TEMPORAL AND TIME-FREQUENCY CORRELATION-BASED BLIND SOURCE SEPARATION METHODS

In this paper, we propose two versions of a correlation-based blind source separation (BSS) method. Whereas its basic version operates in the time domain, its extended form is based on the timefrequency (TF) representations of the observed signals and thus applies to much more general conditions. The latter approach consists in identifying the columns of the (permuted scaled) mixing matrix in TF areas where this method detects that a single source occurs. Both the detection and identification stages of this approach use local correlation parameters of the TF transforms of the observed signals. This BSS method, called TIFCORR (for TImeFrequency CORRelation-based BSS), is shown to yield very accurate separation for linear instantaneous mixtures of real speech signals (output SNR’s are above 60 dB).