Scaling and compressing melodies using geometric similarity measures

TL;DR

This paper introduces geometric matching algorithms to measure and optimize melody similarity through scaling and compression, enhancing music retrieval techniques.

Contribution

It presents novel algorithms for melody scaling and compression based on geometric similarity measures, improving efficiency in music information retrieval.

Findings

Efficient algorithms for melody scaling and compression

Improved accuracy in melodic similarity measurement

Enhanced retrieval performance in music databases

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 melodies. We represent music as sets of points or sets of horizontal line segments in the Euclidean plane and propose efficient algorithms for optimization problems inspired in two operations on melodies; linear scaling and audio compression. In the scaling problem, an incoming query melody is scaled forward until the similarity measure between the query and a reference melody is minimized. The compression problem asks for a subset of notes of a given melody such that the matching cost between the selected notes and the reference melody is minimized.