REVIEW 3 cited by
Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis
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
read the original abstract
This paper introduces Opencpop, a publicly available high-quality Mandarin singing corpus designed for singing voice synthesis (SVS). The corpus consists of 100 popular Mandarin songs performed by a female professional singer. Audio files are recorded with studio quality at a sampling rate of 44,100 Hz and the corresponding lyrics and musical scores are provided. All singing recordings have been phonetically annotated with phoneme boundaries and syllable (note) boundaries. To demonstrate the reliability of the released data and to provide a baseline for future research, we built baseline deep neural network-based SVS models and evaluated them with both objective metrics and subjective mean opinion score (MOS) measure. Experimental results show that the best SVS model trained on our database achieves 3.70 MOS, indicating the reliability of the provided corpus. Opencpop is released to the open-source community WeNet, and the corpus, as well as synthesized demos, can be found on the project homepage.
Forward citations
Cited by 3 Pith papers
-
MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation
Adding SVS-style phoneme conditioning and a length regulator to melody-guided cover generation cuts phoneme error rate from 45.6% to 18.7% while holding melody metrics.
-
STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation
STARS unifies lyric alignment, note transcription, vocal technique detection, and global style prediction into one multi-level neural model that matches or beats several single-task baselines.
-
SmoothSinger: A Conditional Diffusion Model for Singing Voice Synthesis with Multi-Resolution Architecture
A reference-guided diffusion model with a low-frequency upsampling module achieves marginal quality improvements over prior SVS baselines on Opencpop, with significant caveats about statistical significance and reprod...
Discussion (0). Sign in to comment.