Subband Based Blind Source Separation.

In this chapter, we address subband-based blind source separation (BSS) for convolutive mixtures of speech by reporting a large number of experimental results. The subband-based BSS approach offers a compromise between time-domain and frequency-domain techniques. The former is usually difficult and slow with many separation filter coefficients to estimate. With the latter it is difficult to estimate statistics when the adaptation data length is insufficient. With subband-based BSS, a sufficient number of samples for estimating statistics can be held in each subband by using a moderate number of subbands. Moreover, by using FIR filters in each subband, which are shorter than the filters used for time-domain BSS, we can handle long reverberation. In addition, subband-based BSS allows us to select the separation method suited to each subband. Using this advantage, we introduce efficient separation procedures that take both the frequency characteristics of the room reverberation and speech signals into consideration. In concrete terms, longer separation filters and an overlap-blockshift in BSS’s batch adaptation in low frequency bands improve the separation performance. Consequently, frequency-dependent subband processing is successfully realized with subband-based BSS. [ABSTRACT FROM AUTHOR]