TY - GEN
T1 - Vocalset
T2 - 19th International Society for Music Information Retrieval Conference, ISMIR 2018
AU - Wilkins, Julia
AU - Seetharaman, Prem
AU - Wahl, Alison
AU - Pardo, Bryan
N1 - Funding Information:
This work was supported by NSF Award #1420971 and by a Northwestern University Center for Interdisciplinary Research in the Arts grant.
Publisher Copyright:
© Julia Wilkins, Prem Seetharaman, Alison Wahl, Bryan Pardo.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - We present VocalSet, a singing voice dataset of a capella singing. Existing singing voice datasets either do not capture a large range of vocal techniques, have very few singers, or are single-pitch and devoid of musical context. VocalSet captures not only a range of vowels, but also a diverse set of voices on many different vocal techniques, sung in contexts of scales, arpeggios, long tones, and excerpts. VocalSet has recordings of 10.1 hours of 20 professional singers (11 male, 9 female) performing 17 different different vocal techniques. This data will facilitate the development of new machine learning models for singer identification, vocal technique identification, singing generation and other related applications. To illustrate this, we establish baseline results on vocal technique classification and singer identification by training convolutional network classifiers on VocalSet to perform these tasks.
AB - We present VocalSet, a singing voice dataset of a capella singing. Existing singing voice datasets either do not capture a large range of vocal techniques, have very few singers, or are single-pitch and devoid of musical context. VocalSet captures not only a range of vowels, but also a diverse set of voices on many different vocal techniques, sung in contexts of scales, arpeggios, long tones, and excerpts. VocalSet has recordings of 10.1 hours of 20 professional singers (11 male, 9 female) performing 17 different different vocal techniques. This data will facilitate the development of new machine learning models for singer identification, vocal technique identification, singing generation and other related applications. To illustrate this, we establish baseline results on vocal technique classification and singer identification by training convolutional network classifiers on VocalSet to perform these tasks.
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M3 - Conference contribution
AN - SCOPUS:85059773103
T3 - Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018
SP - 468
EP - 474
BT - Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018
A2 - Gomez, Emilia
A2 - Hu, Xiao
A2 - Humphrey, Eric
A2 - Benetos, Emmanouil
PB - International Society for Music Information Retrieval
Y2 - 23 September 2018 through 27 September 2018
ER -