Subband Blind Source Separation for Convolutive Mixture of Speech Signals Based on Dynamic Modeling

In this paper, a subband blind source separation method based on dynamic modeling for convolutive mixture of speech signals is proposed. We show that by applying a dynamical model to subband signals, some of the drawbacks of the time domain approach can be resolved, leading to improvements in separation performance. By employing the subband processing, we enhance the speed of the method, first by reducing the computational cost of the algorithm resulting from shorter demixing filters and second, by considering the parallel processing capability of the subband domain. Furthermore, by applying particular settings to the step-size parameter and to the demixing filter lengths in various subbands, we achieve much better performance in terms of the separation ability. The proposed algorithm is applied to two different experiments and a comparison is done against the time domain approach. The results demonstrate the superiority of the subband domain in terms of speed and accuracy.