A new method for blind source separation of nonstationary signals

Many algorithms for blind source separation have been introduced in the past few years, most of which assume statistically stationary sources. In many applications, such as separation of speech or fading communications signals, the sources are nonstationary. We present a new adaptive algorithm for blind source separation of nonstationary signals which relies only on the nonstationary nature of the sources to achieve separation. The algorithm is an efficient, online, stochastic gradient update based on minimizing the average squared cross-output-channel-correlations along with deviation from unity average energy in each output channel. Advantages of this algorithm over existing methods include increased computational efficiency, a simple on-line, adaptive implementation requiring only multiplications and additions, and the ability to blindly separate nonstationary sources regardless of their detailed statistical structure.