A generalized Bayesian model for tracking long metrical cycles in acoustic music signals
2016 (English)In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, 76-80 p., 7471640Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. 76-80 p., 7471640
, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, ISSN 1520-6149 ; 2016
Bayesian models, Hindustani music, Meter tracking, Particle filters, Rhythm analysis
Media and Communication Technology
IdentifiersURN: urn:nbn:se:kth:diva-193775DOI: 10.1109/ICASSP.2016.7471640ScopusID: 2-s2.0-84973358754ISBN: 978-147999988-0OAI: oai:DiVA.org:kth-193775DiVA: diva2:1033968
41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai International Convention Center Shanghai, China, 20 March 2016 through 25 March 2016.
QC 201610112016-10-102016-10-102016-10-11Bibliographically approved