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Two contributions to blind source separation using time-frequency distributions

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2 Author(s)
C. Fevotte ; Inst. de Recherche en Commun. et Cybernetique de Nantes, UMR CNRS, Nantes, France ; C. Doncarli

We present two improvements/extensions of a previous deterministic blind source separation (BSS) technique, by Belouchrani and Amin, that involves joint-diagonalization of a set of Cohen's class spatial time-frequency distributions. The first contribution concerns the extension of the BSS technique to the stochastic case using spatial Wigner-Ville spectrum. Then, we show that Belouchrani and Amin's technique can be interpreted as a practical implementation of the general equations we provide in the stochastic case. The second contribution is a new criterion aimed at selecting more efficiently the time-frequency locations where the spatial matrices should be joint-diagonalized, introducing single autoterms selection. Simulation results on stochastic time-varying autoregressive moving average (TVARMA) signals demonstrate the improved efficiency of the method.

Published in:

IEEE Signal Processing Letters  (Volume:11 ,  Issue: 3 )