Abstract
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.
Original language | English (US) |
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Title of host publication | Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018 |
Editors | Emilia Gomez, Xiao Hu, Eric Humphrey, Emmanouil Benetos |
Publisher | International Society for Music Information Retrieval |
Pages | 468-474 |
Number of pages | 7 |
ISBN (Electronic) | 9782954035123 |
State | Published - Jan 1 2018 |
Event | 19th International Society for Music Information Retrieval Conference, ISMIR 2018 - Paris, France Duration: Sep 23 2018 → Sep 27 2018 |
Publication series
Name | Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018 |
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Conference
Conference | 19th International Society for Music Information Retrieval Conference, ISMIR 2018 |
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Country/Territory | France |
City | Paris |
Period | 9/23/18 → 9/27/18 |
Funding
This work was supported by NSF Award #1420971 and by a Northwestern University Center for Interdisciplinary Research in the Arts grant.
ASJC Scopus subject areas
- Music
- Information Systems