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Paper Abstract and Keywords
Presentation 2017-03-21 16:50
On Data Extension For Signal Data
Shu Eguchi, Masaru Tanaka, Jun Fujiki (Fukuoka Univ.), Takio Kurita (Hiroshima Univ.) BioX2016-73 PRMU2016-236
Abstract (in Japanese) (See Japanese page) 
(in English) Efficient learning using limited data in machine learning is also important now even in the era of big data. In the case of image identification, data expansion such as parallel movement, specular inversion, coloring or the like is performed based on the original sample to increase variations and increase the number of data. In this research, we extend data to musical instrument sound data. Specifically, the instrument sound is converted into an image graphically represented by the reproduction time and the sample frequency, and the data is expanded by coloring the image. By coloring, it is possible to obtain as much information as simply being expanded to 3 ch than the original signal data. Then, learning is performed using GoogLeNet implemented by Chainer which is a deep learning tool using this extended data as input data. With this system, learning to recognize a musical instrument from instrumental sounds created with three kinds of instruments such as flute, oboe and fagott, it is possible to expand data similar to image identification even for signal processing confirmed.
Keyword (in Japanese) (See Japanese page) 
(in English) instrument sound / musical chord / signal processing / image recognition / chainer / ata extension / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 528, PRMU2016-236, pp. 237-242, March 2017.
Paper # PRMU2016-236 
Date of Issue 2017-03-13 (BioX, PRMU) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 PRMU BioX  
Conference Date 2017-03-20 - 2017-03-21 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2017-03-PRMU-BioX 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) On Data Extension For Signal Data 
Sub Title (in English)  
Keyword(1) instrument sound  
Keyword(2) musical chord  
Keyword(3) signal processing  
Keyword(4) image recognition  
Keyword(5) chainer  
Keyword(6) ata extension  
1st Author's Name Shu Eguchi  
1st Author's Affiliation Fukuoka University (Fukuoka Univ.)
2nd Author's Name Masaru Tanaka  
2nd Author's Affiliation Fukuoka University (Fukuoka Univ.)
3rd Author's Name Jun Fujiki  
3rd Author's Affiliation Fukuoka University (Fukuoka Univ.)
4th Author's Name Takio Kurita  
4th Author's Affiliation Hiroshima University (Hiroshima Univ.)
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Speaker Author-1 
Date Time 2017-03-21 16:50:00 
Presentation Time 25 minutes 
Registration for PRMU 
Paper # BioX2016-73, PRMU2016-236 
Volume (vol) vol.116 
Number (no) no.527(BioX), no.528(PRMU) 
Page pp.237-242 
Date of Issue 2017-03-13 (BioX, PRMU) 

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