Maximum likelihood source separation for discrete sources

This communication deals with the source separation problem which consists in the separation of a noisy mixture of independent sources without a priori knowledge of the mixture coeecients. In this paper, we consider the maximum likelihood (ML) approach for discrete source signals with known probability distributions. An important feature of the ML approach in Gaussian noise is that the covariance matrix of the additive noise can be treated as a parameter. Hence, it is not necessary to know or to model the spatial structure of the noise. Another striking feature ooered in the case of discrete sources is that, under mild assumptions, it is possible to separate more sources than sensors. In this paper, we consider maximization of the likelihood via the Expectation-Maximization (EM) algorithm.