Holzapfel, AndreDavies, Matthew E. P.Zapata González, José RicardoLobato Oliveira, JoãoGouyon, Fabien2019-07-252019-07-252012Holzapfel A, Davies MEP, Zapata JR, Oliveira J, Gouyon F. Selective sampling for beat tracking evaluation. IEEE Trans Audio Speech Lang Process. 2012;20(9):2539-48. DOI: 10.1109/TASL.2012.22052441558-7916http://hdl.handle.net/10230/42182In 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.application/pdfeng© 2012 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. http://dx.doi.org/10.1109/TASL.2012.2205244info:eu-repo/semantics/articlehttp://dx.doi.org/10.1109/TASL.2012.2205244Beat trackingSelective samplingEvaluationGround truth annotationinfo:eu-repo/semantics/openAccess