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This article addresses the modeling of reverberant recording environments inthe context of under-determined convolutive blind source separation. We modelthe contribution of each source to all mixture channels in the time-frequencydomain as a zero-mean Gaussian random variable whose covariance encodes thespatial characteristics of the source. We then consider four specificcovariance models, including a full-rank unconstrained model. We derive afamily of iterative expectationmaximization (EM) algorithms to estimate theparameters of each model and propose suitable procedures to initialize theparameters and to align the order of the estimated sources across all frequencybins based on their estimated directions of arrival (DOA). Experimental resultsover reverberant synthetic mixtures and live recordings of speech data show theeffectiveness of the proposed approach.

Under-determined reverberant audio source separation using a full-rank spatial covariance model