Two Contributions to Blind Source Separation Using Time–Frequency Distributions

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.