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On the automatic identification of difficult examples for beat tracking: Towards building new evaluation datasets
2012
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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
doi:10.1109/icassp.2012.6287824
dblp:conf/icassp/HolzapfelDZOG12
fatcat:z2hdhczra5ek5kyzzgtewmivye