Methods of Automated Music Comparison Based on Multi-Objective Metrics of Network Similarity

Abstract

This paper describes methods and techniques of measuring similarity of musical pieces. This topic is crucial in plagiarism control and arrangement evaluation as these processes depend in particular on a previous experience and subjective aesthetical feelings of a researcher. Although there are some common frameworks for comparing musical pieces (i.e., some characteristics of compared pieces and details to consider), having a set of comprehensive metrics would allow to make such comparisons more unbiased. We show that such a comparison can be made using a network representation of a track. Tracks are compared using a structural and quantitative similarity between matrices corresponding to these musical pieces. In this article, we describe network representations of music. We introduce a set of specific methods of calculating this similarity and study their characteristics. We also evaluate them on the set of test pieces and provide results. We show that this method can be especially used for detecting instances of plagiarism between pieces and evaluating similarity of created arrangements, thus measuring their “innovativeness”.

Keywords

• graph theory
• automated music arrangement
• musical pieces comparison
• music representation
• multi-objective optimization
• computational musicology