Welcome to the UPF Digital Repository

Particle filters for efficient meter tracking with dynamic bayesian networks

Show simple item record

dc.contributor.author Srinivasamurthy, Ajay
dc.contributor.author Holzapfel, Andre
dc.contributor.author Cemgil, Ali Taylan
dc.contributor.author Serra, Xavier
dc.date.accessioned 2018-06-28T08:20:06Z
dc.date.available 2018-06-28T08:20:06Z
dc.date.issued 2015
dc.identifier.citation Srinivasamurthy A, Holzapfel A, Cemgil AT, Serra X. Particle filters for efficient meter tracking with dynamic bayesian networks. In: Müller M, Wiering F, editors. ISMIR 2015. 16th International Society for Music Information Retrieval Conference; 2015 Oct 26-30; Málaga, Spain. Canada: ISMIR; 2015. p. 197-203
dc.identifier.uri http://hdl.handle.net/10230/34998
dc.description Comunicació presentada a la 16th International Society for Music Information Retrieval Conference (ISMIR 2015), celebrada els dies 26 a 30 d'octubre de 2015 a Màlaga, Espanya.
dc.description.abstract Recent approaches in meter tracking have successfully applied Bayesian models. While the proposed models can be adapted to different musical styles, the applicability of these flexible methods so far is limited because the application of exact inference is computationally demanding. More efficient approximate inference algorithms using particle filters (PF) can be developed to overcome this limitation. In this paper, we assume that the type of meter of a piece is known, and use this knowledge to simplify an existing Bayesian model with the goal of incorporating a more diverse observation model. We then propose Particle Filter based inference schemes for both the original model and the simplification. We compare the results obtained from exact and approximate inference in terms of meter tracking accuracy as well as in terms of computational demands. Evaluations are performed using corpora of Carnatic music from India and a collection of Ballroom dances. We document that the approximate methods perform similar to exact inference, at a lower computational cost. Furthermore, we show that the inference schemes remain accurate for long and full length recordings in Carnatic music.
dc.description.sponsorship This work is supported by the European Research Council (grant number 267583) and a Marie Curie Intra-European Fellowship (grant number 328379).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher International Society for Music Information Retrieval (ISMIR)
dc.relation.ispartof Müller M, Wiering F, editors. ISMIR 2015. 16th International Society for Music Information Retrieval Conference; 2015 Oct 26-30; Málaga, Spain. Canada: ISMIR; 2015.
dc.rights © Ajay Srinivasamurthy, Andre Holzapfel, Ali Taylan Cemgil, Xavier Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Ajay Srinivasamurthy, Andre Holzapfel, Ali Taylan Cemgil, Xavier Serra. “Particle Filters for Efficient Meter Tracking with Dynamic Bayesian Networks”, 16th International Society for Music Information Retrieval Conference, 2015.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Particle filters for efficient meter tracking with dynamic bayesian networks
dc.type info:eu-repo/semantics/conferenceObject
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/267583
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/328379
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