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The Singing Voice Conversion Challenge 2023

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arxiv 2306.14422 v2 pith:ZYIFI5DC submitted 2023-06-26 cs.SD cs.CLeess.AS

classification cs.SDcs.CLeess.AS
keywords challengeconversionvoicesingingablecross-domainin-domainsimilarity
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. Neurodyne: Neural Pitch Manipulation with Representation Learning and Cycle-Consistency GAN

    cs.SD 2025-05 conditional novelty 6.0 of 10

    Neurodyne, a GAN-based singing voice pitch manipulator, uses adversarial representation learning and inversion plus composition cycle-consistency to improve pitch accuracy while preserving singer identity.

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