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Paper Abstract and Keywords
Presentation 2021-07-16 13:00
Sound event detection based on complementary-label learning
Keigo Wakayama, Shoichiro Saito (NTT) EA2021-17
Abstract (in Japanese) (See Japanese page) 
(in English) Sound Event Detection (SED) is an important research field that can be applied to smart cities, and etc. SED estimate the start and end of events in addition to the presence or absence of events in acoustic data. To improve the estimation accuracy, a lot of labeled data is required, but it is difficult to assign a lot of correct labels. Considering effectively utilizing the data with the complementary label (class that is not the correct answer), therefore, we propose a method to realize SED by learning model from complementary labeled data. The effectiveness of the method is confirmed experimentally.
Keyword (in Japanese) (See Japanese page) 
(in English) Sound Event Detection / Audio Tagging / Weakly-supervised learning / Complementary-label learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 112, EA2021-17, pp. 77-82, July 2021.
Paper # EA2021-17 
Date of Issue 2021-07-08 (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 EA2021-17

Conference Information
Committee EA ASJ-H  
Conference Date 2021-07-15 - 2021-07-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Engineering/Electro Acoustics, Psychological and Physiological Acoustics, Speech, Musical Acoustics, Education in Acoustics, and Related Topics 
Paper Information
Registration To EA 
Conference Code 2021-07-EA-H 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Sound event detection based on complementary-label learning 
Sub Title (in English)  
Keyword(1) Sound Event Detection  
Keyword(2) Audio Tagging  
Keyword(3) Weakly-supervised learning  
Keyword(4) Complementary-label learning  
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1st Author's Name Keigo Wakayama  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Shoichiro Saito  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2021-07-16 13:00:00 
Presentation Time 25 minutes 
Registration for EA 
Paper # EA2021-17 
Volume (vol) vol.121 
Number (no) no.112 
Page pp.77-82 
#Pages
Date of Issue 2021-07-08 (EA) 


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