# VibE-SVC: Vibrato Extraction with High-frequency F0 Contour for Singing Voice Conversion

arXiv: https://arxiv.org/abs/2505.20794 · HTML: https://mlsanity.com/paper/2505.20794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 21 of 21)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://mlsanity.com/paper/2206.04658/) (2022)
- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism](https://mlsanity.com/paper/2105.02446/) (2021)
- [DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion](https://mlsanity.com/paper/2105.13871/) (2021)
- [VISinger: Variational Inference with Adversarial Learning for End-to-End Singing Voice Synthesis](https://mlsanity.com/paper/2110.08813/) (2021)
- [TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control](https://mlsanity.com/paper/2409.15977/) (2024)
- [Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders](https://mlsanity.com/paper/1912.02613/) (2019)
- [Unsupervised Singing Voice Conversion](https://mlsanity.com/paper/1904.06590/) (2019)
- [Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding](https://mlsanity.com/paper/2110.04754/) (2021)
- [VITS-Based Singing Voice Conversion Leveraging Whisper and multi-scale F0 Modeling](https://mlsanity.com/paper/2310.02802/) (2023)
- [Singing Voice Synthesis with Vibrato Modeling and Latent Energy Representation](https://mlsanity.com/paper/2211.00996/) (2022)
- [SPA-SVC: Self-supervised Pitch Augmentation for Singing Voice Conversion](https://mlsanity.com/paper/2406.05692/) (2024)
- [Period Singer: Integrating Periodic and Aperiodic Variational Autoencoders for Natural-Sounding End-to-End Singing Voice Synthesis](https://mlsanity.com/paper/2406.09894/) (2024)
- [Boosting the Predictive Accurary of Singer Identification Using Discrete Wavelet Transform For Feature Extraction](https://mlsanity.com/paper/2102.00550/) (2021)

## Built on by (most cited first, 1 of 1)

- [Vibrato Expression Control for Singing Voice Conversion with Improving Independent Control](https://mlsanity.com/paper/2606.17126/) (2026)

## Abstract (arXiv)

Controlling singing style is crucial for achieving an expressive and natural singing voice. Among the various style factors, vibrato plays a key role in conveying emotions and enhancing musical depth. However, modeling vibrato remains challenging due to its dynamic nature, making it difficult to control in singing voice conversion. To address this, we propose VibESVC, a controllable singing voice conversion model that explicitly extracts and manipulates vibrato using discrete wavelet transform. Unlike previous methods that model vibrato implicitly, our approach decomposes the F0 contour into frequency components, enabling precise transfer. This allows vibrato control for enhanced flexibility. Experimental results show that VibE-SVC effectively transforms singing styles while preserving speaker similarity. Both subjective and objective evaluations confirm high-quality conversion.