Creating an a cappella singing audio dataset for automatic Jingju singing evaluation research

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• dc.contributor.author Gong, Rong
• dc.contributor.author Caro Repetto, Rafael
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2019-05-09T09:31:59Z
• dc.date.available 2019-05-09T09:31:59Z
• dc.date.issued 2017
• dc.description Comunicació presentada al 4th International Workshop on Digital Libraries for Musicology celebrat el 28 d'octubre de 2017 a Shanghai, Xina.
• dc.description.abstract e data-driven computational research on automatic jingju (also known as Beijing or Peking opera) singing evaluation lacks a suitable and comprehensive a cappella singing audio dataset. In this work, we present an a cappella singing audio dataset which consists of 120 arias, accounting for 1265 melodic lines. is dataset is also an extension our existing CompMusic jingju corpus. Both professional and amateur singers were invited to the dataset recording sessions, and the most common jingju musical elements have been covered. is dataset is also accompanied by metadata per aria and melodic line annotated for automatic singing evaluation research purpose. All the gathered data is openly available online.
• dc.description.sponsorship This research was funded by the European Research Council under the European Union's Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583).
• dc.format.mimetype application/pdf
• dc.identifier.citation Gong R, Caro Repetto R, Serra X. Creating an a cappella singing audio dataset for automatic Jingju singing evaluation research. In: Proceedings of the 4th International Workshop on Digital Libraries for Musicology; 2017 Oct 28; Shanghai, China. New York: ACM; 2017. p. 37-40. DOI: 10.1145/3144749.3144757
• dc.identifier.doi http://dx.doi.org/10.1145/3144749.3144757
• dc.identifier.uri http://hdl.handle.net/10230/37199
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof Proceedings of the 4th International Workshop on Digital Libraries for Musicology; 2017 Oct 28; Shanghai, China. New York: ACM; 2017. p. 37-40.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/267583
• dc.rights © 2017 Association for Computing Machinery
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword A cappella singing
• dc.subject.keyword Automatic jingju singing evaluation
• dc.subject.keyword Audio recording dataset
• dc.title Creating an a cappella singing audio dataset for automatic Jingju singing evaluation research
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion