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Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks

Citation Author(s):
Kazuhiro Nakamura, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda
Submitted by:
Kazuhiro Nakamura
Last updated:
20 May 2020 - 8:26pm
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters:
Kazuhiro Nakamura
Paper Code:
SPE-P12.8
 

The present paper describes singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. As singing voices represent a rich form of expression, a powerful technique to model them accurately is required. In the proposed technique, long-term dependencies of singing voices are modeled by CNNs. An acoustic feature sequence is generated for each segment that consists of long-term frames, and a natural trajectory is obtained without the parameter generation algorithm. Furthermore, a computational complexity reduction technique, which drives the DNNs in different time units depending on type of musical score features, is proposed. Experimental results show that the proposed method can synthesize natural sounding singing voices much faster than the conventional method.

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