Speaker localization and separation using incremental distributed expectation-maximization

Yuval Dorfan, Dani Cherkassky, Sharon Gannot

Abstract: A network of microphone pairs is utilized for the joint task of localizing and separating multiple concurrent speakers. The recently presented incremental distributed expectation-maximization (IDEM) is addressing the first task, namely detection and localization. Here we extend this algorithm to address the second task, namely blindly separating the speech sources. We show that the proposed algorithm, denoted distributed algorithm for localization and separation (DALAS), is capable of separating speakers in reverberant enclosure without a priori information on their number and locations. In the first stage of the proposed algorithm, the IDEM algorithm is applied for blindly detecting the active sources and to estimate their locations. In the second stage, the location estimates are utilized for selecting the most useful node of microphones for the subsequent separation stage. Separation is finally obtained by utilizing the hidden variables of the IDEM algorithm to construct masks for each source in the relevant node.