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Paper Abstract and Keywords
Presentation 2017-08-24 15:05
Anomaly Detection from Subsampled Audio Signal for Machine Diagnosis
Yohei Kawaguchi, Takashi Endo (Hitachi) SIP2017-54
Abstract (in Japanese) (See Japanese page) 
(in English) Low-cost sound monitoring is required for maintaining machinery. We aim to reduce the cost of monitoring by reducing the sampling rate, i.e., sub-Nyquist sampling. Monitoring based on sub-Nyquist sampling requires two sub-systems; a sub-system on-site for sampling machinery sound at a low rate, and a sub-system off-site for detecting anomalies from the subsampled signal. In this paper, to achieve both subsystems, we apply non-uniform sampling methods and propose an anomaly detection method based on a demultiplexer and an end-to-end LSTM autoencoder.
Keyword (in Japanese) (See Japanese page) 
(in English) machine maintenance system / sound monitoring / sound diagnosis / predictive maintenance / sub-Nyquist non-uniform sampling / end-to-end / long short-term memory (LSTM) / autoencoder  
Reference Info. IEICE Tech. Rep., vol. 117, no. 180, SIP2017-54, pp. 33-38, Aug. 2017.
Paper # SIP2017-54 
Date of Issue 2017-08-17 (SIP) 
ISSN Print edition: ISSN 0913-5685    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 2017-08-24 - 2017-08-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo Denki University 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2017-08-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Anomaly Detection from Subsampled Audio Signal for Machine Diagnosis 
Sub Title (in English)  
Keyword(1) machine maintenance system  
Keyword(2) sound monitoring  
Keyword(3) sound diagnosis  
Keyword(4) predictive maintenance  
Keyword(5) sub-Nyquist non-uniform sampling  
Keyword(6) end-to-end  
Keyword(7) long short-term memory (LSTM)  
Keyword(8) autoencoder  
1st Author's Name Yohei Kawaguchi  
1st Author's Affiliation Hitachi, Ltd. (Hitachi)
2nd Author's Name Takashi Endo  
2nd Author's Affiliation Hitachi, Ltd. (Hitachi)
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Speaker Author-1 
Date Time 2017-08-24 15:05:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # SIP2017-54 
Volume (vol) vol.117 
Number (no) no.180 
Page pp.33-38 
#Pages
Date of Issue 2017-08-17 (SIP) 


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