Convolutive Demixing with Sparse Discrete Prior Models for Markov Sources

In this paper we present a new source separation method based on dynamic sparse source signal models. Source signals are modeled in frequency domain as a product of a Bernoulli selection variable with a deterministic but unknown spectral amplitude. The Bernoulli variables are modeled in turn by first order Markov processes with transition probabilities learned from a training database. We consider a scenario where the mixing parameters are estimated by calibration. We obtain the MAP signal estimators and show they are implemented by a Viterbi decoding scheme. We validate this approach by simulations using TIMIT database, and compare the separation performance of this algorithm with our previous extended DUET method.