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Separating Multiple Moving Sources by microphone array signals for Wayside Acoustic Fault Diagnosis

JOURNAL OF VIBRATION AND ACOUSTICS-TRANSACTIONS OF THE ASME 2019年第5期141卷 051004页

作者： Xiong, Wei He, Qingbo Peng, Zhike Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei 230026 Anhui Peoples R China Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

关键词： multiple moving sources separation microphone array signal processing Doppler distortion removal wayside acoustic fault diagnosis

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A JOINT AUDITORY ATTENTION DECODING AND ADAPTIVE BINAURAL BEAMFORMING ALGORITHM FOR HEARING DEVICES 44

A JOINT AUDITORY ATTENTION DECODING AND ADAPTIVE BINAURAL BE...

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44th IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Pu, Wenqiang Xiao, Jinjun Zhang, Tao Luo, Zhi-Quan Xidian Univ Natl Lab Radar Signal Proc Xian Shaanxi Peoples R China Starkey Hearing Technol Eden Prairie MN 55344 USA Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen Hong Kong Peoples R China

ISBN: (纸本)9781479981311

Traditional adaptive binaural beamforming algorithms for hearing devices often assume that the target talker is known or can be derived from the listener's look direction. When this assumption is violated, the traditional beamforming algorithms often produce distorted target speech and less than optimal noise and interference suppression. Recent advances in electroencephalography (EEG) and its applications to auditory attention decoding have offered a potential solution for tracking the listeners auditory attention in a multi-talker environment [2-5]. In this paper, we propose a unified model for joint auditory attention decoding and adaptive binaural beamforming, and solve the problem using an iterative optimization approach. The proposed algorithm has two advantages over the existing algorithms. First, the optimization objective aims to balance auditory attention alignment, target speech distortion, noise and interference suppression. Secondly, there is no need to estimate the speech envelope of each talker from the noisy and reverberant mixture which is a very challenging problem in practice. The proposed algorithm was evaluated using a newly recorded EEG database for a multi-talker, noisy and reverberant environment [6]. The evaluation results confirm the benefits of the proposed algorithm.

关键词： EEG signals auditory attention microphone array signal processing acoustic beamforming

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The max-norm minimization in non-synchronous measurements 23

The max-norm minimization in non-synchronous measurements

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23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Yu, Liang Fan, Yi Antoni, Jerome Wu, Haijun Leclere, Quentin Yuan, Baihua Jiang, Weikang Institute of Vibration Shock and Noise School of Mechanical Engineering Shanghai Jiao Tong University China Hagong Intelligent Robot Co. Ltd China University of Lyon INSA-Lyon France Key Laboratory of Aerodynamic Noise Control

ISBN: (纸本)9783939296157

Acoustic imaging technique based on microphone array is limited by the size and density of the array, and the method of the non-synchronous measurements is a way to beyond these constraints. The core problem of archiving the non-synchronous measurements of microphone array boils down to a matrix completion of a block diagonal spectral matrix. In this paper, the max-norm minimization is investigated for the spectral matrix completion. Second, the rank, nuclear-norm and max-norm have been investigated as complexity measures in the context of spectral matrix completion, and their performances are compared at different frequencies and low signal-to-noise ratio. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Low-rank Model Max-norm Minimization microphone array signal processing

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PERMUTATION-FREE CGMM: COMPLEX GAUSSIAN MIXTURE MODEL WITH INVERSE WISHART MIXTURE MODEL BASED SPATIAL PRIOR FOR PERMUTATION-FREE SOURCE SEPARATION AND SOURCE COUNTING

PERMUTATION-FREE CGMM: COMPLEX GAUSSIAN MIXTURE MODEL WITH I...

