Abstract
In this paper, we evaluate several factors that influence the performance of n-gram-based music similarity algorithms. Those algorithms are derived from textual information retrieval and adapted to operate on music data. The influence of n-gram length, applied feature extraction method, term weighting approach and similarity measure to the final performance of the similarity measure has been analyzed. MIREX 2005 data and MIREX 2011 evaluation framework for symbolic music similarity task have been used to measure the impact of each of the factors. The paper concludes that the choice of a proper feature extraction method and n-gram length are more important than the applied similarity measure or term weighting technique.
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Wołkowicz, J., Kešelj, V. (2012). Analysis of Important Factors for Measuring Similarity of Symbolic Music Using n-gram-Based, Bag-of-Words Approach. In: Kosseim, L., Inkpen, D. (eds) Advances in Artificial Intelligence. Canadian AI 2012. Lecture Notes in Computer Science(), vol 7310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30353-1_20
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DOI: https://doi.org/10.1007/978-3-642-30353-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30352-4
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