# PPG-based singing voice conversion with adversarial representation learning

arXiv: https://arxiv.org/abs/2010.14804 · HTML: https://mlsanity.com/paper/2010.14804/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [Zero-shot Voice Conversion with Diffusion Transformers](https://mlsanity.com/paper/2411.09943/) (2024)
- [DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion](https://mlsanity.com/paper/2105.13871/) (2021)
- [The Singing Voice Conversion Challenge 2023](https://mlsanity.com/paper/2306.14422/) (2023)
- [Learning the Beauty in Songs: Neural Singing Voice Beautifier](https://mlsanity.com/paper/2202.13277/) (2022)
- [Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding](https://mlsanity.com/paper/2110.04754/) (2021)
- [LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any Singing Voice Conversion with Singer Guidance](https://mlsanity.com/paper/2406.05325/) (2024)
- [SaMoye: Zero-shot Singing Voice Conversion Model Based on Feature Disentanglement and Enhancement](https://mlsanity.com/paper/2407.07728/) (2024)
- [Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements](https://mlsanity.com/paper/2504.19197/) (2025)
- [DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matching](https://mlsanity.com/paper/2508.05978/) (2025)
- [Multi-Modal Automatic Prosody Annotation with Contrastive Pretraining of SSWP](https://mlsanity.com/paper/2309.05423/) (2023)
- [Singing Voice Conversion with Accompaniment Using Self-Supervised Representation-Based Melody Features](https://mlsanity.com/paper/2502.04722/) (2025)
- [kNN-SVC: Robust Zero-Shot Singing Voice Conversion with Additive Synthesis and Concatenation Smoothness Optimization](https://mlsanity.com/paper/2504.05686/) (2025)

## Abstract (arXiv)

Singing voice conversion (SVC) aims to convert the voice of one singer to that of other singers while keeping the singing content and melody. On top of recent voice conversion works, we propose a novel model to steadily convert songs while keeping their naturalness and intonation. We build an end-to-end architecture, taking phonetic posteriorgrams (PPGs) as inputs and generating mel spectrograms. Specifically, we implement two separate encoders: one encodes PPGs as content, and the other compresses mel spectrograms to supply acoustic and musical information. To improve the performance on timbre and melody, an adversarial singer confusion module and a mel-regressive representation learning module are designed for the model. Objective and subjective experiments are conducted on our private Chinese singing corpus. Comparing with the baselines, our methods can significantly improve the conversion performance in terms of naturalness, melody, and voice similarity. Moreover, our PPG-based method is proved to be robust for noisy sources.