Minimum entropy algorithms for source separation
The minimum entropy or maximum likelihood estimation can be utilized in blind source separation problem. Based on the local generalized Gaussian probability density model, a set of general anti-Hebbian rules can be derived. This set of adaptation rules give promising results when we test the real re...
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| Veröffentlicht in: | Circuits and Systems; Proceedings: Midwest Symposium on Circuits and Systems S. 242 - 245 |
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| Hauptverfasser: | , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
1998
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| ISBN: | 9780818689147, 0818689145 |
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| Abstract | The minimum entropy or maximum likelihood estimation can be utilized in blind source separation problem. Based on the local generalized Gaussian probability density model, a set of general anti-Hebbian rules can be derived. This set of adaptation rules give promising results when we test the real recordings. |
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| AbstractList | The minimum entropy or maximum likelihood estimation can be utilized in blind source separation problem. Based on the local generalized Gaussian probability density model, a set of general anti-Hebbian rules can be derived. This set of adaptation rules give promising results when we test the real recordings. |
| Author | Principe, J.C. Hsiao-Chun Wu |
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| Snippet | The minimum entropy or maximum likelihood estimation can be utilized in blind source separation problem. Based on the local generalized Gaussian probability... |
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| StartPage | 242 |
| SubjectTerms | Blind source separation Entropy Equations Finite impulse response filter Gaussian distribution Maximum likelihood estimation Noise reduction Probability density function Source separation Testing |
| Title | Minimum entropy algorithms for source separation |
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