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Paper Abstract and Keywords
Presentation 2017-03-01 12:40
[Poster Presentation] Estimation of Music Genres from Spontaneous Brain Activity Analysis by Using Neural Network
Hiroki Itoga, Yoshikazu Washizawa (UEC) EA2016-103 SIP2016-158 SP2016-98
Abstract (in Japanese) (See Japanese page) 
(in English) Quantitative evaluation of the mind states has been addressed for a long time. Evaluated mind states can be applied for many applications. For example, recommendation technology such as music is researched widely. Some of them utilized EEG for emotional states estimation. In this study, music genres estimated by EEG and features were effective for genre estimation are studied. In 5 class genre recognition, we achieved 38.1±9.4% recog- nition accuracy, it is higher than chance level, 20%. The features that are effective for genre estimation differs for each subject, however the result showed an association with emotional states.
Keyword (in Japanese) (See Japanese page) 
(in English) EEG / Spontaneous brain activity analysis / Neural Networks / Genre recognition / Feature Extraction / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 476, SIP2016-158, pp. 119-122, March 2017.
Paper # SIP2016-158 
Date of Issue 2017-02-22 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685    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)
Download PDF EA2016-103 SIP2016-158 SP2016-98

Conference Information
Committee SP SIP EA  
Conference Date 2017-03-01 - 2017-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics 
Paper Information
Registration To SIP 
Conference Code 2017-03-SP-SIP-EA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimation of Music Genres from Spontaneous Brain Activity Analysis by Using Neural Network 
Sub Title (in English)  
Keyword(1) EEG  
Keyword(2) Spontaneous brain activity analysis  
Keyword(3) Neural Networks  
Keyword(4) Genre recognition  
Keyword(5) Feature Extraction  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Hiroki Itoga  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Yoshikazu Washizawa  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2017-03-01 12:40:00 
Presentation Time 90 minutes 
Registration for SIP 
Paper # EA2016-103, SIP2016-158, SP2016-98 
Volume (vol) vol.116 
Number (no) no.475(EA), no.476(SIP), no.477(SP) 
Page pp.119-122 
#Pages 4 
Date of Issue 2017-02-22 (EA, SIP, SP) 

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