# WGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN

arXiv: https://arxiv.org/abs/1903.10729 · HTML: https://mlsanity.com/paper/1903.10729/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

## Built on by (most cited first, 13 of 13)

- [Catch-A-Waveform: Learning to Generate Audio from a Single Short Example](https://mlsanity.com/paper/2106.06426/) (2021)
- [DiffMoog: a Differentiable Modular Synthesizer for Sound Matching](https://mlsanity.com/paper/2401.12570/) (2024)
- [Latent Space Explorations of Singing Voice Synthesis using DDSP](https://mlsanity.com/paper/2103.07197/) (2021)
- [LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters](https://mlsanity.com/paper/2105.10371/) (2021)
- [SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy](https://mlsanity.com/paper/2203.17001/) (2022)
- [Differentiable WORLD Synthesizer-based Neural Vocoder With Application To End-To-End Audio Style Transfer](https://mlsanity.com/paper/2208.07282/) (2022)
- [CoMelSinger: Discrete Token-Based Zero-Shot Singing Synthesis With Structured Melody Control and Guidance](https://mlsanity.com/paper/2509.19883/) (2025)
- [Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control](https://mlsanity.com/paper/2111.09146/) (2021)
- [Adversarial synthesis based data-augmentation for code-switched spoken language identification](https://mlsanity.com/paper/2205.15747/) (2022)
- [A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion](https://mlsanity.com/paper/2302.13678/) (2023)
- [SmoothSinger: A Conditional Diffusion Model for Singing Voice Synthesis with Multi-Resolution Architecture](https://mlsanity.com/paper/2506.21478/) (2025)
- [Anyone GAN Sing](https://mlsanity.com/paper/2102.11058/) (2021)
- [SongSong: A Time Phonograph for Chinese SongCi Music from Thousand of Years Away](https://mlsanity.com/paper/2602.24071/) (2026)

## Abstract (arXiv)

We present a deep neural network based singing voice synthesizer, inspired by the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm. We use vocoder parameters for acoustic modelling, to separate the influence of pitch and timbre. This facilitates the modelling of the large variability of pitch in the singing voice. Our network takes a block of consecutive frame-wise linguistic and fundamental frequency features, along with global singer identity as input and outputs vocoder features, corresponding to the block of features. This block-wise approach, along with the training methodology allows us to model temporal dependencies within the features of the input block. For inference, sequential blocks are concatenated using an overlap-add procedure. We show that the performance of our model is competitive with regards to the state-of-the-art and the original sample using objective metrics and a subjective listening test. We also present examples of the synthesis on a supplementary website and the source code via GitHub.