%0 Conference Proceedings %T Under-determined convolutive blind source separation using spatial covariance models %+ Speech and sound data modeling and processing (METISS) %A Duong, Ngoc, Q. K. %A Vincent, Emmanuel %A Gribonval, Rémi %< avec comité de lecture %B Acoustics, Speech and Signal Processing, IEEE Conference on (ICASSP'10) %C Dallas, United States %P 9--12 %8 2010-03-14 %D 2010 %R 10.1109/ICASSP.2010.5496284 %Z Computer Science [cs]/Signal and Image Processing %Z Engineering Sciences [physics]/Signal and Image processingConference papers %X 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. %G English %2 https://inria.hal.science/inria-00541863/document %2 https://inria.hal.science/inria-00541863/file/duong_ICASSP10.pdf %L inria-00541863 %U https://inria.hal.science/inria-00541863 %~ EC-PARIS %~ UNIV-RENNES1 %~ CNRS %~ INRIA %~ INSA-RENNES %~ INRIA-RENNES %~ IRISA %~ IRISA_SET %~ INRIA_TEST %~ TESTALAIN1 %~ IRISA-D5 %~ INRIA2 %~ UR1-HAL %~ UR1-MATH-STIC %~ UR1-UFR-ISTIC %~ TEST-UNIV-RENNES %~ TEST-UR-CSS %~ UNIV-RENNES %~ INRIA-RENGRE %~ INRIA-300009 %~ INSA-GROUPE %~ UR1-MATH-NUM