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Acoustic scene classification using inter- and intra-subarray spatial features in distributed microphone array

EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2024年第1期2024卷 1-13页

作者： Kawamura, Takao Kinoshita, Yuma Ono, Nobutaka Scheibler, Robin Tokyo Metropolitan Univ Dept Comp Sci 6-6 Asahigaoka Hino Tokyo 1910065 Japan Tokai Univ Dept Human & Informat Sci 4-1-1 Kitakaname Hiratsuka Kanagawa 2591292 Japan LY Corp Mus Proc Team 1-3 KioichoChiyoda Ku Tokyo 1028282 Japan

In this study, we investigate the effectiveness of spatial features in acoustic scene classification using distributed microphone arrays. Under the assumption that multiple subarrays, each equipped with microphones, are synchronized, we investigate two types of spatial feature: intra- and inter-generalized cross-correlation phase transforms (GCC-PHATs). These are derived from channels within the same subarray and between different subarrays, respectively. Our approach treats the log-Mel spectrogram as a spectral feature and intra- and/or inter-GCC-PHAT as a spatial feature. We propose two integration methods for spectral and spatial features: (a) middle integration, which fuses embeddings obtained by spectral and spatial features, and (b) late integration, which fuses decisions estimated using spectral and spatial features. The evaluation experiments showed that, when using only spectral features, employing all channels did not markedly improve the F1-score compared with the single-channel case. In contrast, integrating both spectral and spatial features improved the F1-score compared with using only spectral features. Additionally, we confirmed that the F1-score for late integration was slightly higher than that for middle integration.

关键词： Domestic activity monitoring Acoustic scene classification distributed microphone array Subarray Generalized cross-correlation phase transform Middle integration Late integration

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Acoustic Source Localization Using Kernel-based Extreme Learning Machine in distributed microphone array

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ARCHIVES OF ACOUSTICS 2021年第1期46卷 67-78页

作者： Wang, Rong Chen, Zhe Yin, Fuliang Dalian Univ Technol Sch Informat & Commun Engn Dalian 116023 Peoples R China

Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.

关键词： extreme learning machine acoustic source localization distributed microphone array generalized cross correlation function

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A constrained total least squares calibration method for distributed microphone array

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APPLIED ACOUSTICS 2018年 140卷 188-197页

作者： Wang, Rong Chen, Zhe Yin, Fuliang Dalian Univ Technol Sch Informat & Commun Engn Dalian 116023 Peoples R China

microphone positions have to be calibrated in distributed microphone array applications. A constrained total least squares calibration method for distributed microphone arrays is proposed in this paper. All the source event positions are first estimated by the weighted multidimensional scaling algorithm. Then the suitable source events are picked up by the TDOA selection strategy at each node. Finally, the node microphones are calibrated by the total least squares, and further refined based on constrained total least squares when the estimated results suffer from large errors. The proposed method can obtain higher calibration accuracy and works well in noise and reverberation conditions. Simulation and real-world experiment results reveal the validity of the proposed method.

关键词： Calibration Multidimensional scaling Total least squares distributed microphone array

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Distant Noise Reduction Based on Multi-delay Noise Model Using distributed microphone array 26

Distant Noise Reduction Based on Multi-delay Noise Model Usi...

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European Signal Processing Conference (EUSIPCO)

作者： Koizumi, Yuma Saito, Shoichiro Shimauchi, Suehiro Kobayashi, Kazunori Harada, Noboru NTT Corp NTT Media Intelligence Labs Tokyo Japan

ISBN: (纸本)9789082797015

We propose a novel framework for reducing distant noise by using a distributed microphone array;reducing noise propagated from a far distance in real-time. Previous studies have revealed that a distributed microphone array with an instantaneous mixing assumption can effectively reduce noise when the target and noise sources are significantly far apart. However, in distant noise reduction, the target and noise sources are not usually instantaneously mixed because the reverberation-and propagation-time from the noise sources to a microphone is longer than the short-time Fourier transform (STFT) length. To express reverberation-and propagation-parameters, we introduce a multi-delay noise model that represents the reverberation-time as a convolution of the transfer-function-gains and the noise sources and the propagation-time as time-frame delays. These parameters are estimated on the basis of the maximum a posteriori (MAP) estimation. Experimental results show that the proposed method outperformed conventional methods in several performance measurements and could reduce distant noise propagated from more than 100 m away in a real-environment.

