Overcomplete source separation using Laplacian mixture models

Nikolaos Mitianoudis

doi:10.1109/LSP.2005.843759

Overcomplete source separation using Laplacian mixture models

2005-04-01

18

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AI

DOI：10.1109/LSP.2005.843759

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In this letter, 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.

Keyword:

expectation-maximization (EM) algorithm

mixture models

overcomplete source separation

Overcomplete source separation using Laplacian mixture models

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