| 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., Oliveira J., & Gouyon F. |
| Pagination | 89-92 |
| Conference Start Date | 25/03/2012 |
| Publisher | IEEE |
| Conference Location | Kyoto, Japan |
| Keywords | beat tracking, database |
| 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.
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