Multichannel Blind Deconvolution Using a Generalized Gaussian Source Model

In this paper, we present an algorithm for the problem of multi-channel blind deconvolution which can adapt to un-known sources with both sub-Gaussian and super-Gaussian probability density distributions using a generalized gaussian source model.
We use a state space representation to model the mixer and demixer respectively, and show how the parameters of the demixer can be adapted using a gradient descent algorithm incorporating the natural gradient extension. We also present a learning method for the unknown parameters of the generalized Gaussian source model. The
performance of the proposed generalized Gaussian source model on a typical example is compared with those of other algorithm, viz the switching nonlinearity algorithm
proposed by Lee et al. [8].