Convolutive demixing with sparse discrete prior models for markov sources

Authors:
Radu Balan;Justinian Rosca
Affiliations:
Siemens Corporate Research, Princeton, NJ;Siemens Corporate Research, Princeton, NJ
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 3
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Estimator for number of sources using minimum description length criterion for blind sparse source mixtures

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Abstract

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.