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Paper Abstract and Keywords
Presentation 2025-03-03 12:20
Memory-efficient and low-computational hierarchical musical instruments classification using element selection
Ryu Kato (Tokyo Metropolitan Univ.), Natsuki Ueno (Kumamoto Univ./), Nobutaka Ono (Tokyo Metropolitan Univ.), Ryo Matsuda, Kazunobu Kondo, Yu Takahashi (Yamaha Corp.) EA2024-111 SIP2024-146 SP2024-52
Abstract (in Japanese) (See Japanese page) 
(in English) We focus on a hierarchical classification approach for musical instruments classification using machine learning to reduce memory usage and computational cost.
In this study, we first use LightGBM, a type of gradient boosting tree, to divide sounds into percussive and non-percussive instruments.
For the subsequent hierarchy levels, we propose using lightweight CNN-based networks and dimensionality reduction of input features using element selection.
Element selection is a technique for obtaining low-dimensional representations by selecting elements from the feature vector without requiring multiplications, unlike principal component analysis.
The selected indices are optimized by minimizing reconstruction error using training data for each instrument group.
This method simplifies each classification task, reducing the parameters of networks. Evaluation experiment results show that the proposed method performed better than single-network classification.
Keyword (in Japanese) (See Japanese page) 
(in English) convolutional neural network / musical instruments classification / dimensionality reduction / element selection / hierarchical classification / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 389, EA2024-111, pp. 215-220, March 2025.
Paper # EA2024-111 
Date of Issue 2025-02-23 (EA, SIP, SP) 
ISSN Online edition: ISSN 2432-6380
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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 EA2024-111 SIP2024-146 SP2024-52

Conference Information
Committee EA SIP SP IPSJ-SLP  
Conference Date 2025-03-02 - 2025-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EA 
Conference Code 2025-03-EA-SIP-SP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Memory-efficient and low-computational hierarchical musical instruments classification using element selection 
Sub Title (in English)  
Keyword(1) convolutional neural network  
Keyword(2) musical instruments classification  
Keyword(3) dimensionality reduction  
Keyword(4) element selection  
Keyword(5) hierarchical classification  
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1st Author's Name Ryu Kato  
1st Author's Affiliation Toyko Metropolitan University (Tokyo Metropolitan Univ.)
2nd Author's Name Natsuki Ueno  
2nd Author's Affiliation Kumamoto University/Tokyo Metropolitan University (Kumamoto Univ./)
3rd Author's Name Nobutaka Ono  
3rd Author's Affiliation Toyko Metropolitan 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.)
6th Author's Name Yu Takahashi  
6th Author's Affiliation Yamaha Corporation (Yamaha Corp.)
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Speaker Author-1 
Date Time 2025-03-03 12:20:00 
Presentation Time 20 minutes 
Registration for EA 
Paper # EA2024-111, SIP2024-146, SP2024-52 
Volume (vol) vol.124 
Number (no) no.389(EA), no.390(SIP), no.391(SP) 
Page pp.215-220 
#Pages 6 
Date of Issue 2025-02-23 (EA, SIP, SP) 

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