Blind source separation of nonstationary convolutively mixed signals

Many algorithms for blind source separation (BSS) have been introduced in the past few years, most of which assume statistically stationary sources as well as instantaneous mixtures of signals. In many applications, such as separation of speech or fading communications signals, the sources are nonstationary. Furthermore, the source signals may undergo convolutive (or dynamic) linear mixing, and a more complex BSS algorithm is required to achieve better source separation. We present a new BSS algorithm for separating linear convolutive mixtures of nonstationary signals which relies on the nonstationary nature of the sources to achieve separation. The algorithm is an on-line, LMS-like update based on minimizing the average squared cross-output-channel-correlations along with unity average energy output in each channel. We explain why, for nonstationary signals, such a criterion is sufficient to achieve source separation regardless of the signal statistics.