关键词： Distant noise reduction distributed microphone array MAP estimation transfer function

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Spatial Cepstrum as a Spatial Feature Using a distributed microphone array for Acoustic Scene Analysis

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2017年第6期25卷 1335-1343页

作者： Imoto, Keisuke Ono, Nobutaka Grad Univ Adv Studies SOKENDAI Sch Multidisciplinary Sci Dept Informat Hayama Kanagawa 2400193 Japan Natl Inst Informat Tokyo 1018430 Japan

In this paper, with the aim of using the spatial information obtained from a distributed microphone array employed for acoustic scene analysis, we propose a robust and efficient method, which is called the spatial cepstrum. In our approach, similarly to the cepstrum, which is widely used as a spectral feature, the logarithm of the amplitude in multichannel observation is converted to a feature vector by a linear orthogonal transformation. This linear orthogonal transformation is achieved by principal component analysis (PCA) in general. Moreover, we also show that for a circularly symmetric microphone arrangement with an isotropic sound field, PCA is identical to the inverse discrete Fourier transform and the spatial cepstrum exactly corresponds to the cepstrum. The proposed approach does not require the positions of the microphones and is robust against the synchronization mismatch of channels, thus ensuring its suitability for use with a distributed microphone array. Experimental results obtained using actual environmental sounds verify the validity of our approach even when a smaller feature dimension than the original one is used, which is achieved by dimensionality reduction through PCA. Additionally, experimental results also indicate that the robustness of the proposed method is satisfactory for observations that have the synchronization mismatch of channels.

关键词： Acoustic scene analysis (ASA) circularly symmetric array distributed microphone array isotropic sound field spatial cepstrum

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A novel sparse model for multi-source localization using distributed microphone array

A novel sparse model for multi-source localization using dis...

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2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017

作者： Nguyen, Thi Ngoc Tho Tuna, Cagdas Zhao, Shengkui Jones, Douglas L. Illinois at Singapore 138632 Singapore Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL61801 United States

ISBN: (纸本)9781509041176

When distances between microphone pairs are larger than the half-wavelength of signals, source localization methods using cross-correlation such as time-difference-of-arrival (TDOA), steered response power (SRP) are commonly used in practice. We present here a novel model that expresses microphone pairwise cross-correlations as a sum of autocorrelations of source signals shifted by the relative delays of the signals arriving at the microphone pairs, and weighted by the source power and the distances between the sources and the microphone pairs. The model is formulated as a linear inverse problem and is sparse with respect to the source power map. The source power map, which directly shows the locations of all the sound sources, can be reconstructed using 1-norm minimization algorithms. We demonstrate the effectiveness of our model in a wildlife monitoring application, where the goal is to locate multiple frogs in a dense chorus. © 2017 IEEE.

关键词： cross-correlation distributed microphone array linear inverse problem multi-source source localization sparse representation

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A novel sparse model for multi-source localization using distributed microphone array

A novel sparse model for multi-source localization using dis...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Thi Ngoc Tho Nguyen Cagdas Tuna Shengkui Zhao Douglas L. Jones Advanced Digital Science Center (ADSC) Illinois at Singapore 138632 Singapore

ISBN: (纸本)9781509041183

When distances between microphone pairs are larger than the half-wavelength of signals, source localization methods using cross-correlation such as time-difference-of-arrival (TDOA), steered response power (SRP) are commonly used in practice. We present here a novel model that expresses microphone pairwise cross-correlations as a sum of autocorrelations of source signals shifted by the relative delays of the signals arriving at the microphone pairs, and weighted by the source power and the distances between the sources and the microphone pairs. The model is formulated as a linear inverse problem and is sparse with respect to the source power map. The source power map, which directly shows the locations of all the sound sources, can be reconstructed using l_1-norm minimization algorithms. We demonstrate the effectiveness of our model in a wildlife monitoring application, where the goal is to locate multiple frogs in a dense chorus.

