IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
... (for ESS/CS/ES/ISS)
Tech. Rep. Archives
... (for ES/CS)
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2015-03-05 16:15
Adaptation of Machine Learning Method for Music Structure Analysis
Yoshiyuki Kushibe, Toshiaki Takita (Univ. of Tsukuba), Masatoshi Hamanaka (Kyoto Univ.), Sakurako Yazawa, Junichi Hoshino (Univ. of Tsukuba) IBISML2014-89
Abstract (in Japanese) (See Japanese page) 
(in English) This paper describes the music structure analysis method using machine learning. Music structure analysis is to automatically extract the musical structures, such as verse and chorus, from the audio-based music data. In the previous work, the method using self-similarity matrix is proposed. It is the matrix to convert an audio data into a suitable feature sequence, and then to compare all elements of the sequence with each other. However, the result is poor accuracy for the music with the unexpected situation. There is a lot of factor becoming the obstacle of analysis, for example, change of the tempo, noise and change of musical instrument. We propose the method to learn the music to contain their factors using Deep Belief Networks which is a kind of machine learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Music Structure Analysis / Deep Belief Networks / Restricted Boltzmann Machine / / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 502, IBISML2014-89, pp. 31-38, March 2015.
Paper # IBISML2014-89 
Date of Issue 2015-02-26 (IBISML) 
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. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IBISML2014-89

Conference Information
Committee IBISML  
Conference Date 2015-03-05 - 2015-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Statistical mathematics, machine learning, data mining, and others 
Paper Information
Registration To IBISML 
Conference Code 2015-03-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Adaptation of Machine Learning Method for Music Structure Analysis 
Sub Title (in English)  
Keyword(1) Music Structure Analysis  
Keyword(2) Deep Belief Networks  
Keyword(3) Restricted Boltzmann Machine  
1st Author's Name Yoshiyuki Kushibe  
1st Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
2nd Author's Name Toshiaki Takita  
2nd Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
3rd Author's Name Masatoshi Hamanaka  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Sakurako Yazawa  
4th Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
5th Author's Name Junichi Hoshino  
5th Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Date Time 2015-03-05 16:15:00 
Presentation Time 30 
Registration for IBISML 
Paper # IEICE-IBISML2014-89 
Volume (vol) IEICE-114 
Number (no) no.502 
Page pp.31-38 
#Pages IEICE-8 
Date of Issue IEICE-IBISML-2015-02-26 

[Return to Top Page]

[Return to IEICE Web Page]

The Institute of Electronics, Information and Communication Engineers (IEICE), Japan