Sparse Source Separation Using Discrete Prior Models
نویسندگان
چکیده
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 meeting transcription system scenario where the mixing parameters are estimated using a calibration procedure. We derive the MAP signal estimators and show they are implemented by a Viterbi decoding scheme. We perform simulations using TIMIT database, and compare the separation performance of this algorithm with our previous extended DUET method.
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