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Source Separation Using Sparse Discrete Prior Models

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 video conferencing scenario where the mixing parameters are estimated by the video system. We obtain the MAP signal estimators and show they are implemented by a Vitterbi 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