Blind source separation by nonstationarity of variance: A cumulant-based approach

Blind separation of source signals usually relies either on the nongaussian ity of the signals or on their linear autocorrelations. A third approach wa s introduced by Matsuoka et al., who showed that source separation can be p erformed by using the nonstationarity of the signals, in particular the non stationarity of their variances. In this paper, we show how to interpret th e nonstationarity due to a smoothly changing variance in terms of higher or der cross-cumulants. This is based on considering the time-correlation of t he squares (energies) of the signals and leads to a simple optimization cri terion. Using this criterion, we construct a fixed-point algorithm that is computationally very efficient.