DocumentCode
744930
Title
Overcomplete source separation using Laplacian mixture models
Author
Mitianoudis, Nikolaos ; Stathaki, Tania
Author_Institution
Electr. & Electron. Eng. Dept., Imperial Coll. London, UK
Volume
12
Issue
4
fYear
2005
fDate
4/1/2005 12:00:00 AM
Firstpage
277
Lastpage
280
Abstract
The authors explore the use of Laplacian mixture models (LMMs) to address the overcomplete blind source separation problem in the case that the source signals are very sparse. A two-sensor setup was used to separate an instantaneous mixture of sources. A hard and a soft decision scheme were introduced to perform separation. The algorithm exhibits good performance as far as separation quality and convergence speed are concerned.
Keywords
Laplace transforms; blind source separation; optimisation; sensors; EM; LMM; Laplacian mixture models; convergence speed; expectation-maximization algorithm; hard-soft decision scheme; overcomplete blind source separation; sparse source signal; two-sensor setup; Additive noise; Blind source separation; Convergence; Independent component analysis; Laplace equations; Noise robustness; Probability distribution; Scattering; Signal processing algorithms; Source separation; Expectation-maximization (EM) algorithm; mixture models; overcomplete source separation;
fLanguage
English
Journal_Title
Signal Processing Letters, IEEE
Publisher
ieee
ISSN
1070-9908
Type
jour
DOI
10.1109/LSP.2005.843759
Filename
1407919
Link To Document