Maximum likelihood estimation of ICA model for wide class of source distributions

We propose two blind source separation techniques that are applicable to a wide class of source distributions that may also be skewed and may even have zero kurtosis. Skewed distributions are encountered in many important application areas such as communications and biomedical signal processing. The methods are based on maximum likelihood approach where source distributions are modeled adaptively by the Pearson system and the extended generalized lambda distribution (EGLD). To compare the developed methods with the existing methods, quantitative measures for the quality of separation are used. Simulation experiments demonstrate the good performance of proposed methods in the cases where the standard BSS methods perform poorly.

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