Welcome to the UPF Digital Repository

Metrical-accent aware vocal onset detection in polyphonic audio

Show simple item record

dc.contributor.author Dzhambazov, Georgi Bogomilov
dc.contributor.author Holzapfel, Andre
dc.contributor.author Srinivasamurthy, Ajay
dc.contributor.author Serra, Xavier
dc.date.accessioned 2018-08-22T09:29:16Z
dc.date.available 2018-08-22T09:29:16Z
dc.date.issued 2017
dc.identifier.citation Dzhambazov G, Holzapfel A, Srinivasamurthy A, Serra X. Metrical-accent aware vocal onset detection in polyphonic audio. In: Hu X, Cunningham SJ, Turnbull D, Duan Z. ISMIR 2017 Proceedings of the 18th International Society for Music Information Retrieval Conference; 2017 Oct 23-27; Suzhou, China. [Suzhou]: ISMIR; 2017. p 702-8.
dc.identifier.uri http://hdl.handle.net/10230/35345
dc.description Comunicació presentada a la ISMIR 2017: 18th International Society for Music Information Retrieval Conference, celebrada els dies 23 a 27 d'octubre de 2017 a Suzhou, Xina.
dc.description.abstract The goal of this study is the automatic detection of onsets of the singing voice in polyphonic audio recordings. Starting with a hypothesis that the knowledge of the current position in a metrical cycle (i.e. metrical accent) can improve the accuracy of vocal note onset detection, we propose a novel probabilistic model to jointly track beats and vocal note onsets. The proposed model extends a state of the art model for beat and meter tracking, in which a-priori probability of a note at a specific metrical accent interacts with the probability of observing a vocal note onset. We carry out an evaluation on a varied collection of multi-instrument datasets from two music traditions (English popular music and Turkish makam) with different types of metrical cycles and singing styles. Results confirm that the proposed model reasonably improves vocal note onset detection accuracy compared to a baseline model that does not take metrical position into account.
dc.description.sponsorship This work is partly supported by the European Research Council under the European Union’s Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583) and partly by the Spanish Ministry of Economy and Competitiveness, through the ”Mar´ıa de Maeztu” Programme for Centres/Units of Excellence in R&D” (MDM-2015-0502).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher International Society for Music Information Retrieval (ISMIR)
dc.relation.ispartof Hu X, Cunningham SJ, Turnbull D, Duan Z. ISMIR 2017 Proceedings of the 18th International Society for Music Information Retrieval Conference; 2017 Oct 23-27; Suzhou, China. [Suzhou]: ISMIR; 2017.
dc.rights © Georgi Dzhambazov, Andre Holzapfel, Ajay Srinivasamurthy, Xavier Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Georgi Dzhambazov, Andre Holzapfel, Ajay Srinivasamurthy, Xavier Serra. “Metrical-accent aware vocal onset detection in polyphonic audio”, 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Metrical-accent aware vocal onset detection in polyphonic audio
dc.type info:eu-repo/semantics/conferenceObject
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/267583
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

In collaboration with Compliant to Partaking