# JaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus

arXiv: https://arxiv.org/abs/2211.16028 · HTML: https://mlsanity.com/paper/2211.16028/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation](https://mlsanity.com/paper/2007.13975/) (2020)
- [Music Demixing Challenge 2021](https://mlsanity.com/paper/2108.13559/) (2021)
- [All for One and One for All: Improving Music Separation by Bridging Networks](https://mlsanity.com/paper/2010.04228/) (2020)
- [Sinsy: A Deep Neural Network-Based Singing Voice Synthesis System](https://mlsanity.com/paper/2108.02776/) (2021)
- [Speech Denoising in the Waveform Domain with Self-Attention](https://mlsanity.com/paper/2202.07790/) (2022)
- [Deep Learning Based Source Separation Applied To Choir Ensembles](https://mlsanity.com/paper/2008.07645/) (2020)
- [Music Separation Enhancement with Generative Modeling](https://mlsanity.com/paper/2208.12387/) (2022)
- [Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks](https://mlsanity.com/paper/2009.04172/) (2020)
- [Prior Distribution Design for Music Bleeding-Sound Reduction Based on Nonnegative Matrix Factorization](https://mlsanity.com/paper/2109.00237/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?](https://mlsanity.com/paper/2505.19663/) (2025)
- [Latent-Mark: An Audio Watermark Robust to Neural Codec Compression](https://mlsanity.com/paper/2603.05310/) (2026)
- [Sampling-Frequency-Independent Universal Sound Separation](https://mlsanity.com/paper/2309.12581/) (2023)
- [Source Separation for A Cappella Music](https://mlsanity.com/paper/2509.26580/) (2025)
- [Revisiting Input Time-frequency Representations in Multi-pitch Estimation for Vocal Ensembles](https://mlsanity.com/paper/2610.03656/) (2026)

## Abstract (arXiv)

We construct a corpus of Japanese a cappella vocal ensembles (jaCappella corpus) for vocal ensemble separation and synthesis. It consists of 35 copyright-cleared vocal ensemble songs and their audio recordings of individual voice parts. These songs were arranged from out-of-copyright Japanese children's songs and have six voice parts (lead vocal, soprano, alto, tenor, bass, and vocal percussion). They are divided into seven subsets, each of which features typical characteristics of a music genre such as jazz and enka. The variety in genre and voice part match vocal ensembles recently widespread in social media services such as YouTube, although the main targets of conventional vocal ensemble datasets are choral singing made up of soprano, alto, tenor, and bass. Experimental evaluation demonstrates that our corpus is a challenging resource for vocal ensemble separation. Our corpus is available on our project page (https://tomohikonakamura.github.io/jaCappella_corpus/).