Abstract
In this paper a new method based on evolution strategies (ES) is presented to optimize a classifier for personal music categories. The user assigns songs to multiple personal music categories: Examples from each category are selected in order to train a category-specific classifier using musical features as input. The classifier then ranks all songs according to their similarity to the category examples. Since an exhaustive search for parameters maximizing the classifier performance is not feasible an ES is applied. The experiments show a significant performance increase for various music categories due to the ES optimization.
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Vatolkin, I., Theimer, W. (2008). Optimization of Feature Processing Chain in Music Classification by Evolution Strategies. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_114
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DOI: https://doi.org/10.1007/978-3-540-87700-4_114
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