The Singing Voice Conversion Challenge 2023

Wen-Chin Huang, Lester Phillip Violeta, Songxiang Liu, Jiatong Shi, Tomoki Toda
6/26/2023
cs.SDcs.CLeess.AS

No official code found. Best implementation: lesterphillip/SVCC23_FastSVC (116 stars, last commit 2023-11-25).

Abstract

We present the latest iteration of the voice conversion challenge (VCC) series, a bi-annual scientific event aiming to compare and understand different voice conversion (VC) systems based on a common dataset. This year we shifted our focus to singing voice conversion (SVC), thus named the challenge the Singing Voice Conversion Challenge (SVCC). A new database was constructed for two tasks, namely in-domain and cross-domain SVC. The challenge was run for two months, and in total we received 26 submissions, including 2 baselines. Through a large-scale crowd-sourced listening test, we observed that for both tasks, although human-level naturalness was achieved by the top system, no team was able to obtain a similarity score as high as the target speakers. Also, as expected, cross-domain SVC is harder than in-domain SVC, especially in the similarity aspect. We also investigated whether existing objective measurements were able to predict perceptual performance, and found that only few of them could reach a significant correlation.

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Code Implementations(1)

lesterphillip/SVCC23_FastSVCCommunity100%

Singing Voice Conversion Challenge 2023 Starter Kit: FastSVC Reimplementation

11610Perl, ShellFeb 16, 20232 years ago

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Cite this paper

@article{huang2023singing,
  title  = {The Singing Voice Conversion Challenge 2023},
  author = {Wen-Chin Huang and Lester Phillip Violeta and Songxiang Liu and Jiatong Shi and Tomoki Toda},
  year   = {2023},
  eprint = {2306.14422},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.14422v2}
}

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