On the automatic identification of difficult examples for beat tracking: towards building new evaluation datasets
2012 (English)In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE conference proceedings, 2012, 89-92 p.Conference paper (Refereed)
In this paper, an approach is presented that identifies music samples which are difficult for current state-of-the-art beat trackers. In order to estimate this difficulty even for examples without ground truth, a method motivated by selective sampling is applied. This method assigns a degree of difficulty to a sample based on the mutual disagreement between the output of various beat tracking systems. On a large beat annotated dataset we show that this mutual agreement is correlated with the mean performance of the beat trackers evaluated against the ground truth, and hence can be used to identify difficult examples by predicting poor beat tracking performance. Towards the aim of advancing future beat tracking systems, we demonstrate how our method can be used to form new datasets containing a high proportion of challenging music examples.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2012. 89-92 p.
Beat tracking; evaluation; selective sampling
Research subject Media Technology
IdentifiersURN: urn:nbn:se:kth:diva-193753ISI: 000312381400023ScopusID: 2-s2.0-84865695113OAI: oai:DiVA.org:kth-193753DiVA: diva2:1040422
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
QC 201610312016-10-272016-10-102016-11-10Bibliographically approved