A blind source separation of instantaneous acoustic mixtures using natural gradient method
A variety of applications concerning communication signal processing involves recovering unobserved signals or 'sources' from several observed mixtures, and 'cocktail party effect' is a good paradigm related to this process. Given a set of linearly superimposed acoustic signals w...
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| Main Authors: | , |
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| Format: | text |
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Animo Repository
2012
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| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/2343 https://animorepository.dlsu.edu.ph/context/faculty_research/article/3342/type/native/viewcontent |
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| Institution: | De La Salle University |
| Summary: | A variety of applications concerning communication signal processing involves recovering unobserved signals or 'sources' from several observed mixtures, and 'cocktail party effect' is a good paradigm related to this process. Given a set of linearly superimposed acoustic signals without knowledge about the sources makes Blind Source Separation (BSS) a very suitable scheme. A more popular approach of BSS, Independent Component Analysis, has been exploited which basically senses the statistical independence of the source signal estimates to achieve separation. A set of interfering signals present in a typical acoustic environment has been instantaneously combined with a pre-determined mixing matrix. A great weight has been given on an excellent rendition of the Infomax technique of Independent Component Analysis (ICA), called the Natural Gradient Method, to employ a cost function that would yield an optimized de-mixing matrix, producing fairly estimated source signals. By varying the learning rate and the score function, a robust performance of the Natural Gradient has been exhibited, maximizing the separation quality, stability and convergence speed. © 2012 IEEE. |
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