Tree language automata for melody recognition
The representation of symbolic music by means of trees has shown to be suitable in melodic similarity computation. In order to compare trees, different tree edit distances have been previously used, being their complexity a main drawback. In this paper, the application of stochastic k-testable treemodels for computing the similarity between two melodies as a probability, compared to the classical edit distance has been addressed. The results show that k-testable tree-models seem to be adequate for the task, since they outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs. For it, the utilized method must be able to deal with inexact queries and efficiency for scalability issues.