Speech blind signal separation with FastICA and Markov Chain combination

Applying Independent component analysis (ICA) on higher order statistics signal, can separate both the statistical independent and non-Gaussian source signals from the mixed signal. In many variants of ICA, fixed-point algorithm (also be known as FastlCA) is widely used in signal processing due to its fast convergence and good separation. Although the FastlCA achieves some promising separation results, but when the source signals are the non-sparse signals, the effect of the separation is not very ideal. Therefore, in this paper, the Bayesian estimation method based on Markov Chain Monte Carlo is proposed for improving these limitations.