TY - GEN
T1 - jaCappella Corpus
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
AU - Nakamura, Tomohiko
AU - Takamichi, Shinnosuke
AU - Tanji, Naoko
AU - Fukayama, Satoru
AU - Saruwatari, Hiroshi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We construct a corpus of Japanese a cappella vocal ensembles (ja-Cappella 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.
AB - We construct a corpus of Japanese a cappella vocal ensembles (ja-Cappella 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.
KW - Corpus
KW - audio source separation
KW - singing voice
KW - singing voice synthesis
KW - vocal ensemble
UR - https://www.scopus.com/pages/publications/85174004564
UR - https://www.scopus.com/pages/publications/85174004564#tab=citedBy
U2 - 10.1109/ICASSP49357.2023.10095569
DO - 10.1109/ICASSP49357.2023.10095569
M3 - Conference contribution
AN - SCOPUS:85174004564
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 June 2023 through 10 June 2023
ER -