Vocalset: A singing voice dataset

Julia Wilkins, Prem Seetharaman, Alison Wahl, Bryan Pardo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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 languageEnglish (US)
Title of host publicationProceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018
EditorsEmilia Gomez, Xiao Hu, Eric Humphrey, Emmanouil Benetos
PublisherInternational Society for Music Information Retrieval
Pages468-474
Number of pages7
ISBN (Electronic)9782954035123
StatePublished - Jan 1 2018
Event19th International Society for Music Information Retrieval Conference, ISMIR 2018 - Paris, France
Duration: Sep 23 2018Sep 27 2018

Publication series

NameProceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018

Conference

Conference19th International Society for Music Information Retrieval Conference, ISMIR 2018
CountryFrance
CityParis
Period9/23/189/27/18

ASJC Scopus subject areas

  • Music
  • Information Systems

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