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Paper Abstract and Keywords
Presentation 2023-11-23 13:00
[Poster Presentation] A Study of Complexity Reduction for Classification of Musical Instruments Using Element Selection
Ryu Kato, Natsuki Ueno, Nobutaka Ono (Tokyo Metropolitan Univ.), Ryo Matsuda, Kazunobu Kondo (Yamaha Corp.) EA2023-37 EMM2023-68
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we propose complexity reduction in convolutional-neural-network (CNN)-based music instruments classification by incorporating an element selection method. Classification of musical instruments is a fundamental technic in various applications such as automatic transcription, musical information retrieval, and automatic application of audio effects. Reducing computational costs is desired in some of these applications. In contrast, element selection is a method that extracts specific components from a feature vector to achieve a lower-dimensional representation. Unlike other dimension reduction techniques like Principal Component Analysis, it offers the advantage of not requiring multiplication. In our study, we aim to reduce the computational complexity in both CNN and the dimension reduction process itself by applying element selection to the input features. The selected elements are optimized to minimize the mean reconstruction error. We experimentally evaluated the changes in computation time and estimation accuracy using a dataset of musical instrument sounds. Our proposed method showed lower degradation in estimation performance compared to random element selection. Additionally, we confirmed that the dimension reduction technique using element selection enables instrument sound classification in a shorter computation time.
Keyword (in Japanese) (See Japanese page) 
(in English) convolutional neural network / musical instruments classification / dimensyonality reduction / element selection / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 278, EA2023-37, pp. 51-56, Nov. 2023.
Paper # EA2023-37 
Date of Issue 2023-11-16 (EA, EMM) 
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)
Download PDF EA2023-37 EMM2023-68

Conference Information
Committee EMM EA ASJ-H  
Conference Date 2023-11-23 - 2023-11-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) [Beginners Session] Engineering/Electro Acoustics, Content Processing, Digital Watermarking, Psychological and Physiological Acoustics, and Related Topics 
Paper Information
Registration To EA 
Conference Code 2023-11-EMM-EA-H 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Complexity Reduction for Classification of Musical Instruments Using Element Selection 
Sub Title (in English)  
Keyword(1) convolutional neural network  
Keyword(2) musical instruments classification  
Keyword(3) dimensyonality reduction  
Keyword(4) element selection  
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1st Author's Name Ryu Kato  
1st Author's Affiliation Tokyo Matropolitan University (Tokyo Metropolitan Univ.)
2nd Author's Name Natsuki Ueno  
2nd Author's Affiliation Tokyo Matropolitan University (Tokyo Metropolitan Univ.)
3rd Author's Name Nobutaka Ono  
3rd Author's Affiliation Tokyo Matropolitan University (Tokyo Metropolitan Univ.)
4th Author's Name Ryo Matsuda  
4th Author's Affiliation Yamaha Corporation (Yamaha Corp.)
5th Author's Name Kazunobu Kondo  
5th Author's Affiliation Yamaha Corporation (Yamaha Corp.)
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Speaker Author-1 
Date Time 2023-11-23 13:00:00 
Presentation Time 150 minutes 
Registration for EA 
Paper # EA2023-37, EMM2023-68 
Volume (vol) vol.123 
Number (no) no.278(EA), no.279(EMM) 
Page pp.51-56 
#Pages 6 
Date of Issue 2023-11-16 (EA, EMM) 

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