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Bayesian Nonparametrics for microphone array processing

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2014年第2期22卷 493-504页

作者： Otsuka, Takuma Ishiguro, Katsuhiko Sawada, Hiroshi Okuno, Hiroshi G. Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan NTT Corp NTT Commun Sci Labs Kyoto 6190237 Japan NTT Corp NTT Serv Evolut Labs Kanagawa 2390847 Japan

Sound source localization and separation from a mixture of sounds are essential functions for computational auditory scene analysis. The main challenges are designing a unified framework for joint optimization and estimating the sound sources under auditory uncertainties such as reverberation or unknown number of sounds. Since sound source localization and separation are mutually dependent, their simultaneous estimation is required for better and more robust performance. A unified model is presented for sound source localization and separation based on Bayesian nonparametrics. Experiments using simulated and recorded audio mixtures show that a method based on this model achieves state-of-the-art sound source separation quality and has more robust performance on the source number estimation under reverberant environments.

关键词： Audio source separation and enhancement (AUDSSEN) Bayesian nonparametrics blind source separation microphone array processing sound source localization spatial andmultichannel audio (AUD-SMCA) time frequency masking

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Diffusion-Based Sound Source Localization Using a Distributed Network of microphone arrays

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SENSORS 2025年第7期25卷 2078-2078页

作者： Albertini, Davide Bernardini, Alberto Greco, Gioele Sarti, Augusto Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy

Traditionally, microphone array networks for 3D sound source localization rely on centralized data processing, which can limit scalability and robustness. In this article, we recast the task of sound source localization (SSL) with networks of acoustic arrays as a distributed optimization problem. We then present two resolution approaches of such a problem;one is computationally centralized, while the other is computationally distributed and based on an Adapt-Then-Combine (ATC) diffusion strategy. In particular, we address 3D SSL with a network of linear microphone arrays, each of which estimates a stream of 2D directions of arrival (DoAs) and they cooperate with each other to localize a single sound source. We develop adaptive cooperation strategies to penalize the arrays with the most detrimental effects on localization accuracy and improve performance through error-based and distance-based penalties. The performance of the method is evaluated using increasingly complex DoA stream models and simulated acoustic environments characterized by various levels of reverberation and signal-to-noise ratio (SNR). Furthermore, we investigate how the performance is related to the connectivity of the network and show that the proposed approach maintains high localization accuracy and stability even in sparsely connected networks.

关键词： sound source localization wireless acoustic sensor networks ATC diffusion microphone array processing

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STFT-Domain Neural Speech Enhancement With Very Low Algorithmic Latency

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2023年 31卷 397-410页

作者： Wang, Zhong-Qiu Wichern, Gordon Watanabe, Shinji Le Roux, Jonathan Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA

Deep learning based speech enhancement in the short-time Fourier transform (STFT) domain typically uses a large window length such as 32 ms. A larger window can lead to higher frequency resolution and potentially better enhancement. This however incurs an algorithmic latency of 32 ms in an online setup, because the overlap-add algorithm used in the inverse STFT (iSTFT) is also performed using the same window size. To reduce this inherent latency, we adapt a conventional dual-window-size approach, where a regular input window size is used for STFT but a shorter output window is used for overlap-add, for STFT-domain deep learning based frame-online speech enhancement. Based on this STFT-iSTFT configuration, we employ complex spectral mapping for frame-online enhancement, where a deep neural network (DNN) is trained to predict the real and imaginary (RI) components of target speech from the mixture RI components. In addition, we use the DNN-predicted RI components to conduct frame-online beamforming, the results of which are used as extra features for a second DNN to perform frame-online postfiltering. The frequency-domain beamformer can be easily integrated with our DNNs and is designed to not incur any algorithmic latency. Additionally, we propose a future-frame prediction technique to further reduce the algorithmic latency. Evaluation on noisy-reverberant speech enhancement shows the effectiveness of the proposed algorithms. Compared with Conv-TasNet, our STFT-domain system can achieve better enhancement performance for a comparable amount of computation, or comparable performance with less computation, maintaining strong performance at an algorithmic latency as low as 2 ms.

关键词： Speech enhancement array signal processing Prediction algorithms Time-domain analysis Deep learning Low latency communication Discrete Fourier transforms Complex spectral mapping deep learning frame-online speech enhancement microphone array processing

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Ray-Space constrained multichannel Nonnegative Matrix Factorization for Audio Source Separation 32

Ray-Space constrained multichannel Nonnegative Matrix Factor...

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32nd European Signal processing Conference (EUSIPCO)

作者： Munoz-Montoro, Antonio J. Olivieri, Marco Pezzoli, Mirco Carabias-Orti, Julio Antonacci, Fabio Sarti, Augusto Univ Jaen Dept Ingn Telecomunicac Linares Spain Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9789464593617;9798331519773

Non-negative matrix factorization (NMF) has been widely adopted for the blind separation of acoustic sources. In the context of VR and AR applications, when several microphones are available the adoption of sound field representations such as spherical harmonics or ray space is shown to be effective in the NMF context. In this work, we propose a dictionary-based NMF-based model considering ray-space-transformed signals. The novelty of this approach is to account for the explicit modelling of the frequency dependency of the sound propagation from the source positions to the sensors. Spatially-constrained approaches aim at exploiting spatial information to improve the separation performance;however, they may not take advantage of possible priors given by representations of the data. The proposed approach allows us to exploit the source location model of the Ray Space through a predefined frequency-dependent dictionary of Ray Space patterns. Results demonstrate the competitive performance of the proposed method with respect to state-of-the-art NMF-based algorithms using real recordings.

