Algorithms for Markovian source separation by entropy rate minimization

Since in many blind source separation applications, latent sources are both non-Gaussian and have sample dependence, it is desirable to exploit both non-Gaussianity and sample dependency. In this paper, we use the Markov model to construct a general framework for the analysis and derivation of algorithms that take both properties into account. We also present two algorithms using two effective source priors. The first one is a multivariate generalized Gaussian distribution and the second is an autoregressive model driven by a generalized Gaussian distributed process. We derive the Cramér-Rao lower bound and demonstrate that the performance of the algorithms approach the lower bound especially when the underlying model matches the parametric model. We also demonstrate that a flexible semi-parametric approach exhibits very desirable performance.