Scaling and compressing melodies using geometric similarity measures

Abstract

Melodic similarity measurement is of key importance in Music Information Retrieval. In this paper, we use geometric matching techniques to measure the similarity between two monophonic melodies. We propose efficient algorithms for optimization problems inspired in two operations on melodies: scaling and compressing. In the scaling problem, an incoming query melody is scaled forward until the similarity measure between the query and the reference melody is minimized. The compressing problem asks for a subset of notes of a given melody so that the matching cost between the selected notes and the reference melody is minimized.