An EM algorithm for convolutive independent component analysis
We address the problem of blind separation of convolutive mixtures of spatially and temporally independent sources modeled with mixtures of Gaussians. We present an EM algorithm to compute maximum likelihood estimates of both the separating filters and the source density parameters, whereas, in the state-of-the-art, separating filters are usually estimated with gradient descent techniques. The use of the EM algorithm, as opposed to the usual gradient descent techniques, does not require the empirical tuning of a learning rate, and thus can be expected to provide a more stable convergence. Besides, we show how multichannel autoregressive spectral estimation techniques can be used in order to initialize the EM algorithm properly. We demonstrate the efficiency of our EM algorithm together with the proposed initialization scheme by reporting on simulations with artificial mixtures.