# Singing voice synthesis based on convolutional neural networks

arXiv: https://arxiv.org/abs/1904.06868 · HTML: https://mlsanity.com/paper/1904.06868/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism](https://mlsanity.com/paper/2105.02446/) (2021)
- [A Melody-Unsupervision Model for Singing Voice Synthesis](https://mlsanity.com/paper/2110.06546/) (2021)
- [WeSinger: Data-augmented Singing Voice Synthesis with Auxiliary Losses](https://mlsanity.com/paper/2203.10750/) (2022)
- [Singing-Tacotron: Global duration control attention and dynamic filter for End-to-end singing voice synthesis](https://mlsanity.com/paper/2202.07907/) (2022)
- [SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy](https://mlsanity.com/paper/2203.17001/) (2022)
- [SmoothSinger: A Conditional Diffusion Model for Singing Voice Synthesis with Multi-Resolution Architecture](https://mlsanity.com/paper/2506.21478/) (2025)
- [SongSong: A Time Phonograph for Chinese SongCi Music from Thousand of Years Away](https://mlsanity.com/paper/2602.24071/) (2026)

## Abstract (arXiv)

The present paper describes a 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. In these systems, the relationship between musical score feature sequences and acoustic feature sequences extracted from singing voices is modeled by DNNs. Then, an acoustic feature sequence of an arbitrary musical score is output in units of frames by the trained DNNs, and a natural trajectory of a singing voice is obtained by using a parameter generation algorithm. As singing voices contain rich 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 in units of segments that consist of long-term frames, and a natural trajectory is obtained without the parameter generation algorithm. Experimental results in a subjective listening test show that the proposed architecture can synthesize natural sounding singing voices.