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- Holzapfel, André, 1976-, et al.
(author)
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On the automatic identification of difficult examples for beat tracking : towards building new evaluation datasets
- 2012
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In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). - : IEEE conference proceedings. ; , s. 89-92
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Conference paper (peer-reviewed)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 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.
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| 2. |
- Holzapfel, André, 1976-, et al.
(author)
-
Selective sampling for beat tracking evaluation
- 2012
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In: IEEE Transactions on Audio, Speech, and Language Processing. - : IEEE Press. - 1558-7916 .- 1558-7924. ; 20:9, s. 2539-2548
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Journal article (peer-reviewed)abstract
- In 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.
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