Two contributions to blind source separation using time-frequency distributions

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Authors C. Févotte & C. Doncarli
Journal/Conference Name IEEE Signal Processing Letters
Paper Category
Paper Abstract 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.
Date of publication 2004
Code Programming Language MATLAB
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