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IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Azcarreta, Juan Ito, Nobutaka Araki, Shoko Nakatani, Tomohiro NTT Corp NTT Commun Sci Labs Kyoto Japan

ISBN: (纸本)9781538646588

Here we propose a permutation-free cGMM (PF-cGMM), a new probabilistic model of observed mixtures, which can resolve permutation ambiguity between frequency bins, and is applicable even when the number of sources is unknown. A recently proposed complex Gaussian mixture model (cGMM) is highly effective for frequency bin-wise clustering when the number of sources is known. However, it cannot resolve the permutation ambiguity, and is inapplicable when the number of sources is unknown. The proposed PF-cGMM is an extension of the cGMM, which resolves these issues. The resolution of the permutation ambiguity can be realized by a spatial prior called a complex inverse Wishart mixture model (cI-WMM). The absence of the permutation ambiguity facilitates source counting, which is performed by hierarchical clustering in this paper. Experiments showed that the PF-cGMM was able to (1) resolve the permutation ambiguity and (2) realize source separation even when the number of sources was unknown with little performance degradation compared to when it was known.

关键词： microphone array signal processing source separation time-frequency masks permutation ambiguity source counting

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EVALUATION OF THE PENALIZED INEQUALITY CONSTRAINED MINIMUM VARIANCE BEAMFORMER FOR HEARING AIDS

EVALUATION OF THE PENALIZED INEQUALITY CONSTRAINED MINIMUM V...

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IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Xiao, Jinjun Pu, Wenqiang Luo, Zhi-Quan Zhang, Tao Starkey Hearing Technol Eden Prairie MN 55344 USA Xidian Univ Natl Lab Radar Signal Proc Xian Shaanxi Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen Peoples R China

ISBN: (纸本)9781538646588

Beamforming is a common technique used to improve speech intelligibility and listening comfort of hearing aids users in a noisy environment. Traditional beamforming algorithms such as linearly constrained minimum variance (LCMV) beamformer cannot effectively suppress multiple interferences when the degree of freedom (DoF) of the array is less than the number of sources in the environment. In [1], a penalized inequality-constrained minimum variance (P-ICMV) beamformer was proposed to address this challenge. In this study, we evaluate the P-ICMV beamformer and compare its performance with other beamformers including the LCMV in a multiple-interference environment. In an objective evaluation, objective metrics related to speech intelligibility and sound quality are used to compare the algorithm performance. In a subjective evaluation, the speech intelligibility of the beamformer processed stimuli are evaluated using normal-hearing listeners. Both the objective and subjective evaluation results show that the P-ICMV beamformer can suppress the interferences more effectively than the existing beamformers when the array DoF is limited.

关键词： microphone array signal processing acoustic beamforming binaural signal processing speech intelligibility

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MAXIMUM-LIKELIHOOD ONLINE SPEAKER DIARIZATION IN NOISY MEETINGS BASED ON CATEGORICAL MIXTURE MODEL AND PROBABILISTIC SPATIAL DICTIONARY

MAXIMUM-LIKELIHOOD ONLINE SPEAKER DIARIZATION IN NOISY MEETI...

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IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Ito, Nobutaka Makino, Takashi Araki, Shoko Nakatani, Tomohiro NTT Corp NTT Commun Sci Labs Kyoto Japan

ISBN: (纸本)9781538646588

In this paper, we propose a maximum-likelihood online diarization method based on a probabilistic spatial dictionary. This dictionary consists of the given probability distribution of spatial features for each possible direction of arrival (DOA) of source signals. Recently, we have developed an online, noise-robust diarization method by utilizing this dictionary as spatial prior information. In this method, DOA estimation is first performed frame-wise based on the dictionary, and subsequently diarization is performed. Although the DOA estimation is performed optimally in the maximum-likelihood sense, the diarization is performed suboptimally based on some heuristics. In contrast, the proposed method performs DOA estimation and diarization jointly and optimally in the maximum-likelihood sense. This is realized by introducing a categorical mixture model (CMM), which has source-wise DOA information and diarization information as unknown parameters. We conducted an experiment on a real-world meeting dataset, and confirmed that the proposed method reduced a diarization error rate by absolute 2.7% compared to the above conventional method.

