DocumentCode :
2459187
Title :
MAP Source Separation using Belief Propagation Networks
Author :
Balan, Radu ; Rosca, Justinian
Author_Institution :
Siemens Corp. Res., Princeton, NJ
fYear :
2006
fDate :
Oct. 29 2006-Nov. 1 2006
Firstpage :
1402
Lastpage :
1406
Abstract :
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.
Keywords :
Markov processes; belief networks; learning (artificial intelligence); maximum likelihood estimation; optimisation; probability; source separation; spectral analysis; Bernoulli selection variable; MAP signal estimator; MAP source separation; belief propagation network; calibration; dynamic sparse source signal model; first order Markov process; frequency domain; optimization problem; parameter estimation; spectral amplitude variable; training database; transition probability; Belief propagation; Calibration; Databases; Frequency domain analysis; Hidden Markov models; Markov processes; Random variables; Sensor arrays; Source separation; Time frequency analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Systems and Computers, 2006. ACSSC '06. Fortieth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
ISSN :
1058-6393
Print_ISBN :
1-4244-0784-2
Electronic_ISBN :
1058-6393
Type :
conf
DOI :
10.1109/ACSSC.2006.354988
Filename :
4176798
Link To Document :
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