关键词： cross-correlation distributed microphone array linear inverse problem multi-source source localization sparse representation

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A microphone position calibration method based on combination of acoustic energy decay model and TDOA for distributed microphone array

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APPLIED ACOUSTICS 2015年第Aug.期95卷 13-19页

作者： Chen, Zhe Li, Zhenglin Wang, Shuwen Yin, Fuliang Dalian Univ Technol Sch Informat & Commun Engn Dalian 116023 Peoples R China

The geometrical structure and size of a distributed microphone array are usually irregular, and need to be estimated in many applications. A microphone position calibration method based on combination of acoustic energy decay model and time difference of arrival for distributed microphone arrays is proposed in this paper. The method utilizes the acoustic energy decay model to estimate the coarse distance between the microphone and the sound source, and then applies time difference of arrival to search for the accurate distance within a certain range near the coarse distance. Finally, the minimum mean square error estimation method is employed to determine the position of the microphone. The proposed method has a high positioning accuracy, stable calibration performance and low computational complexity. Simulation results reveal the validity of the proposed method at a theoretical level. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： distributed microphone array Calibration Acoustic energy decay model Time difference of arrival Minimum mean square error estimation

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SPATIAL-FEATURE-BASED ACOUSTIC SCENE ANALYSIS USING distributed microphone array 23

SPATIAL-FEATURE-BASED ACOUSTIC SCENE ANALYSIS USING DISTRIBU...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Imoto, Keisuke Ono, Nobutaka SOKENDAI Hayama Kanagawa Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9780992862633

In this paper we propose a robust and efficient method to utilize the spatial information provided by a distributed microphone array for acoustic scene analysis. In our approach, similarly to the cepstrum, which is widely used as a spectral feature, the logarithm of the amplitude in multichannel observation is converted to a feature vector by a linear orthogonal transformation. Then, the spatial information of the acoustic scene is represented in the spatial feature space. This approach does not require the positions of the microphones and is not sensitive to the synchronization mismatch of channels, both of which make the method suitable for use with a distributed microphone array. Experimental results using real-life environmental sounds show the validity of our approach even when a smaller feature dimension than the original one is used.

关键词： Acoustic scene analysis distributed microphone array spatial cepstrum symmetric microphone array isotropic sound field

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Modeling inter-node acoustic dependencies with Restricted Boltzmann Machine for distributed microphone array based BSS 40

Modeling inter-node acoustic dependencies with Restricted Bo...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Kinoshita, Keisuke Nakatani, Tomohiro NTT Communication Science Laboratories NTT Corporation 2-4 Hikaridai Soraku-gun Kyoto Japan

ISBN: (纸本)9781467369978

An accurate estimation of a source activity information is essential for many speech enhancement algorithms including blind source separation (BSS). In this paper, we propose a novel BSS method that accurately models and estimates the source activity in distributed microphone array (DMA) scenarios. In DMA scenarios, microphones (or in more general term, microphone-nodes) are often spatially distributed to a great degree. If there are multiple source signals in such an environment, the level of each source signal at each microphone-node varies significantly, thus the source activities observable at one microphone-node should be significantly different from those of other nodes. Therefore, it is essential to assume node-specific source activities in DMA scenarios. In the proposed method, the estimation of the node-specific source activities are done by integrating node-wise clustering-based BSS processings based on inter-node acoustic dependencies, i.e., a co-occurrence of the source activities among nodes. To model the co-occurrence relationship, we employ Restricted Boltzmann Machine (RBM) in a similar manner as it is used for collaborative filtering. This paper introduces a probabilistic formulation of the proposed method, and experimentally demonstrates how essential it is to estimate the node-specific source activities for distributed microphone array based BSS. © 2015 IEEE.

关键词： blind source separation distributed microphone array node-specific source activity restricted Boltzmann machine

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