DocumentCode :
3631361
Title :
A hybrid method for deconvolution of Bernoulli-Gaussian processes
Author :
Sinan Yildirim;A. Taylan Cemgil;Aysin B. Ertuzun
Author_Institution :
Department of Electrical and Electronics Engineering, Bo?azi?i University, Bebek, ?stanbul, Turkey
fYear :
2009
fDate :
4/1/2009 12:00:00 AM
Firstpage :
3417
Lastpage :
3420
Abstract :
We investigate a hybrid method which improves the quality of state inference and parameter estimation in blind deconvolution of a sparse source modeled by a Bernoulli-Gaussian process. In this problem, when both the signal and the filter are jointly estimated, the true posterior is typically highly multimodal. Therefore, when not properly initialized, standard stochastic inference methods, (MCEM, SEM or SAEM), tend to get stuck and suffer from poor convergence. In our approach, we first relax the Bernoulli-Gaussian prior model by a Student-t model. Our simulations suggest that deterministic inference in the relaxed model is not only efficient, but also provides a very good initialization for the Bernoulli-Gaussian model. We provide simulation studies that compare the results obtained with and without our initialization method for several combinations of state inference and parameter estimation methods used for the Bernoulli-Gaussian model.
Keywords :
"Deconvolution","Parameter estimation","Random variables","Filters","Optimization methods","Gaussian noise","Source separation","Filtering","Stochastic processes","Convergence"
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
2379-190X
Type :
conf
DOI :
10.1109/ICASSP.2009.4960359
Filename :
4960359
Link To Document :
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