A generalized Bayesian model for tracking long metrical cycles in acoustic music signals

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Srinivasamurthy, Ajayca
• dc.contributor.author Holzapfel, Andreca
• dc.contributor.author Cemgil, Ali Taylanca
• dc.contributor.author Serra, Xavierca
• dc.date.accessioned 2017-05-04T10:37:12Z
• dc.date.available 2017-05-04T10:37:12Z
• dc.date.issued 2016
• dc.description.abstract Most musical phenomena involve repetitive structures that enable listeners to track meter, i.e. the tactus or beat, the longer over-arching measure or bar, and possibly other related layers. Meters with long measure duration, sometimes lasting more than a minute, occur in many music cultures, e.g. from India, Turkey, and Korea. However, current meter tracking algorithms, which were devised for cycles of a few seconds length, cannot process such structures accurately. We present a novel generalization to an existing Bayesian model for meter tracking that overcomes this limitation. The proposed model is evaluated on a set of Indian Hindustani music recordings, and we document significant performance increase over the previous models. The presented model opens the way for computational analysis of performances with long metrical cycles, and has important applications in music studies as well as in commercial applications that involve such musics.
• dc.description.sponsorship This work is partly supported by the European Research Council as part of the CompMusic project (ERC grant agreement 267583), and Vienna Science and Technology Fund (WWTF, project MA14-018).
• dc.format.mimetype application/pdfca
• dc.identifier.citation Srinivasasmurthy A, Holzapfel A, Cemgil AT, Serra X. A generalized Bayesian model for tracking long metrical cycles in acoustic music signals. In: Proceedings of the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016); 2016 March 20-25; Shanghai, China. [S.l]: IEEE; 2016. p. 76-80. DOI: 10.1109/ICASSP.2016.7471640
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP.2016.7471640
• dc.identifier.uri http://hdl.handle.net/10230/32090
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)ca
• dc.relation.ispartof Proceedings of the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016); 2016 March 20-25; Shanghai, China. [S.l]: IEEE; 2016. p. 76-80.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/267583
• dc.rights © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at http://ieeexplore.ieee.org/document/7471640/?arnumber=7471640
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Hindustani music
• dc.subject.keyword Rhythm analysis
• dc.subject.keyword Bayesian models
• dc.subject.keyword Meter tracking
• dc.subject.keyword Particle filters
• dc.title A generalized Bayesian model for tracking long metrical cycles in acoustic music signalsca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion