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Paper Abstract and Keywords
Presentation 2017-03-14 13:00
Analysis of Coin Falling Sound Using Machine Leaning
Wataru Yokota, Yasunari Obuchi (Tokyo Univ Tech)
Abstract (in Japanese) (See Japanese page) 
(in English) As a method of discriminating invisible part information, there is a hammering test for discriminating an object using a sound signal. In this paper, we deal with coin falling sounds as an example of such sound classification. In conventional coin discrimination studies, humans have been analyzing a single speech feature. However, it is difficult to accurately analyze many speech features by this method. It is necessary to accurately analyze large quantities of features. Combining machine learning with sound signals analysis seems to make it possible to analyze many feature quantities more accurately. For this reason, we analyzed sounds of popular five-yen and ten-yen coins falling into various materials using machine learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine Leaning / Coin / Sound Signal / Feature extraction / / / /  
Reference Info. ITE Tech. Rep., vol. 41, pp. 261-264, March 2017.
Paper #  
Date of Issue 2017-03-07 (AIT) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Conference Date 2017-03-14 - 2017-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Ochanomizu Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To AS 
Conference Code 2017-03-AIT-IIEEJ-AS-ARTS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis of Coin Falling Sound Using Machine Leaning 
Sub Title (in English)  
Keyword(1) Machine Leaning  
Keyword(2) Coin  
Keyword(3) Sound Signal  
Keyword(4) Feature extraction  
1st Author's Name Wataru Yokota  
1st Author's Affiliation Tokyo University of Technology (Tokyo Univ Tech)
2nd Author's Name Yasunari Obuchi  
2nd Author's Affiliation Tokyo University of Technology (Tokyo Univ Tech)
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Speaker Author-1 
Date Time 2017-03-14 13:00:00 
Presentation Time 90 minutes 
Registration for AS 
Paper # AIT2017-119 
Volume (vol) vol.41 
Number (no) no.12 
Page pp.261-264 
Date of Issue 2017-03-07 (AIT) 

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