A probabilistic approach to melodic similarity

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.