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On the automatic identification of difficult examples for beat tracking: towards building new evaluation datasets

Title On the automatic identification of difficult examples for beat tracking: towards building new evaluation datasets
Publication Type Conference Paper
Year of Publication 2012
Conference Name The 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Authors Holzapfel, A. , Davies M. E. P. , Zapata J. R. , Oliveira J. , & Gouyon F.
Pagination 89-92
Conference Start Date 25/03/2012
Publisher IEEE
Conference Location Kyoto, Japan
Abstract 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 dis- agreement 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.
preprint/postprint document http://hdl.handle.net/10230/42182