Abstract
This paper studies the performance of alignment methods for folk music classification. An edit distance approach is applied to three datasets with different associated classification tasks (tune family, geographic region, and dance type), and compared with a baseline n-gram classifier. Experimental results show that the edit distance performs well for the specific task of tune family classification, yielding similar results to an n-gram model with a pitch interval representation. However, for more general classification tasks, where tunes within the same class are heterogeneous, the n-gram model is recommended.
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Notes
- 1.
We would like to thank Peter van Kranenburg for sharing the Annotated Corpus and for the kind correspondence.
References
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Hillewaere, R., Manderick, B., Conklin, D. (2014). Alignment Methods for Folk Tune Classification. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_40
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DOI: https://doi.org/10.1007/978-3-319-01595-8_40
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