Abstract
This paper presents a new time---frequency approach for recovering source 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 underdetermined 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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