PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A probabilistic approach to melodic similarity
Jose F. Bernabeu, Jorge Calera-Rubio, José Iñesta and David Rizo
In: 2nd International Workshop on Machine Learning and Music (MML 2009), 7 Sep 2009, Bled, Slovenia.

Abstract

Melodic similarity is an important research topic in music information retrieval. The representation of symbolic music by means of trees has proven to be suitable in melodic similarity computation, because they are able to code rhythm in their structure leaving only pitch representations as a degree of freedom for coding. In order to compare trees, different edit distances have been previously used. In this paper, stochastic k-testable tree-models, formerly used in other domains like structured document compression or natural language processing, have been used for computing a similarity measure between melody trees as a probability and their performance has been compared to a classical tree edit distance.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:5859
Deposited By:José Iñesta
Deposited On:08 March 2010