A time-scale correlation-based blind separation method applicable to correlated sources

Yannick Deville (1) , Dass Bissessur (1) , Matthieu Puigt (1) , Shahram Hosseini (1) , Hervé Carfantan (1)

1 LATT - Laboratoire Astrophysique de Toulouse-Tarbes

We first propose a correlation-based blind source separation (BSS) method based on time-scale (TS) representations of the observed signals. This approach consists in identifying the columns of the (permuted scaled) mixing matrix in TS zones where this method detects that a single source is active. It thus sets very limited constraints on the sparsity of the sources in the TS domain. Both the detection and identification stages of this approach use local correlation parameters of the TS transforms of the observed signals. This BSS method, called TISCORR (for TIme-Scale CORRelation-based BSS), is an extension of our previous two temporal and time-frequency versions of this class of methods. Our second contribution in this paper consists in proving that all three approaches apply if the (transformed) source signals are linearly independent, thus allowing them to be correlated. This extends our previous demonstration, which only guaranteed our previous two approaches to be applicable to uncorrelated sources. Experimental tests show that our TISCORR method achieves good separation for linear instantaneous mixtures of real, correlated or uncorrelated, speech signals (output SIRs are above 40 dB).

Domaines: Traitement du signal et de l'image [eess.SP]

Yannick Deville, Dass Bissessur, Matthieu Puigt, Shahram Hosseini, Hervé Carfantan. A time-scale correlation-based blind separation method applicable to correlated sources. 14th European Symposium on Artificial Neural Networks (ESANN 2006), Apr 2006, Bruges, Belgium. pp.337-344. ⟨hal-01413554⟩