Abstract

In this paper, the speech separation task will be regarded as a convolutive mixture Blind Source Separation (BSS) problem. The Maximum Entropy (ME) algorithm, the Minimum Mutual Information (MMI) algorithm and the Maximum Likelihood (ML) algorithm are main approaches of the algorithms solving the BSS problem. The relationship of these three algorithms has been analyzed in this paper. Based on the feedback network architecture, a new speech separation algorithm is proposed by using the Gaussian Mixture Model (GMM) pdf estimation in this paper. From the computer simulation results, it can be concluded that the proposed algorithm can get faster convergence rate and lower output Mean Square Error than the conventional ME algorithm.

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