and reverberant environments provided as part of CHiME-3.

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arXiv2017

HRTF-based two-dimensional robust least-squares frequency-invariant beamformer design for robot audition

In this work, we propose a two-dimensional Head-Related Transfer Function (HRTF)-based robust beamformer design for robot audition, which allows for explicit control of the beamformer response for the entire three-dimensional sound field surrounding a humanoid robot. We evaluate the proposed method by means of both signal-independent and signal-dependent measures in a robot audition scenario. Our results confirm the effectiveness of the proposed two-dimensional HRTF-based beamformer design, compared to our previously published one-dimensional HRTF-based beamformer design, which was carried out for a fixed elevation angle only.

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arXiv2016

Efficient Target Activity Detection based on Recurrent Neural Networks

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 TAD.

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arXiv2016

HRTF-based Robust Least-Squares Frequency-Invariant Polynomial Beamforming

In this work, we propose a robust Head-Related Transfer Function (HRTF)-based polynomial beamformer design which accounts for the influence of a humanoid robot's head on the sound field. In addition, it allows for a flexible steering of our previously proposed robust HRTF-based beamformer design. We evaluate the HRTF-based polynomial beamformer design and compare it to the original HRTF-based beamformer design by means of signal-independent measures as well as word error rates of an off-the-shelf speech recognition system. Our results confirm the effectiveness of the polynomial beamformer design, which makes it a promising approach to robust beamforming for robot audition.

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arXiv2016

An improved uncertainty decoding scheme with weighted samples for DNN-HMM hybrid systems

In this paper, we advance a recently-proposed uncertainty decoding scheme for DNN-HMM (deep neural network - hidden Markov model) hybrid systems. This numerical sampling concept averages DNN outputs produced by a finite set of feature samples (drawn from a probabilistic distortion model) to approximate the posterior likelihoods of the context-dependent HMM states. As main innovation, we propose a weighted DNN-output averaging based on a minimum classification error criterion and apply it to a probabilistic distortion model for spatial diffuseness features. The experimental evaluation is performed on the 8-channel REVERB Challenge task using a DNN-HMM hybrid system with multichannel front-end signal enhancement. We show that the recognition accuracy of the DNN-HMM hybrid system improves by incorporating uncertainty decoding based on random sampling and that the proposed weighted DNN-output averaging further reduces the word error rate scores.

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