JaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus
Not on the map. Why?
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/).
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 29 Nov 2022.
Builds on
14 of 35 references.
References · 14
- 2018SDR - half-baked or well done?cited by 420
- 2016Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separationcited by 189
- 2018Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separationcited by 125
- 2019Deep Learning for Audio Signal Processingcited by 92
- 2021Hybrid Spectrogram and Waveform Source Separationcited by 90
- 2020Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separationcited by 67
- 2021Music Demixing Challenge 2021cited by 38
- 2020All for One and One for All: Improving Music Separation by Bridging Networkscited by 20
- 2021Sinsy: A Deep Neural Network-Based Singing Voice Synthesis Systemcited by 14
- 2022Speech Denoising in the Waveform Domain with Self-Attentioncited by 13
- 2020Deep Learning Based Source Separation Applied To Choir Ensemblescited by 9
- 2022Music Separation Enhancement with Generative Modelingcited by 8
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2025A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?cited by 10
- 2026Latent-Mark: An Audio Watermark Robust to Neural Codec Compressioncited by 1
- 2023Sampling-Frequency-Independent Universal Sound Separationcited by 0
- 2025Source Separation for A Cappella Musiccited by 0
- 2026Latent-Mark: An Audio Watermark Robust to Neural Codec Compression5 Mar 2026
- 2025Source Separation for A Cappella Music30 Sep 2025
- 2025A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?26 May 2025
- 2023Sampling-Frequency-Independent Universal Sound Separation22 Sep 2023