Abstract
The paper deals with evaluation of various n-gram-based composer classification algorithms. Our analysis has a broad scope: We have analyzed three labelled corpora, five similarity measures, several feature extraction methods, the influence of forced balanced training and an extensive range of n-gram lengths. We found that most of the approaches we analyzed, when properly parametrized, can give very good results, on par with other state-of-the art data mining techniques and greatly outperforming humans in composer recognition.
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Wołkowicz, J., Kešelj, V. (2013). Evaluation of n-Gram-Based Classification Approaches on Classical Music Corpora. In: Yust, J., Wild, J., Burgoyne, J.A. (eds) Mathematics and Computation in Music. MCM 2013. Lecture Notes in Computer Science(), vol 7937. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39357-0_17
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DOI: https://doi.org/10.1007/978-3-642-39357-0_17
Publisher Name: Springer, Berlin, Heidelberg
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