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Selective sampling for beat tracking evaluation
Universitat Pompeu Fabra, Spain .ORCID iD: 0000-0003-1679-6018
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2012 (English)In: IEEE Transactions on Audio, Speech and Language Processing, Vol. 20, no 9, 2539-2548 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose a method that can identify challenging music samples for beat tracking without ground truth. Our method, motivated by the machine learning method "selective sampling," is based on the measurement of mutual agreement between beat sequences. In calculating this mutual agreement we show the critical influence of different evaluation measures. Using our approach we demonstrate how to compile a new evaluation dataset comprised of difficult excerpts for beat tracking and examine this difficulty in the context of perceptual and musical properties. Based on tag analysis we indicate the musical properties where future advances in beat tracking research would be most profitable and where beat tracking is too difficult to be attempted. Finally, we demonstrate how our mutual agreement method can be used to improve beat tracking accuracy on large music collections.

Place, publisher, year, edition, pages
IEEE Press, 2012. Vol. 20, no 9, 2539-2548 p.
Keyword [en]
Beat tracking; evaluation; ground truth annotation; selective sampling
National Category
Media Engineering
Research subject
Media Technology
Identifiers
URN: urn:nbn:se:kth:diva-193754DOI: 10.1109/TASL.2012.2205244ISI: 000308108900004ScopusID: 2-s2.0-84865681012OAI: oai:DiVA.org:kth-193754DiVA: diva2:1040421
Note

QC 20161031

Available from: 2016-10-27 Created: 2016-10-10 Last updated: 2016-11-11Bibliographically approved

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Holzapfel, André
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