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Overcomplete source separation using Laplacian mixture models
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DOI：10.1109/LSP.2005.843759.png)
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Abstract
En 中文
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.
Keywords:
expectation-maximization (EM) algorithm
mixture models
overcomplete source separation
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
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