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SUPER-EXPONENTIAL ALGORITHMS AND EIGENVECTOR ALGORITHMS FOR BLIND SOURCE SEPARATIOIN
http://hdl.handle.net/2237/10429
http://hdl.handle.net/2237/10429eb85e53a-9a64-4c09-a584-1685bb79820e
名前 / ファイル | ライセンス | アクション |
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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公開日 | 2008-08-22 | |||||
タイトル | ||||||
タイトル | SUPER-EXPONENTIAL ALGORITHMS AND EIGENVECTOR ALGORITHMS FOR BLIND SOURCE SEPARATIOIN | |||||
言語 | en | |||||
著者 |
Ito, Masanori
× Ito, Masanori× Kawamoto, Mitsuru× Ohnishi, Noboru× Inouye, Yujiro |
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アクセス権 | ||||||
アクセス権 | open access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
抄録 | ||||||
内容記述 | In this paper we deal with the blind source separation (BSS) problem in the frequency domain. To solve this problem we adopt the idea of super-exponential methods (SEM) and eigenvector algorithms (EVA) with reference signals. SEMs have the attractive property that they are computationally efficient and converge to the desired solutions at a super-exponential rate. Conventional SEMs have the problem that they are sensitive to Gaussian noise. To overcome this issue, we propose the robust SEM, which utilize only the higher-order cumulants. SEMs, however, have the drawback that they fail to extract all the source signals due to the failure of the deflation process. To compensate for this drawback, we propose the EVA with reference signals, which makes it possible to extract all the sources. | |||||
言語 | en | |||||
内容記述タイプ | Abstract | |||||
出版者 | ||||||
言語 | en | |||||
出版者 | INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源 | http://purl.org/coar/resource_type/c_5794 | |||||
タイプ | conference paper | |||||
書誌情報 |
en : 4th Symposium on "Intelligent Media Integration for Social Information Infrastructure" December 7-8, 2006 p. 39-44, 発行日 2006-12 |
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application/pdf | ||||||
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値 | publisher | |||||
URI | ||||||
識別子 | http://hdl.handle.net/2237/10429 | |||||
識別子タイプ | HDL |