mlsanityData to 14 Oct 2026

Hierarchical disentangled representation learning for singing voice conversion

arXiv 2101.06842 ↗First posted 18 Jan 2021cs.SD cs.LG eess.AScited by 9

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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.

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

14 of 23 references.

References · 14

  1. 2017Neural Discrete Representation Learningcited by 4,712
  2. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  3. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  4. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,387
  5. 2018Hierarchical Generative Modeling for Controllable Speech Synthesiscited by 93
  6. 2020Hierarchical Quantized Autoencoderscited by 57
  7. 2020Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesiscited by 41
  8. 2017Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networkscited by 38
  9. 2019StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversioncited by 37
  10. 2020Unsupervised Cross-Domain Singing Voice Conversioncited by 23
  11. 2019Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoderscited by 18
  12. 2019PitchNet: Unsupervised Singing Voice Conversion with Pitch Adversarial Networkcited by 15
2 more
  1. 2019Unsupervised Singing Voice Conversioncited by 14
  2. 2019Singing voice conversion with non-parallel datacited by 8

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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 1; 2025: 2.

9 papers.

  1. 2021Adversarially learning disentangled speech representations for robust multi-factor voice conversioncited by 14
  2. 2021Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Codingcited by 8
  3. 2022Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generationcited by 6
  4. 2022Robust One-Shot Singing Voice Conversioncited by 6
  5. 2024LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any Singing Voice Conversion with Singer Guidancecited by 6
  6. 2023Iteratively Improving Speech Recognition and Voice Conversioncited by 2
  7. 2025DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matchingcited by 2
  8. 2023Cross-modal Face- and Voice-style Transfercited by 1
  9. 2025Singing Voice Conversion with Accompaniment Using Self-Supervised Representation-Based Melody Featurescited by 1