MAP Source Separation using Belief Propagation Networks
Radu Victor Balan, Justinian P. Rosca · 2006
In this paper we continue our treatment of source separation 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 variable. The Bernoulli variable is modeled by a first order Markov process with transition probabilities learned from a training database. We consider a scenario where the mixing parameters are estimated by calibration. We derive the MAP signal estimators and show that the optimization problem reduces to a Belief Propagation Network simulation. We also present preliminary separation performance results using TIMET database.