Convolutive blind separation of non-stationary sources

Abstract

—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 underlying sources. We tackle the problem by explicitly exploiting the nonstationarity of the acoustic sources. Changing cross-correlations 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 multi-path 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. We show good performance in real room environments and demonstrate the algorithm's utility for automatic speech recognition. Index Terms—Blind source separation, frequency domain, mul- tipath channel, multiple decorrelation, nonstationary signals.