# SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation

arXiv: https://arxiv.org/abs/2110.07468 · HTML: https://mlsanity.com/paper/2110.07468/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2021.

## Built on by (most cited first, 27 of 27)

- [ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech](https://mlsanity.com/paper/2207.06389/) (2022)
- [TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation](https://mlsanity.com/paper/2205.12523/) (2022)
- [Make-A-Voice: Unified Voice Synthesis With Discrete Representation](https://mlsanity.com/paper/2305.19269/) (2023)
- [Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion](https://mlsanity.com/paper/2308.02552/) (2023)
- [ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer](https://mlsanity.com/paper/2305.12708/) (2023)
- [GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech](https://mlsanity.com/paper/2205.07211/) (2022)
- [StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis](https://mlsanity.com/paper/2312.10741/) (2023)
- [Delving into the Adversarial Robustness of Federated Learning](https://mlsanity.com/paper/2302.09479/) (2023)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Text-to-Song: Towards Controllable Music Generation Incorporating Vocals and Accompaniment](https://mlsanity.com/paper/2404.09313/) (2024)
- [RMSSinger: Realistic-Music-Score based Singing Voice Synthesis](https://mlsanity.com/paper/2305.10686/) (2023)
- [Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt](https://mlsanity.com/paper/2403.11780/) (2024)
- [SongCreator: Lyrics-based Universal Song Generation](https://mlsanity.com/paper/2409.06029/) (2024)
- [VarietySound: Timbre-Controllable Video to Sound Generation via Unsupervised Information Disentanglement](https://mlsanity.com/paper/2211.10666/) (2022)
- [Contrastive Learning with Positive-Negative Frame Mask for Music Representation](https://mlsanity.com/paper/2203.09129/) (2022)
- [HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models](https://mlsanity.com/paper/2306.06814/) (2023)
- [USTC-KXDIGIT System Description for ASVspoof5 Challenge](https://mlsanity.com/paper/2409.01695/) (2024)
- [AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation](https://mlsanity.com/paper/2305.15403/) (2023)
- [An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music Recommendation](https://mlsanity.com/paper/2402.08300/) (2024)
- [LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training](https://mlsanity.com/paper/2606.30642/) (2026)
- [Wav2SQL: Direct Generalizable Speech-To-SQL Parsing](https://mlsanity.com/paper/2305.12552/) (2023)
- [CoMelSinger: Discrete Token-Based Zero-Shot Singing Synthesis With Structured Melody Control and Guidance](https://mlsanity.com/paper/2509.19883/) (2025)
- [AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment](https://mlsanity.com/paper/2305.04476/) (2023)
- [Prevailing Research Areas for Music AI in the Era of Foundation Models](https://mlsanity.com/paper/2409.09378/) (2024)
- [Enhancing the vocal range of single-speaker singing voice synthesis with melody-unsupervised pre-training](https://mlsanity.com/paper/2309.00284/) (2023)
- [Training Universal Vocoders with Feature Smoothing-Based Augmentation Methods for High-Quality TTS Systems](https://mlsanity.com/paper/2409.02517/) (2024)
- [Controllable Singing Style Conversion with Boundary-Aware Information Bottleneck](https://mlsanity.com/paper/2604.05526/) (2026)

## Abstract (arXiv)

Deep generative models have achieved significant progress in speech synthesis to date, while high-fidelity singing voice synthesis is still an open problem for its long continuous pronunciation, rich high-frequency parts, and strong expressiveness. Existing neural vocoders designed for text-to-speech cannot directly be applied to singing voice synthesis because they result in glitches and poor high-frequency reconstruction. In this work, we propose SingGAN, a generative adversarial network designed for high-fidelity singing voice synthesis. Specifically, 1) to alleviate the glitch problem in the generated samples, we propose source excitation with the adaptive feature learning filters to expand the receptive field patterns and stabilize long continuous signal generation; and 2) SingGAN introduces global and local discriminators at different scales to enrich low-frequency details and promote high-frequency reconstruction; and 3) To improve the training efficiency, SingGAN includes auxiliary spectrogram losses and sub-band feature matching penalty loss. To the best of our knowledge, SingGAN is the first work designed toward high-fidelity singing voice vocoding. Our evaluation of SingGAN demonstrates the state-of-the-art results with higher-quality (MOS 4.05) samples. Also, SingGAN enables a sample speed of 50x faster than real-time on a single NVIDIA 2080Ti GPU. We further show that SingGAN generalizes well to the mel-spectrogram inversion of unseen singers, and the end-to-end singing voice synthesis system SingGAN-SVS enjoys a two-stage pipeline to transform the music scores into expressive singing voices. Audio samples are available at urlhttps://SingGAN.github.io/