# Singing voice conversion with non-parallel data

arXiv: https://arxiv.org/abs/1903.04124 · HTML: https://mlsanity.com/paper/1903.04124/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://mlsanity.com/paper/2304.00830/) (2023)
- [DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion](https://mlsanity.com/paper/2105.13871/) (2021)
- [Hierarchical disentangled representation learning for singing voice conversion](https://mlsanity.com/paper/2101.06842/) (2021)
- [Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding](https://mlsanity.com/paper/2110.04754/) (2021)
- [YingMusic-SVC: Real-World Robust Zero-Shot Singing Voice Conversion with Flow-GRPO and Singing-Specific Inductive Biases](https://mlsanity.com/paper/2512.04793/) (2025)
- [A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023](https://mlsanity.com/paper/2310.05203/) (2023)
- [SingVisio: Visual Analytics of Diffusion Model for Singing Voice Conversion](https://mlsanity.com/paper/2402.12660/) (2024)
- [Who is Authentic Speaker](https://mlsanity.com/paper/2405.00248/) (2024)

## Abstract (arXiv)

Singing voice conversion is a task to convert a song sang by a source singer to the voice of a target singer. In this paper, we propose using a parallel data free, many-to-one voice conversion technique on singing voices. A phonetic posterior feature is first generated by decoding singing voices through a robust Automatic Speech Recognition Engine (ASR). Then, a trained Recurrent Neural Network (RNN) with a Deep Bidirectional Long Short Term Memory (DBLSTM) structure is used to model the mapping from person-independent content to the acoustic features of the target person. F0 and aperiodic are obtained through the original singing voice, and used with acoustic features to reconstruct the target singing voice through a vocoder. In the obtained singing voice, the targeted and sourced singers sound similar. To our knowledge, this is the first study that uses non parallel data to train a singing voice conversion system. Subjective evaluations demonstrate that the proposed method effectively converts singing voices.