Presentation | 2009-10-23 Estimation of an un-mixed matrix for blind source separation with least-squares joint diagonalization problem Shinya SAITO, Kunio OISHI, Hajime KUBOTA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We present a new learning algorithm for solving a least-squares joint diagonalization problem at every frequency bin in convolutive frequency-domain blind source separation. Conventional algorithm requires computational burden for computing inverse matrix at every frequency bin. The proposed algorithm employs the matrix inversion lemma. It is useful to recursively estimate un-mixed matrix without being affected by initializing the algorithm. The performance of the algorithm will be evaluated in real reverberant environments. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | frequency-domain blind source separation / joint diagonalization / inversion lemma / convolutive mixture |
Paper # | EA2009-68 |
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Committee | EA |
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Conference Date | 2009/10/15(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Engineering Acoustics (EA) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Estimation of an un-mixed matrix for blind source separation with least-squares joint diagonalization problem |
Sub Title (in English) | |
Keyword(1) | frequency-domain blind source separation |
Keyword(2) | joint diagonalization |
Keyword(3) | inversion lemma |
Keyword(4) | convolutive mixture |
1st Author's Name | Shinya SAITO |
1st Author's Affiliation | Graduate School of Engineering, Chiba Institute of Technology() |
2nd Author's Name | Kunio OISHI |
2nd Author's Affiliation | Graduate School of Bionics, Computer and Media Sciences, Tokyo University of Technology |
3rd Author's Name | Hajime KUBOTA |
3rd Author's Affiliation | Graduate School of Engineering, Chiba Institute of Technology |
Date | 2009-10-23 |
Paper # | EA2009-68 |
Volume (vol) | vol.109 |
Number (no) | 240 |
Page | pp.pp.- |
#Pages | 6 |
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