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Paper Abstract and Keywords
Presentation 2021-08-23 13:25
A study on transfer learning in unsupervised anomalous sound detection based on deep metric learning considering variance of normal data
Hiroki Narita, Akira Tamamori (AIT) SIP2021-28
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, anomaly detection research in the field of computer vision has focused on methods based on transfer learning. Transfer learning can significantly reduce the training time of deep learning models for high-dimensional media data such as images, and has the advantage that anomaly detection models can be operated even when there is little training data. However, there have been few studies on the use of pre-trained models for acoustic anomaly detection, and the design of the model structure and loss function has not been investigated. In this study, we propose a method to improve the accuracy of anomalous sound detection by suppressing the variance of normal data extracted by the pre-trained model using distance learning. In the proposed method, acoustic features are extracted from the middle layer of ResNet, which is pre-trained with acoustic data, and are trained and inferred by the distance learning model in the later stage to detect anomlous sounds. We also applied feature extraction by transfer learning to the DCASE Challenge2020 Task2 dataset, and conducted evaluation experiments with several anomaly detection models including the proposed method. The experimental results show that the proposed method is more effective than the variational autoencoders and the Mahalanobis distance-based anomaly detection methods in achieving higher detection accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Anomalous sound detection / Unsupervised learning / Transfer learning / Deep metric Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 144, SIP2021-28, pp. 5-10, Aug. 2021.
Paper # SIP2021-28 
Date of Issue 2021-08-16 (SIP) 
ISSN Online edition: ISSN 2432-6380
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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)
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Conference Information
Committee SIP  
Conference Date 2021-08-23 - 2021-08-24 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2021-08-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study on transfer learning in unsupervised anomalous sound detection based on deep metric learning considering variance of normal data 
Sub Title (in English)  
Keyword(1) Anomalous sound detection  
Keyword(2) Unsupervised learning  
Keyword(3) Transfer learning  
Keyword(4) Deep metric Learning  
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1st Author's Name Hiroki Narita  
1st Author's Affiliation Aichi Institute Of Technology (AIT)
2nd Author's Name Akira Tamamori  
2nd Author's Affiliation Aichi Institute Of Technology (AIT)
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Speaker Author-1 
Date Time 2021-08-23 13:25:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # SIP2021-28 
Volume (vol) vol.121 
Number (no) no.144 
Page pp.5-10 
#Pages
Date of Issue 2021-08-16 (SIP) 


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