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Paper Abstract and Keywords
Presentation 2019-12-06 16:00
A comparison of neural vocoders in singing voice synthesis
Sota Wada, Yukiya Hono, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda (Nagoya Inst. of Tech.) SP2019-42
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we compare five types of vocoders based on neural networks (neural vocoders) for singing voice synthesis. In recent years, WaveNet vocoder has been proposed as a neural vocoder. WaveNet vocoder can model speech waveforms with high accuracy and generate natural sounding speech. However there is a problem that WaveNet vocoder cannot synthesize speech in real time due to its autoregressive structure. To address this problem, two approaches have been proposed. The first approach is to reduce the model structure of the autoregressive models. This increases the efficiency of sampling from the models and allows faster synthesis than real time. The second approach is to synthesize multiple samples simultaneously by using flow-based generative models.The performance of these methods has been investigated using normal utterances, and no singing voice has been used yet. Therefore, in this paper, we compare the performance of five types of neural vocoders for singing voice synthesis. The results of subjective and objective evaluation experiments show that WaveRNN is an appropriate neural vocoder when emphasizing naturalness, and WaveNet is appropriate if emphasizing reproducibility of pitch and vibrato.
Keyword (in Japanese) (See Japanese page) 
(in English) DNN / Singing voice synthesis / Neural vocoder / WaveNet / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 321, SP2019-42, pp. 85-90, Dec. 2019.
Paper # SP2019-42 
Date of Issue 2019-11-29 (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)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF SP2019-42

Conference Information
Committee NLC IPSJ-NL SP IPSJ-SLP  
Conference Date 2019-12-04 - 2019-12-06 
Place (in Japanese) (See Japanese page) 
Place (in English) NHK Science & Technology Research Labs. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 6th Natural Language Processing Symposium & The 21th Spoken Language Symposium 
Paper Information
Registration To SP 
Conference Code 2019-12-NLC-NL-SP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A comparison of neural vocoders in singing voice synthesis 
Sub Title (in English)  
Keyword(1) DNN  
Keyword(2) Singing voice synthesis  
Keyword(3) Neural vocoder  
Keyword(4) WaveNet  
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1st Author's Name Sota Wada  
1st Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
2nd Author's Name Yukiya Hono  
2nd Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
3rd Author's Name Shinji Takaki  
3rd Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
4th Author's Name Kei Hashimoto  
4th Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
5th Author's Name Keiichiro Oura  
5th Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
6th Author's Name Yoshihiko Nankaku  
6th Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
7th Author's Name Keiichi Tokuda  
7th Author's Affiliation Nagoya Institute of Technology (Nagoya Inst. of Tech.)
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Speaker Author-1 
Date Time 2019-12-06 16:00:00 
Presentation Time 25 minutes 
Registration for SP 
Paper # SP2019-42 
Volume (vol) vol.119 
Number (no) no.321 
Page pp.85-90 
#Pages
Date of Issue 2019-11-29 (SP) 


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