A Distortion Free Learning Algorithm for Feed-Forward BSS with Convolutive Mixture and Multi-Channel Signal Sources

摘要

Feed-forward Blind Source Separation (FF-BSS) systems have some degree of freedom in the solution space, and signal distortion is likely to occur in convolutive mixtures. Previously, a condition for complete separation and distortion free has been derived for 2-channel FF-BSS. This condition has been applied to the learning algorithms as a distortion free constraint in both the time and frequency domains. In this paper, the condition is further extended to multiple channel FF-BSSs. This condition requires the a high computational complexity to be applied to the learning process as a constraint. An approximate constraint is proposed in order to relax the high computational load. In comparison with the originalconstraint, computer simulations have demonstrated that the approximation can obtain similar performances with respect to source separation as well as signal distortion using speech signals. Furthermore, the performances can be improved compared to the conventional for three channels.