关键词： Ray Space Non-negative matrix factorization (NMF) blind source separation (BSS) microphone array processing

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NEURAL DIRECTIONAL FILTERING: FAR-FIELD DIRECTIVITY CONTROL WITH A SMALL microphone array 18

NEURAL DIRECTIONAL FILTERING: FAR-FIELD DIRECTIVITY CONTROL ...

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18th International Workshop on Acoustic Signal Enhancement (IWAENC)

作者： Wechsler, Julian Chetupalli, Srikanth Raj Halimeh, Mhd Modar Thiergart, Oliver Habets, Emanuel A. P. Int Audio Labs Erlangen Wolfsmantel 33 D-91058 Erlangen Germany

ISBN: (纸本)9798350361865;9798350361858

Capturing audio signals with specific directivity patterns is essential in speech communication. This study presents a deep neural network (DNN)-based approach to directional filtering, alleviating the need for explicit signal models. More specifically, our proposed method uses a DNN to estimate a single-channel complex mask from the signals of a microphone array. This mask is then applied to a reference microphone to render a signal that exhibits a desired directivity pattern. We investigate the training dataset composition and its effect on the directivity realized by the DNN during inference. Using a relatively small DNN, the proposed method is found to approximate the desired directivity pattern closely. Additionally, it allows for the realization of higher-order directivity patterns using a small number of microphones, which is a difficult task for linear and parametric directional filtering.

关键词： Spatial filtering directivity pattern microphone array processing

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Parametric Ambisonic Encoding of Arbitrary microphone arrays

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2022年 30卷 2062-2075页

作者： McCormack, Leo Politis, Archontis Gonzalez, Raimundo Lokki, Tapio Pulkki, Ville Aalto Univ Dept Signal Proc & Acoust Espoo 02150 Finland Tampere Univ Dept Informat Technol & Commun Sci Tampere 33100 Finland

This article proposes a parametric signal-dependent method for the task of encoding microphone array signals into Ambisonic signals. The proposed method is presented and evaluated in the context of encoding a simulated seven-sensor microphone array, which is mounted on an augmented reality headset device. Given the inherent flexibility of the Ambisonics format, and its popularity within the context of such devices, this array configuration represents a potential future use case for Ambisonic recording. However, due to its irregular geometry and non-uniform sensor placement, conventional signal-independent Ambisonic encoding is particularly limited. The primary aims of the proposed method are to obtain Ambisonic signals over a wider frequency band-width, and at a higher spatial resolution, than would otherwise be possible through conventional signal-independent encoding. The proposed method is based on a multi-source sound-field model and employs spatial filtering to divide the captured sound-field into its individual source and directional ambient components, which are subsequently encoded into the Ambisonics format at an arbitrary order. It is demonstrated through both objective and perceptual evaluations that the proposed parametric method outperforms conventional signal-independent encoding in the majority of cases.

关键词： Encoding microphone arrays Decoding array signal processing Recording Measurement Geometry microphone array processing ambisonic encoding parametric spatial audio

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Real-Time Acoustic Perception for Automotive Applications

Real-Time Acoustic Perception for Automotive Applications

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Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Yin, Jun Damiano, Stefano Verhelst, Marian van Waterschoot, Toon Guntoro, Andre Katholieke Univ Leuven ESAT MICAS Leuven Belgium Katholieke Univ Leuven ESAT STADIUS Leuven Belgium Robert Bosch GmbH Gerlingen Germany

ISBN: (纸本)9798350396249

In recent years the automotive industry has been strongly promoting the development of smart cars, equipped with multi-modal sensors to gather information about the surroundings, in order to aid human drivers or make autonomous decisions. While the focus has mostly been on visual sensors, also acoustic events are crucial to detect situations that require a change in the driving behavior, such as a car honking, or the sirens of approaching emergency vehicles. In this paper, we summarize the results achieved so far in the Marie Sklodowska-Curie Actions (MSCA) Eruopean Industrial Doctorates (EID) project "Intelligent Ultra Low-Power Signal processing for Automotive (I-SPOT)". On the algorithmic side, the I-SPOT Project aims to enable detecting, localizing and tracking environmental audio signals by jointly developing microphone array processing and deep learning techniques that specifically target automotive applications. Data generation software has been developed to cover the I-SPOT target scenarios and research challenges. This tool is currently being used to develop low-complexity deep learning techniques for emergency sound detection. On the hardware side, the goal impels workflows for hardware-algorithm co-design to ease the generation of architectures that are sufficiently flexible towards algorithmic evolutions without giving up on efficiency, as well as enable rapid feedback of hardware implications of algorithmic decision. This is pursued though a hierarchical workflow that breaks the hardware-algorithm design space into reasonable subsets, which has been tested for operator-level optimizations on state-of-the-art robust sound source localization for edge devices. Further, several open challenges towards an end-to-end system are clarified for the next stage of I-SPOT.

