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Paper Abstract and Keywords
Presentation 2022-05-13 16:50
Basic study for permutation solver based on deep neural networks
Fumiya Hasuike, Rui Watanabe, Daichi Kitamura (NIT, Kagawa) EA2022-13
Abstract (in Japanese) (See Japanese page) 
(in English) This paper focuses on a permutation problem associated with frequency-domain independent component analysis (FDICA) that is a technique for (over-)determined blind audio source separation.
In FDICA, independent component analysis is applied to each of frequencies, and FDICA encounters the so-called permutation problem, which is a frequency-wise reordering problem of separated source components.
Thus, FDICA requires a permutation solver as post processing to obtain the separated source signals.
In this paper, we propose a new permutation solver based on a deep neural network (DNN), where DNN predicts a frequency-wise permutation matrix that aligns the order of estimated source components.
The validity of using DNN for solving the permutation problem is investigated via basic experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) independent component analysis / permutation problem / deep neural network / blind audio source separation / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 20, EA2022-13, pp. 62-67, May 2022.
Paper # EA2022-13 
Date of Issue 2022-05-06 (EA) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction
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)
Download PDF EA2022-13

Conference Information
Committee EA  
Conference Date 2022-05-13 - 2022-05-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EA 
Conference Code 2022-05-EA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Basic study for permutation solver based on deep neural networks 
Sub Title (in English)  
Keyword(1) independent component analysis  
Keyword(2) permutation problem  
Keyword(3) deep neural network  
Keyword(4) blind audio source separation  
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1st Author's Name Fumiya Hasuike  
1st Author's Affiliation National Institute of Technology, Kagawa College (NIT, Kagawa)
2nd Author's Name Rui Watanabe  
2nd Author's Affiliation National Institute of Technology, Kagawa College (NIT, Kagawa)
3rd Author's Name Daichi Kitamura  
3rd Author's Affiliation National Institute of Technology, Kagawa College (NIT, Kagawa)
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Speaker Author-1 
Date Time 2022-05-13 16:50:00 
Presentation Time 25 minutes 
Registration for EA 
Paper # EA2022-13 
Volume (vol) vol.122 
Number (no) no.20 
Page pp.62-67 
#Pages
Date of Issue 2022-05-06 (EA) 


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