Paper Abstract and Keywords |
Presentation |
2020-01-10 10:25
Examination of abnormal sound detection method of machine using standard deviation of multiple models Akihio Ito, HIroyuki Nishi, Manbu Okamoto (Sojo Univ..) ICM2019-37 LOIS2019-52 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Along with the progress of super aged society, the number of elderlies living alone has increased. Therefore, the demand for services to watch the daily life of the elderly is increasing. Instead, video cameras and pendant sensors have problems such as privacy infringement and annoyance to wear things. Therefore, the authors have proposed a watching system that detects abnormal life sounds using a neural network. However, it is extremely difficult to collect all the abnormal life sounds for learning. Therefore, if the input sound has been learned in advance or not, and if an unlearned sound (unlearned sound) is detected, the user is designated as normal and normal. By adding the sound to the learning sound, the learning sound is expanded, and if there is no input that it is normal, a system that judges that it is abnormal is examined.
Ten models with different initial weight values were prepared, and the experiment showed that the standard deviation of the output values differed between learned and unlearned. As a result of experiments, the performance improved compared to the conventional method.
In this study, we use the public data set ToyADMOS to detect abnormal sounds and unlearned sounds. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Elderly people / Observation / Life sound / Neural network / Untrained acoustic data / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 359, LOIS2019-52, pp. 39-44, Jan. 2020. |
Paper # |
LOIS2019-52 |
Date of Issue |
2020-01-02 (ICM, LOIS) |
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) |
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ICM2019-37 LOIS2019-52 |
Conference Information |
Committee |
LOIS ICM |
Conference Date |
2020-01-09 - 2020-01-10 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
ARKAS SASEBO |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Practical Use of Lifelog, Office Information System, Business Management, etc. |
Paper Information |
Registration To |
LOIS |
Conference Code |
2020-01-LOIS-ICM |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Examination of abnormal sound detection method of machine using standard deviation of multiple models |
Sub Title (in English) |
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Keyword(1) |
Elderly people |
Keyword(2) |
Observation |
Keyword(3) |
Life sound |
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Neural network |
Keyword(5) |
Untrained acoustic data |
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1st Author's Name |
Akihio Ito |
1st Author's Affiliation |
Sojo University (Sojo Univ..) |
2nd Author's Name |
HIroyuki Nishi |
2nd Author's Affiliation |
Sojo University (Sojo Univ..) |
3rd Author's Name |
Manbu Okamoto |
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Sojo University (Sojo Univ..) |
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Speaker |
Author-1 |
Date Time |
2020-01-10 10:25:00 |
Presentation Time |
25 minutes |
Registration for |
LOIS |
Paper # |
ICM2019-37, LOIS2019-52 |
Volume (vol) |
vol.119 |
Number (no) |
no.358(ICM), no.359(LOIS) |
Page |
pp.39-44 |
#Pages |
6 |
Date of Issue |
2020-01-02 (ICM, LOIS) |
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