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Paper Abstract and Keywords
Presentation 2017-01-25 13:00
[Poster Presentation] Study on efficient solver for independent low-rank matrix analysis with sparse time-series-activity regularization
Yoshiki Mitsui (Univ. Tokyo), Daichi Kitamura (SOKENDAI), Shinnosuke Takamichi (Univ. Tokyo), Nobutaka Ono (NII/SOKENDAI), Hiroshi Saruwatari (Univ. Tokyo) EA2016-72
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a new blind source separation (BSS) method based on independent low-rank matrix analysis (ILRMA) with novel sparse regularization. ILRMA is a recently proposed BSS algorithm that simultaneously estimates a demixing matrix and source spectrogram models based on nonnegative matrix factorization (NMF). To improve the separation accuracy and stability, an additional constraint such as sparseness is needed but there have been no studies on this so far. In this study, we introduce an a priori statistical model for time-series amplitudes of source spectrograms, employing a new frequency-wise sparse regularization using estimates from the Bayesian postfilter to enhance the modeling accuracy. This regularization results in a bilevel optimization problem that consists of the estimation of a sparsity-emphasized source model using NMF and the separation of sources by ILRMA. In this paper, we present two approximated optimization schemes and their combination for performing regularized ILRMA. The efficacy of the proposed method is confirmed in a BSS experiment.
Keyword (in Japanese) (See Japanese page) 
(in English) blind source separation / nonnegative matrix factorization / independent low-rank matrix analysis / reguralization / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 420, EA2016-72, pp. 25-30, Jan. 2017.
Paper # EA2016-72 
Date of Issue 2017-01-18 (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 US  
Conference Date 2017-01-25 - 2017-01-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Doshisha Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) [Joint Meeting on Acoustics and Ultrasonics Subsociety] Engineering/Electro Acoustics, Ultrasonic and Underwater Acoustics, and Related Topics 
Paper Information
Registration To EA 
Conference Code 2017-01-EA-US 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on efficient solver for independent low-rank matrix analysis with sparse time-series-activity regularization 
Sub Title (in English)  
Keyword(1) blind source separation  
Keyword(2) nonnegative matrix factorization  
Keyword(3) independent low-rank matrix analysis  
Keyword(4) reguralization  
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1st Author's Name Yoshiki Mitsui  
1st Author's Affiliation The University of Tokyo (Univ. Tokyo)
2nd Author's Name Daichi Kitamura  
2nd Author's Affiliation The Graduate University for Advanced Studies (SOKENDAI)
3rd Author's Name Shinnosuke Takamichi  
3rd Author's Affiliation The University of Tokyo (Univ. Tokyo)
4th Author's Name Nobutaka Ono  
4th Author's Affiliation National Institute of Informatics/The Graduate University for Advanced Studies (NII/SOKENDAI)
5th Author's Name Hiroshi Saruwatari  
5th Author's Affiliation The University of Tokyo (Univ. Tokyo)
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Speaker Author-1 
Date Time 2017-01-25 13:00:00 
Presentation Time 90 minutes 
Registration for EA 
Paper # EA2016-72 
Volume (vol) vol.116 
Number (no) no.420 
Page pp.25-30 
#Pages
Date of Issue 2017-01-18 (EA) 


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