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Paper Abstract and Keywords
Presentation 2024-05-22 15:20
An anomalous sound detection for industrial machines using acoustical features related to timbral-based metrics
Yasuji Ota, Ryoya Ogura, Masashi Unoki (JAIST) EA2024-6
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we proposed a method for detecting anomaly sound of industrial equipment based on timbre-related features that combine sound quality metrics (SQMs) and acoustical features. We investigated the differences in auditory perception of typical disturbance factors and anomaly sounds for four types of industrial equipment, and then correlated them with SQMs from the perspective of timbre attributes. Based on this, we devised timbre-related features that are combined SQMs with two types of acoustical features. We constructed an anomaly sound detection system that performs classification using a support vector machine based on these features. We evaluated performance of the proposed method using an industrial equipment operation sound data set. As results, we found that the F measure was 0.920, which showed excellent anomaly sound classification performance. In addition, we verified the distribution separability of feature quantities for anomaly sounds using a likelihood scale based on probabilistic statistics. It was also found that the ROC-AUC was 0.856. These results demonstrated from multiple perspectives that the proposed method has the effectiveness of feature quantities for anomaly sounds.
Keyword (in Japanese) (See Japanese page) 
(in English) anomaly sound detection / sound quality metrics / acoustical features / timbre-related features / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 42, EA2024-6, pp. 23-28, May 2024.
Paper # EA2024-6 
Date of Issue 2024-05-15 (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 EA2024-6

Conference Information
Committee EA  
Conference Date 2024-05-22 - 2024-05-22 
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 2024-05-EA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An anomalous sound detection for industrial machines using acoustical features related to timbral-based metrics 
Sub Title (in English)  
Keyword(1) anomaly sound detection  
Keyword(2) sound quality metrics  
Keyword(3) acoustical features  
Keyword(4) timbre-related features  
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1st Author's Name Yasuji Ota  
1st Author's Affiliation Japan Advanced Institute of Science and Technology (JAIST)
2nd Author's Name Ryoya Ogura  
2nd Author's Affiliation Japan Advanced Institute of Science and Technology (JAIST)
3rd Author's Name Masashi Unoki  
3rd Author's Affiliation Japan Advanced Institute of Science and Technology (JAIST)
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Speaker Author-3 
Date Time 2024-05-22 15:20:00 
Presentation Time 25 minutes 
Registration for EA 
Paper # EA2024-6 
Volume (vol) vol.124 
Number (no) no.42 
Page pp.23-28 
#Pages 6 
Date of Issue 2024-05-15 (EA) 

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