# JVS-MuSiC: Japanese multispeaker singing-voice corpus

arXiv: https://arxiv.org/abs/2001.07044 · HTML: https://mlsanity.com/paper/2001.07044/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks](https://mlsanity.com/paper/2409.13832/) (2024)
- [SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition](https://mlsanity.com/paper/2402.17645/) (2024)
- [RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction](https://mlsanity.com/paper/2403.05010/) (2024)
- [RapVerse: Coherent Vocals and Whole-Body Motions Generations from Text](https://mlsanity.com/paper/2405.20336/) (2024)
- [Speech Foundation Model Ensembles for the Controlled Singing Voice Deepfake Detection (CtrSVDD) Challenge 2024](https://mlsanity.com/paper/2409.02302/) (2024)
- [SingMOS-Pro: An Comprehensive Benchmark for Singing Quality Assessment](https://mlsanity.com/paper/2510.01812/) (2025)
- [Zero-Shot Duet Singing Voices Separation with Diffusion Models](https://mlsanity.com/paper/2311.07345/) (2023)
- [SongBsAb: A Dual Prevention Approach against Singing Voice Conversion based Illegal Song Covers](https://mlsanity.com/paper/2401.17133/) (2024)
- [SaMoye: Zero-shot Singing Voice Conversion Model Based on Feature Disentanglement and Enhancement](https://mlsanity.com/paper/2407.07728/) (2024)
- [MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation](https://mlsanity.com/paper/2211.07302/) (2022)
- [A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023](https://mlsanity.com/paper/2310.05203/) (2023)
- [SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan](https://mlsanity.com/paper/2405.05244/) (2024)
- [Multi-Accent Mandarin Dry-Vocal Singing Dataset: Benchmark for Singing Accent Recognition](https://mlsanity.com/paper/2512.07005/) (2025)
- [LAPS-Diff: A Diffusion-Based Framework for Singing Voice Synthesis With Language Aware Prosody-Style Guided Learning](https://mlsanity.com/paper/2507.04966/) (2025)

## Abstract (arXiv)

Thanks to developments in machine learning techniques, it has become possible to synthesize high-quality singing voices of a single singer. An open multispeaker singing-voice corpus would further accelerate the research in singing-voice synthesis. However, conventional singing-voice corpora only consist of the singing voices of a single singer. We designed a Japanese multispeaker singing-voice corpus called "JVS-MuSiC" with the aim to analyze and synthesize a variety of voices. The corpus consists of 100 singers' recordings of the same song, Katatsumuri, which is a Japanese children's song. It also includes another song that is different for each singer. In this paper, we describe the design of the corpus and experimental analyses using JVS-MuSiC. We investigated the relationship between 1) the similarity of singing voices and perceptual oneness of unison singing voices and between 2) the similarity of singing voices and that of speech. The results suggest that 1) there is a positive and moderate correlation between singing-voice similarity and the oneness of unison and that 2) the correlation between singing-voice similarity and speech similarity is weak. This corpus is freely available online.