REVIEW 3 cited by
The Singing Voice Conversion Challenge 2023
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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.
Forward citations
Cited by 3 Pith papers
-
Neurodyne: Neural Pitch Manipulation with Representation Learning and Cycle-Consistency GAN
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.
-
SingNet: Towards a Large-Scale, Diverse, and In-the-Wild Singing Voice Dataset
SingNet is a claimed ~3,000-hour in-the-wild singing voice dataset from internet songs and sample packs, with benchmarks for lyric transcription, vocoders, and singing voice conversion.
-
SYKI-SVC: Advancing Singing Voice Conversion with Post-Processing Innovations and an Open-Source Professional Testset
SYKI-SVC improves a VITS-based singing voice converter by fusing ContentVec and Whisper features, adding mel reconstruction supervision, and splicing source high frequencies into the output, and it releases a professi...
Discussion (0). Continue with ORCID to comment.