Separating more sources than sensors using time-frequency distributions

Abstract

This paper deals with the problem of blind source separation of nonstationary signals of which only instantaneous linear signals are observed. Exploiting the effectiveness of time-frequency signal processing for nonstationary signals, a blind source separation approach is considered using the observation spatial time-frequency distributions (STFD). Existing solutions are bound to the situation in which the number of sources being separated is less than the number of available sensors measuring the mixed sources. We consider the more general case when we can have more sources than sensors assuming that the former are "separable" in the time-frequency domain. The proposed solution proceeds through 3 main steps: (i) a testing procedure is applied (after whitening the STFD) to first separate the cross-terms from auto-terms; (ii), source separation in the time-frequency domain (from the autoterms only) using a vector classification approach; and finally (iii), obtaining the source signatures using time-frequency synthesis.