Multichannel Blind Deconvolution Using a Generalized Exponential Source Model

In this paper, we present an algorithm for the problem of multi-channel blind deconvolution which can adapt to unknown sources with both sub-Gaussian and super-Gaussian probability density distributions using a generalized exponential 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 exponential source model. The performance of the proposed generalized exponential source model on a typical example is compared with those of two other algorithms, viz., the learning algorithm with a fixed nonlinearity, without any regard to the underlying probability distribution of sources, and the switching nonlinearity algorithm proposed by Lee et al. [7].