Under-determined convolutive blind source separation using spatial covariance models

Ngoc Duong 1 Emmanuel Vincent 1 Rémi Gribonval 1
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
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.
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Ngoc Duong, Emmanuel Vincent, Rémi Gribonval. Under-determined convolutive blind source separation using spatial covariance models. Acoustics, Speech and Signal Processing, IEEE Conference on (ICASSP'10), Mar 2010, Dallas, United States. pp.9--12, ⟨10.1109/ICASSP.2010.5496284⟩. ⟨inria-00541863⟩

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