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Paper Abstract and Keywords
Presentation 2022-06-18 13:00
Anomalous sound detection using multi-class classifier and reconstructor of its intermediate layer output
Keita Matsumoto, Takeshi Yamada (Univ. of Tsukuba), Shoji Makino (Waseda Univ./Univ. of Tsukuba) SP2022-18
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, there has been a growing demand for techniques to detect unknown anomalous sounds by unsupervised learning using only normal sound data. Currently, the most popular method is based on reconstruction of normal sound data. This method uses an autoencoder to reconstruct the input data, and uses the reconstruction error as the anomalous score for anomalous detection. A method combining a binary classifier for normal/anomaly and distance learning in the latent space was recently proposed, assuming that virtual anomalous sound data is prepared at the time of learning. This method learns a binary classifier using normal and virtual anomalous sound data, and incorporates an index based on the center of gravity of the normal and anomalous classes in the latent space into the loss function and anomalous score. In this paper, we integrate these methods to further improve performance.
Specifically, we propose a method that combines a multi-class classifier, distance learning on the latent space, and a reconstructor of the intermediate layer output.
Experimental results using DCASE2020 Task 2, a task for anomalous sound detection, confirm the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) anomalous sound detection / multiclass classifier / reconstructor / metric learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 81, SP2022-18, pp. 77-81, June 2022.
Paper # SP2022-18 
Date of Issue 2022-06-10 (SP) 
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 SP2022-18

Conference Information
Committee SP IPSJ-MUS IPSJ-SLP  
Conference Date 2022-06-17 - 2022-06-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2022-06-SP-MUS-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Anomalous sound detection using multi-class classifier and reconstructor of its intermediate layer output 
Sub Title (in English)  
Keyword(1) anomalous sound detection  
Keyword(2) multiclass classifier  
Keyword(3) reconstructor  
Keyword(4) metric learning  
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1st Author's Name Keita Matsumoto  
1st Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
2nd Author's Name Takeshi Yamada  
2nd Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
3rd Author's Name Shoji Makino  
3rd Author's Affiliation Waseda University/University of Tsukuba (Waseda Univ./Univ. of Tsukuba)
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Speaker Author-1 
Date Time 2022-06-18 13:00:00 
Presentation Time 120 minutes 
Registration for SP 
Paper # SP2022-18 
Volume (vol) vol.122 
Number (no) no.81 
Page pp.77-81 
#Pages 5 
Date of Issue 2022-06-10 (SP) 

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