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We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis techniques, our approach fully exploits structured sparsity models to obtain substantial improvement over the existing state-of-the-art. We evaluate our method for separation and recognition of a target speaker in a multi-party scenario. Our resultsdoi:10.1109/icassp.2011.5947379 dblp:conf/icassp/AsaeiBC11 fatcat:gzadkg4t6jdthkwirfqdm2texa