Fast And High-quality Singing Voice Synthesis System Based On Convolutional Neural Networks
2019 Β· Kazuhiro Nakamura, Shinji Takaki, Kei Hashimoto, et al.
Abstract
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.
Authors
(none)
Tags
Stats
Related papers
- Singing Voice Synthesis Based On Convolutional Neural Networks (2019)0.00
- Unsupervised Singing Voice Conversion (2019)11.19
- Singgan: Generative Adversarial Network For High-fidelity Singing Voice Generation (2021)10.61
- NNSVS: A Neural Network-based Singing Voice Synthesis Toolkit (2022)13.83
- Singing Voice Synthesis Using Deep Autoregressive Neural Networks For Acoustic Modeling (2019)9.92
- Hiddensinger: High-quality Singing Voice Synthesis Via Neural Audio Codec And Latent Diffusion Models (2023)0.00
- Wgansing: A Multi-voice Singing Voice Synthesizer Based On The Wasserstein-gan (2019)11.08
- An Empirical Study On End-to-end Singing Voice Synthesis With Encoder-decoder Architectures (2021)0.00