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Paper Abstract and Keywords
Presentation 2012-06-08 15:10
Performance comparison of MMSE and sparse models in source separation using multichannel Wiener filter
Ryutaro Sakanashi, Shigeki Miyabe, Takeshi Yamada, Shoji Makino (Univ. Tsukuba) EA2012-43
Abstract (in Japanese) (See Japanese page) 
(in English) Multichannel Wiener filter proposed by Duong {\it et al}. can conduct underdetermined blind source separation (BSS) with low distortion. This method assumes that the observed signal is the superimposition of the multichannel source images generated from multivariate normal distributions. The covariance matrix in each time-frequency slot is estimated by EM algorithm which treats the source images as the latent variables. Using the estimated parameters, the source images are separated as the maximum a posteriori estimate. It is worth nothing that this method does not use assumption of sparseness, which is usually used in underdetermined BSS. In this paper we investigate the effectiveness of the three features of Duong's method, i.e., the source image model with multivariate normal distribution, the observation model without sparseness assumption, and the multichannel Wiener filter. We newly formulate the BSS method with the similar source image model and the different observation model assuming sparseness, and compare with the Duong's method and the conventional binary masking. Experimental results confirmed the effectiveness of all the three features of Duong's method.
Keyword (in Japanese) (See Japanese page) 
(in English) Underdetermined blind source separation / sparseness / time-frequency masking / multivariate normal distribution / EM algorithm / multichannel Wiener filter / /  
Reference Info. IEICE Tech. Rep., vol. 112, no. 76, EA2012-43, pp. 61-66, June 2012.
Paper # EA2012-43 
Date of Issue 2012-06-01 (EA) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee EA  
Conference Date 2012-06-08 - 2012-06-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Yamaha Co. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General topics 
Paper Information
Registration To EA 
Conference Code 2012-06-EA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Performance comparison of MMSE and sparse models in source separation using multichannel Wiener filter 
Sub Title (in English)  
Keyword(1) Underdetermined blind source separation  
Keyword(2) sparseness  
Keyword(3) time-frequency masking  
Keyword(4) multivariate normal distribution  
Keyword(5) EM algorithm  
Keyword(6) multichannel Wiener filter  
Keyword(7)  
Keyword(8)  
1st Author's Name Ryutaro Sakanashi  
1st Author's Affiliation University of Tsukuba (Univ. Tsukuba)
2nd Author's Name Shigeki Miyabe  
2nd Author's Affiliation University of Tsukuba (Univ. Tsukuba)
3rd Author's Name Takeshi Yamada  
3rd Author's Affiliation University of Tsukuba (Univ. Tsukuba)
4th Author's Name Shoji Makino  
4th Author's Affiliation University of Tsukuba (Univ. Tsukuba)
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Speaker Author-1 
Date Time 2012-06-08 15:10:00 
Presentation Time 25 minutes 
Registration for EA 
Paper # EA2012-43 
Volume (vol) vol.112 
Number (no) no.76 
Page pp.61-66 
#Pages
Date of Issue 2012-06-01 (EA) 


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