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Blind Separation of Non Stationary Non Gaussian Sources

Pham Dinh-Tuan, Laboratoire de Modélisation et Calcul

Page numbers in the proceedings:
Volume II pp 67-70

Blind Identification and Deconvolution

Paper abstract
Most blind sources separation methods are based on the non Gaussianity or the coloration of the sources and only recently their non-stationarity. This work proposes new procedures which exploit both the first and last aspects. We adopt the quasi-maximum likelihood approach which provided a set of estimating equations involving the score functions, which are then estimated by a projection method and through the idea blocking or kernel smoothing. Efficient off-line and on-line algorithms are developed. A simpler and less costly procedure based on a simple contrast for sub Gaussian sources is also considered. Some simulation experiments are given illustrating the high performance of the method.

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