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Paper Abstract and Keywords
Presentation 2021-03-03 13:05
[Invited Talk] *
Masahito Togami (LINE) EA2020-64 SIP2020-95 SP2020-29
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, deep learning based speech source separation has been evolved rapidly. A neural network (NN) is usually learned independently
of a spatial model. However, a research question remains whether the NN that is trained such as configuration is really optimal
when speech source separation is performed with the spatial model. In this talk, I will introduce conventional statistical model based
speech source separation and deep learning based speech source separation. After that, I will introduce four research directions which
incorporate a spatial model into the NN structure, i.e. 1) Loss function of the NN that considers the spatial model, 2)Insertion of speech source separation
with the spatial model into the NN structure, 3) A NN framework which estimates parameters for speech source separation with a direction-of-arrival attractor,
and 4) Unsupervised learning of NN which utilizes statistical model based speech source separation as a pseudo clean signal generator.
Keyword (in Japanese) (See Japanese page) 
(in English) spatial model / speech source separation / deep learning / unsupervised learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 397, EA2020-64, pp. 27-32, March 2021.
Paper # EA2020-64 
Date of Issue 2021-02-24 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 EA2020-64 SIP2020-95 SP2020-29

Conference Information
Conference Date 2021-03-03 - 2021-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, Ultrasonics, and Related Topics 
Paper Information
Registration To EA 
Conference Code 2021-03-EA-US-SP-SIP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English)
Sub Title (in English)  
Keyword(1) spatial model  
Keyword(2) speech source separation  
Keyword(3) deep learning  
Keyword(4) unsupervised learning  
1st Author's Name Masahito Togami  
1st Author's Affiliation LINE (LINE)
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Speaker Author-1 
Date Time 2021-03-03 13:05:00 
Presentation Time 50 minutes 
Registration for EA 
Paper # EA2020-64, SIP2020-95, SP2020-29 
Volume (vol) vol.120 
Number (no) no.397(EA), no.398(SIP), no.399(SP) 
Page pp.27-32 
Date of Issue 2021-02-24 (EA, SIP, SP) 

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