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Paper Abstract and Keywords
Presentation 2023-06-23 13:50
Data Augmentation by Synthesised Voice for Deep Learning-based A Cappella Separation
Kyoka Kazama (TMU), Yuma Kinoshita (Tokai Univ.), Natsuki Ueno, Nobutaka Ono (TMU) SP2023-4
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we examine efficacy of training data augmentation for a cappella singing voice separation using deep learning. Since there are few singing voice datasets without musical instruments such as a cappella and chorus, and it is difficult to record these in a real environment, we create an a cappella singing voice data using singing voice synthesis and train a deep neural network (DNN) using this data. First, we obtain a cappella-arranged musical scores from MuseScore, and create a singing voice data of 100 minutes in total using the singing voice synthesis tool UTAU. In order to investigate whether the created synthesized singing voice data is effective for a cappella singing voice separation, we conducted a comparative experiment using Japanese a cappella vocal ensemble corpus as training data and a synthesized singing voice data. Experimental results show that the data augmentation by singing voice synthesis compensates for the lack of training data and is useful for improving the performance of a cappella singing voice separation.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning / data augmentation / singing voice separation / singing voice synthesis / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 88, SP2023-4, pp. 14-19, June 2023.
Paper # SP2023-4 
Date of Issue 2023-06-16 (SP) 
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)
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Conference Information
Committee SP IPSJ-MUS IPSJ-SLP  
Conference Date 2023-06-23 - 2023-06-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2023-06-SP-MUS-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Data Augmentation by Synthesised Voice for Deep Learning-based A Cappella Separation 
Sub Title (in English)  
Keyword(1) deep learning  
Keyword(2) data augmentation  
Keyword(3) singing voice separation  
Keyword(4) singing voice synthesis  
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1st Author's Name Kyoka Kazama  
1st Author's Affiliation Tokyo Metropolitan University (TMU)
2nd Author's Name Yuma Kinoshita  
2nd Author's Affiliation Tokai University (Tokai Univ.)
3rd Author's Name Natsuki Ueno  
3rd Author's Affiliation Tokyo Metropolitan University (TMU)
4th Author's Name Nobutaka Ono  
4th Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2023-06-23 13:50:00 
Presentation Time 140 minutes 
Registration for SP 
Paper # SP2023-4 
Volume (vol) vol.123 
Number (no) no.88 
Page pp.14-19 
#Pages 6 
Date of Issue 2023-06-16 (SP) 

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