MULTICHANNEL BLIND SEPARATION ANDDECONVOLUTION OF SOURCES WITHARBITRARY

{ Blind deconvolution and separation of linearly mixed and convolved sources is an important and challenging task for numerous applications. While several recently-developed algorithms have shown promise in these tasks, these techniques may fail to separate signal mixtures containing both sub-and super-Gaussian-distributed sources. In this paper, we present a simple and eecient extension of a family of algorithms that enables the separation and deconvolution of mixtures of arbitrary non-Gaussian sources. Our technique monitors the statistics of each of the outputs of the sepa-rator using a rigorously-derived suucient criterion for stability and then selects the appropriate nonlinearity for each channel such that local convergence conditions of the algorithm are satissed. Extensive simulations show the validity and eeciency of our method to blindly extract mixtures of arbitrary-distributed source signals.