关键词： Acoustic Perception Sound Event Detection Sound Source Localization Embedded AI Hardware-Algorithm Co-Design Autonomous Driving microphone array processing

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Geometry-Aware DOA Estimation Using a Deep Neural Network with Mixed-Data Input Features 48

Geometry-Aware DOA Estimation Using a Deep Neural Network wi...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Kowalk, Ulrik Doclo, Simon Bitzer, Joerg Jade University of Applied Sciences Institute for Hearing Technology and Audiology Oldenburg Germany University of Oldenburg Department of Medical Physics and Acoustics Cluster of Excellence Hearing4all Oldenburg Germany

ISBN: (纸本)9781728163277

Unlike model-based direction of arrival (DoA) estimation algorithms, supervised learning-based DoA estimation algorithms based on deep neural networks (DNNs) are usually trained for one specific microphone array geometry, resulting in poor performance when applied to a different array geometry. In this paper we illustrate the fundamental difference between supervised learning-based and model-based algorithms leading to this sensitivity. Aiming at designing a supervised learning-based DoA estimation algorithm that generalizes well to different array geometries, in this paper we propose a geometry-aware DoA estimation algorithm. The algorithm uses a fully connected DNN and takes mixed data as input features, namely the time lags maximizing the generalized cross-correlation with phase transform and the microphone coordinates, which are assumed to be known. Experimental results in a reverberant scenario demonstrate the flexibility of the proposed algorithm towards different array geometries and show that the proposed algorithm outperforms model-based algorithms such as steered response power with phase transform. © 2023 IEEE.

关键词： deep neural network DoA estimation microphone array processing

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Reverberation-Robust Self-Calibration and Synchronization of Distributed microphone arrays by Mitigating Heteroscedasticity and Outlier Occurrence in TDoA Measurements

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SENSORS 2024年第1期24卷 114页

作者： Wozniak, Szymon Kowalczyk, Konrad AGH Univ Krakow Inst Elect Fac Comp Sci Elect & Telecommun Al Adama Mickiewicza 30 PL-30059 Krakow Poland

The network of distributed microphone arrays is usually established in an ad hoc manner;hence, network parameters such as the mutual positioning and rotation of the arrays, positions of sources, and synchronization of their recording onset times are initially unknown. In this article, we consider the problem of passively jointly self-calibrating and synchronizing distributed arrays in reverberant rooms. We use a typical two-step approach where, initially, the relative geometry of the network is estimated using Direction of Arrival (DoA) measurements. Subsequently, the absolute scale and synchronization parameters are estimated using Time Difference of Arrival (TDoA) measurements. This article presents methods to improve the robustness and accuracy of estimation of the absolute geometric scaling and synchronization parameters in reverberant conditions, in which TDoA measurements do not follow a normal distribution;furthermore, outliers often occur. To remedy these issues, we propose a Weighted Least Squares (WLS) estimator and schema for weighting the TDoA measurements to increase the estimation accuracy from heteroscedastic TDoA measurements. In addition, we propose an iterative reweighing algorithm with a binary weight to detect and reject TDoA outliers, which exploits the residuals of the parametric model in the least absolute value minimization. A numerical evaluation shows significant improvements in the proposed method over the state of the art in terms of the relative scaling error and mean absolute value of the synchronization parameters.

关键词： distributed sensor networks calibration iterative reweighting microphone array processing robust estimation self-localization synchronization localization sensors

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COMPARISON OF POSITION ESTIMATION METHODS FOR THE ROTATING EQUATORIAL microphone 17

COMPARISON OF POSITION ESTIMATION METHODS FOR THE ROTATING E...

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17th International Workshop on Acoustic Signal Enhancement (IWAENC)

作者： Lawrence, Jeremy Ahrens, Jens Peters, Nils Friedrich Alexander Univ Erlangen Nurnberg Int Audio Labs Erlangen Erlangen Germany Chalmers Univ Technol Gothenburg Sweden

ISBN: (数字)9781665468671

ISBN: (纸本)9781665468671

We present a prototype of a microphone that moves rapidly along the equator of a rigid spherical scatterer. Our prototype allows for up to 100 rotations per second. It will enable processing methods like beamforming or sound field decomposition that are conventionally performed using microphone arrays. Solutions that assume one or more moving microphones have already been proposed in the literature but have not been verified in practise. Most of these methods require precise knowledge of the instantaneous microphone position, for which no convenient practical solution exists. Recent advancements in microphone array processing enable employing simple microphone trajectories, and recent advancements in 3D printing, microcontrollers, and high-speed electric motors allow for the required control of the movement. This paper presents the design of our prototype and evaluates its performance. Of particular interest is the accuracy of the estimation of the microphone's instantaneous position. This paper demonstrates that monitoring the passing time instants of a photodiode that is integrated into the rotating sphere provides the highest precision and robustness.

关键词： microphone array processing acoustic transfer function hardware design

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