A Modified Speech Blind Separation Method Based on Information Maximum

Abstract

A new algorithm based on information maximization is proposed, against the shortcomings of traditional speech blind source separation of low convergence rate and high crosstalk error. It uses the new sgn function to make mutual information of input and output to maximize by analyzing a variety of non-linear function of the separation performance, and advances based sgn function BSS with fixed step-size and adaptive variable step-size. Experiments show that the new algorithm has advantages, such as fast convergence, small crosstalk error and good separation efficiency which compared with traditional methods of Be11’s and Sejnowskl’s.