关键词： microphone array signal processing speaker diarization maximum-likelihood method

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Speech enhancement for robust automatic speech recognition: Evaluation using a baseline system and instrumental measures

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COMPUTER SPEECH AND LANGUAGE 2017年 46卷 574-584页

作者： Moore, A. H. Parada, P. Peso Naylor, P. A. Imperial Coll London Dept Elect & Elect Engn Exhibit Road London England Cirrus Log Marble Arch House66 Seymour St1st Floor London W1H 5BT England

Automatic speech recognition in everyday environments must be robust to significant levels of reverberation and noise. One strategy to achieve such robustness is multi-microphone speech enhancement. In this study, we present results of an evaluation of different speech enhancement pipelines using a state-of-the-art ASR system for a wide range of reverberation and noise conditions. The evaluation exploits the recently released ACE Challenge database which includes measured multichannel acoustic impulse responses from 7 different rooms with reverberation times ranging from 0.33 to 1.34 s. The reverberant speech is mixed with ambient, fan and babble noise recordings made with the same microphone setups in each of the rooms. In the first experiment, performance of the ASR without speech processing is evaluated. Results clearly indicate the deleterious effect of both noise and reverberation. In the second experiment, different speech enhancement pipelines are evaluated with relative word error rate reductions of up to 82%. Finally, the ability of selected instrumental metrics to predict ASR performance improvement is assessed. The best performing metric, Short-Time Objective Intelligibility Measure, is shown to have a Pearson correlation coefficient of 0.79, suggesting that it is a useful predictor of algorithm performance in these tests. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Automatic speech recognition Speech enhancement Dereverberation Beamforming Realistic environments microphone array signal processing

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Maximum-Likelihood Online Speaker Diarization in Noisy Meetings Based on Categorical Mixture Model and Probabilistic Spatial Dictionary

Maximum-Likelihood Online Speaker Diarization in Noisy Meeti...

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Nobutaka Ito Takashi Makino Shoko Araki Tomohiro Nakatani NTT Communication Science Laboratories NTT Corporation Kyoto Japan

ISBN: (纸本)9781538646595

关键词： microphone array signal processing speaker diarization maximum-likelihood method maximum likelihood Direction of arrival estimation Dictionaries as Topic Error analysis Probability distribution Categorical Nominal Unknown parameter United States Department of Agriculture

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Permutation-Free Cgmm: Complex Gaussian Mixture Model with Inverse Wishart Mixture Model Based Spatial Prior for Permutation-Free Source Separation and Source Counting

Permutation-Free Cgmm: Complex Gaussian Mixture Model with I...

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Juan Azcarreta Nobutaka Ito Shoko Araki Tomohiro Nakatani NTT Communication Science Laboratories NTT Corporation Kyoto Japan

ISBN: (纸本)9781538646595

Here we propose a permutation-free cGMM (PF-cGMM), a new probabilistic model of observed mixtures, which can resolve permutation ambiguity between frequency bins, and is applicable even when the number of sources is unknown. A recently proposed complex Gaussian mixture model (cGMM) is highly effective for frequency bin-wise clustering when the number of sources is known. However, it cannot resolve the permutation ambiguity, and is inapplicable when the number of sources is unknown. The proposed PF-cGMM is an extension of the cGMM, which resolves these issues. The resolution of the permutation ambiguity can be realized by a spatial prior called a complex inverse Wishart mixture model (cIWMM). The absence of the permutation ambiguity facilitates source counting, which is performed by hierarchical clustering in this paper. Experiments showed that the PF-cGMM was able to (1) resolve the permutation ambiguity and (2) realize source separation even when the number of sources was unknown with little performance degradation compared to when it was known.

关键词： microphone array signal processing source separation time-frequency masks permutation ambiguity source counting Source separation Permutation ambiguity

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EVALUATION OF THE PENALIZED INEQUALITY CONSTRAINED MINIMUM VARIANCE BEAMFORMER FOR HEARING AIDS

EVALUATION OF THE PENALIZED INEQUALITY CONSTRAINED MINIMUM V...

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Jinjun Xiao Wenqiang Pu Zhi-Quan Luo Tao Zhang Starkey Hearing Technologies Eden Prairie MN 55344 USA National Lab of Radar Signal Processing Xidian University Xi'an China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781538646595

关键词： microphone array signal processing acoustic beamforming binaural signal processing speech intelligibility Speech Intelligibility Hearing Aids Variance Lymphocytic choriomeningitis virus subjective evaluation Beamforming beamformers Noisy environment Voice Quality

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