A DISTORTION FREE LEARNING ALGORITHM FOR MULTI-CHANNEL CONVOLUTIVE BLIND SOURCE SEPARATION

In this paper, source separation and signal distortion are theoretically analyzed in multi-channel convolutive blind source separation (BSS) systems implemented in both the time and the frequency domains. Feedforward (FF-) BSS systems have some degrees of freedom in the solution space. Therefore, signal distortion is likely to occur. A condition for complete separation and distor-tion free is proposed for multi-channel convolutive FF-BSS systems. This condition is incorporated in learning algorithms as a distortion free constraint. The condition for weights in a separation block cannot be expressed in an explicit form. Therefore, an approximation expres-sion is introduced. Computer simulations using speech signals and stationary colored signals have been carried out for conventional methods and the new learning al-gorithms. The proposed method can drastically suppress signal distortion, while maintaining a high source sepa-ration performance. 1