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Online Speech Dereverberation Using Kalman Filter and EM Algorithm

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2015年第2期23卷 394-406页

作者： Schwartz, Boaz Gannot, Sharon Habets, Emanuel A. P. Bar Ilan Univ Fac Engn IL-5290002 Ramat Gan Israel Joint Inst Univ Erlangen Nuremberg & Fraunhofer I Int Audio Labs Erlangen D-91058 Erlangen Germany

Speech signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown and time-varying. In this paper, a scenario with a single desired sound source and slowly time-varying and spatially-white noise is considered, and a multi-microphone algorithm that simultaneously estimates the clean speech signal and the time-varying acoustic system is proposed. The recursive expectation-maximization scheme is employed to obtain both the clean speech signal and the acoustic system in an online manner. In the expectation step, the Kalman filter is applied to extract a new sample of the clean signal, and in the maximization step, the system estimate is updated according to the output of the Kalman filter. Experimental results show that the proposed method is able to significantly reduce reverberation and increase the speech quality. Moreover, the tracking ability of the algorithm was validated in practical scenarios using human speakers moving in a natural manner.

关键词： Dereverberation recursive parameter estimation recursive expectation-maximization convolution in stft

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An EM Algorithm for Joint Dual-Speaker Separation and Dereverberation 27

An EM Algorithm for Joint Dual-Speaker Separation and Dereve...

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27th European Signal Processing Conference (EUSIPCO)

作者： Cohen, Nili Hazan, Gershon Schwartz, Boaz Gannot, Sharon Bar Ilan Univ Fac Engn Ramat Gan Israel

ISBN: (纸本)9789082797039

The scenario of a mixture of two speakers captured by a microphone array in a noisy and reverberant environment is considered. If the problems of source separation and dereverberation are treated separately, performance degradation may result. It is well-known that the performance of blind source separation (BSS) algorithms degrades in the presence of reverberation, unless reverberation effects are properly addressed (leading to the so-called convolutive BSS algorithms). Similarly, the performance of common dereverberation algorithms will severely degrade if an interference signal is also captured by the same microphone array. The aim of the proposed method is to jointly separate and dereverberate the two speech sources, by extending the Kalman expectation-maximization for dereverberation (KEMD) algorithm, previously proposed by the authors. A statistical model is attributed to this scenario, using the convolutive transfer function (CTF) approximation, and the expectation-maximization (EM) scheme is applied to obtain a maximum likelihood (ML) estimate of the parameters. In the expectation step, the separated clean signals are extracted from the observed data by the application of a Kalman Filter, utilizing the parameters that were estimated in the previous iteration. The maximization step updates the parameters estimation according to the E-step output. Simulation results shows that the proposed method improves both the separation of the signals and their overall quality.

关键词： Array processing blind source separation dereverberation expectation-maximization convolution in stft

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