NDLI: Multichannel Sound Source Dereverberation and Separation for Arbitrary Number of Sources Based on Bayesian Nonparametrics

Content Provider	IEEE Xplore Digital Library
Author	Otsuka, T. Ishiguro, K. Yoshioka, T. Sawada, H. Okuno, H.G.
Copyright Year	2014
Abstract	Multichannel signal processing using a microphone array provides fundamental functions for coping with multi-source situations, such as sound source localization and separation, that are needed to extract the auditory information for each source. Auditory uncertainties about the degree of reverberation and the number of sources are known to degrade performance or limit the practical application of microphone array processing. Such uncertainties must therefore be overcome to realize general and robust microphone array processing. These uncertainty issues have been partly addressed-existing methods focus on either source number uncertainty or the reverberation issue, where joint separation and dereverberation has been achieved only for the overdetermined conditions. This paper presents an all-round method that achieves source separation and dereverberation for an arbitrary number of sources including underdetermined conditions. Our method uses Bayesian nonparametrics that realize an infinitely extensible modeling flexibility so as to bypass the model selection in the separation and dereverberation problem, which is caused by the source number uncertainty. Evaluation using a dereverberation and separation task with various numbers of sources including underdetermined conditions demonstrates that (1) our method is applicable to the separation and dereverberation of underdetermined mixtures, and that (2) the source extraction performance is comparable to that of a state-of-the-art method suitable only for overdetermined conditions.
Starting Page	2218
Ending Page	2232
Page Count	15
File Size	3134898
File Format	PDF
ISSN	23299290
Volume Number	22
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Reverberation Microphones Bayes methods Vectors Arrays Uncertainty Source separation underdetermined mixtures Bayesian nonparametrics blind dereverberation blind source separation Markov chain Monte Carlo method microphone array processing
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Signal Processing Instrumentation Speech and Hearing Electrical and Electronic Engineering Computational Mathematics Media Technology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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