Multichannel blind separation and deconvolution of sources with arbitrary distributions

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 efficient 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 separator using a rigorously-derived sufficient criterion for stability and then selects the appropriate nonlinearity for each channel such that local convergence conditions bf the algorithm are satisfied. Extensive simulations show the validity and efficiency of our method to blindly extract mixtures of arbitrary-distributed source signals.