# Hierarchical disentangled representation learning for singing voice conversion

arXiv: https://arxiv.org/abs/2101.06842 · HTML: https://mlsanity.com/paper/2101.06842/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Hierarchical Generative Modeling for Controllable Speech Synthesis](https://mlsanity.com/paper/1810.07217/) (2018)
- [Hierarchical Quantized Autoencoders](https://mlsanity.com/paper/2002.08111/) (2020)
- [Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis](https://mlsanity.com/paper/2002.03785/) (2020)
- [Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks](https://mlsanity.com/paper/1704.00849/) (2017)
- [StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion](https://mlsanity.com/paper/1907.12279/) (2019)
- [Unsupervised Cross-Domain Singing Voice Conversion](https://mlsanity.com/paper/2008.02830/) (2020)
- [Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders](https://mlsanity.com/paper/1912.02613/) (2019)
- [PitchNet: Unsupervised Singing Voice Conversion with Pitch Adversarial Network](https://mlsanity.com/paper/1912.01852/) (2019)
- [Unsupervised Singing Voice Conversion](https://mlsanity.com/paper/1904.06590/) (2019)
- [Singing voice conversion with non-parallel data](https://mlsanity.com/paper/1903.04124/) (2019)

## Built on by (most cited first, 9 of 9)

- [Adversarially learning disentangled speech representations for robust multi-factor voice conversion](https://mlsanity.com/paper/2102.00184/) (2021)
- [Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding](https://mlsanity.com/paper/2110.04754/) (2021)
- [Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation](https://mlsanity.com/paper/2208.04554/) (2022)
- [Robust One-Shot Singing Voice Conversion](https://mlsanity.com/paper/2210.11096/) (2022)
- [LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any Singing Voice Conversion with Singer Guidance](https://mlsanity.com/paper/2406.05325/) (2024)
- [Iteratively Improving Speech Recognition and Voice Conversion](https://mlsanity.com/paper/2305.15055/) (2023)
- [DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matching](https://mlsanity.com/paper/2508.05978/) (2025)
- [Cross-modal Face- and Voice-style Transfer](https://mlsanity.com/paper/2302.13838/) (2023)
- [Singing Voice Conversion with Accompaniment Using Self-Supervised Representation-Based Melody Features](https://mlsanity.com/paper/2502.04722/) (2025)

## Abstract (arXiv)

Conventional singing voice conversion (SVC) methods often suffer from operating in high-resolution audio owing to a high dimensionality of data. In this paper, we propose a hierarchical representation learning that enables the learning of disentangled representations with multiple resolutions independently. With the learned disentangled representations, the proposed method progressively performs SVC from low to high resolutions. Experimental results show that the proposed method outperforms baselines that operate with a single resolution in terms of mean opinion score (MOS), similarity score, and pitch accuracy.