Convolutive Blind Source Separation based on Multiple Decorrelation.

Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underling sources. We tackle the problem by explicitly exploiting the non-stationarity of the acoustic sources. Changing crosscorrelations at multiple times give a sufficient set of constraints for the unknown channels. A least squares optimization allows us to estimate a forward model, identifying thus the multipath channel. In the same manner we can find an FIR backward model, which generates well separated model sources. Furthermore, for more than three channels we have sufficient conditions to estimate underlying additive sensor noise powers, which could be used for further signal enhancement. I. Introduction A growing number of researchers have published in recent years on the problem of blind source separation. For one, th..