Generalized Mixture Models for Blind Source Separation.
Amr Goneid, Abeer Kamel, Ibrahim Farag · Egyptian Computer Science Journal · 2010
Neural Independent Component Analysis (ICA) algorithms based on unimodal source distributions provide acceptable performances in the case of Blind Source Separation (BSS) of super-gaussian sources. However, their convergence profiles are significantly slower in the case of sub-gaussian sources. In some situations it is necessary to deal with sub-gaussian signals in the form of noise or others. In this case, one needs an algorithm that can deal efficiently with mixtures of both superand sub-gaussian signals. In this paper, we introduce generalized mixture models for super-and sub-gaussian sources based on the Exponential Power Distibution (EPD). The kurtosis and stability profiles of these models are investigated and the corresponding non-linearities are derived. A switching algorithm is designed for the blind source separation of mixtres of superand sub-gaussian sources. Experimental results are presented on the application of these models to homogeneous and mixed sources using a modified fast ICA algorithm.