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Under-determined convolutive blind source separation using spatial covariance models
Abstract : This paper deals with the problem of under-determined con- volutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial properties of the source. We consider two covariance models and address the estimation of their parameters from the recorded mixture by a suitable initialization scheme followed by an iterative expectation- maximization (EM) procedure in each frequency bin. We then align the order of the estimated sources across all fre- quency bins based on their estimated directions of arrival (DOA). Experimental results over a stereo reverberant speech mixture show the effectiveness of the proposed approach.
https://hal.inria.fr/inria-00541863
Contributor : Rémi Gribonval <>
Submitted on : Thursday, January 27, 2011 - 9:43:41 PM
Last modification on : Thursday, March 21, 2019 - 2:20:42 PM
Document(s) archivé(s) le : Thursday, April 28, 2011 - 2:30:20 AM
Contributor : Rémi Gribonval <>
Submitted on : Thursday, January 27, 2011 - 9:43:41 PM
Last modification on : Thursday, March 21, 2019 - 2:20:42 PM
Document(s) archivé(s) le : Thursday, April 28, 2011 - 2:30:20 AM
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duong_ICASSP10.pdf
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