Subband based blind source separation for convolutive mixtures of speech

Subband processing is applied to blind source separation (BSS) for convolutive mixtures of speech. This is motivated by the drawback of frequency-domain BSS, i.e., when a long frame with a fixed frame-shift is used to cover reverberation, the number of samples in each frequency decreases and the separation performance is degraded. In our proposed subband BSS, (1) by using a moderate number of subbands, a sufficient number of samples can be held in each subband, mid (2) by using FIR filters in each subband, we can handle long reverberation. Subband BSS achieves better performance than frequency-domain BSS. Moreover, we propose efficient separation procedures that take into consideration the frequency characteristics of room reverberation and speech signals. We achieve this (3) by using longer unmixing filters in low frequency bands, and (4) by adopting overlap-blockshift in BSS´s batch adaptation in low frequency bands. Consequently, frequency-dependent subband processing is successfully realized in the proposed subband BSS.