Abstract

In this paper, we present a new Time-Frequency approach for recovering sources' contribution to two convolutive mixtures. The separation task is performed on two steps: Each mixture is clustered into Voiced/Unvoiced frames, and then the predominant source in each time frequency bin is identified through a specific weight function which is based on sources' excitation characteristics extraction. We investigate the performance of the proposed approach in the un-derdetermined context using objective quality measures. Results for separating three and four speech sources in a live recorded mixture show the superiority of the proposed method in rejecting artifacts over existing convolutive separation techniques.

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