Abstract
In this paper we propose three generalizations of well-known N-gram approaches for measuring similarity of single-line melodies. In a former paper we compared around 50 similarity measures for melodies with empirical data from music psychological experiments. Similarity measures based on edit distances and N-grams always showed the best results for different contexts. This paper aims at a generalization of N-gram measures that can combine N-gram and other similarity measures in a fairly general way.
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References
DOWNIE, J. S. (1999): Evaluating a Simple Approach to Musical Information retrieval: Conceiving Melodic N-grams as Text. PhD thesis, University of Western Ontario.
MÜLLENSIEFEN, D. & FRIELER, K.(2004): Cognitive Adequacy in the Measurement of Melodic Similarity: Algorithmic vs. Human Judgments. Computing in Musicology, Vol. 13.
UITDENBOGERD, A. L. (2002): Music Information Retrieval Technology. PhD thesis, RMIT University Melbourne Victoria, Australia.
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© 2006 Springer-Verlag Berlin · Heidelberg
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Frieler, K. (2006). Generalized N-gram Measures for Melodic Similarity. In: Batagelj, V., Bock, HH., Ferligoj, A., Žiberna, A. (eds) Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-34416-0_31
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DOI: https://doi.org/10.1007/3-540-34416-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34415-5
Online ISBN: 978-3-540-34416-2
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