mlsanityData to 7 Oct 2026

Unsupervised Cross-Domain Singing Voice Conversion

arXiv 2008.02830 ↗First posted 6 Aug 2020eess.AS cs.LG cs.SDcited by 23

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Abstract (arXiv)

We present a wav-to-wav generative model for the task of singing voice conversion from any identity. Our method utilizes both an acoustic model, trained for the task of automatic speech recognition, together with melody extracted features to drive a waveform-based generator. The proposed generative architecture is invariant to the speaker's identity and can be trained to generate target singers from unlabeled training data, using either speech or singing sources. The model is optimized in an end-to-end fashion without any manual supervision, such as lyrics, musical notes or parallel samples. The proposed approach is fully-convolutional and can generate audio in real-time. Experiments show that our method significantly outperforms the baseline methods while generating convincingly better audio samples than alternative attempts.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Aug 2020.

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Citing papers per year: 2021: 10; 2022: 1; 2023: 7; 2024: 5.

23 papers.

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  1. 2021Generative Spoken Language Modeling from Raw Audiocited by 336
  2. 2021Speech Resynthesis from Discrete Disentangled Self-Supervised Representationscited by 243
  3. 2021Text-Free Prosody-Aware Generative Spoken Language Modelingcited by 119
  4. 2023AUDIT: Audio Editing by Following Instructions with Latent Diffusion Modelscited by 81
  5. 2021StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversioncited by 57
  6. 2021DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversioncited by 39
  7. 2021Textless Speech Emotion Conversion using Discrete and Decomposed Representationscited by 29
  8. 2023The Singing Voice Conversion Challenge 2023cited by 25
  9. 2021fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkitcited by 24
  10. 2023The Ethical Implications of Generative Audio Models: A Systematic Literature Reviewcited by 18
  11. 2023FSD: An Initial Chinese Dataset for Fake Song Detectioncited by 12
  12. 2024CoMoSVC: Consistency Model-based Singing Voice Conversioncited by 11
11 more
  1. 2021Hierarchical disentangled representation learning for singing voice conversioncited by 9
  2. 2021Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Codingcited by 8
  3. 2024LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any Singing Voice Conversion with Singer Guidancecited by 6
  4. 2022Robust One-Shot Singing Voice Conversioncited by 5
  5. 2024SaMoye: Zero-shot Singing Voice Conversion Model Based on Feature Disentanglement and Enhancementcited by 5
  6. 2021Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Controlcited by 2
  7. 2023A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023cited by 2
  8. 2024LCM-SVC: Latent Diffusion Model Based Singing Voice Conversion with Inference Acceleration via Latent Consistency Distillationcited by 2
  9. 2023A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversioncited by 1
  10. 2023Iteratively Improving Speech Recognition and Voice Conversioncited by 1
  11. 2024Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Modelscited by 0