Complex Network Application in Music: Uncovering Universal Properties in Favourable Human Perception of Music

Abstract

Across cultures, and between individuals, certain musical pieces are consistently rated more favourably than others and the mathematical analysis of musical perception has a long history [1]. In contrast to previous descriptive mathematical analyses, we introduce a data-driven transformation to represent a musical score as a complex network [2, 3]. We find that musical scores which are widely perceived to be "good" generate complex networks with certain invariant properties: scale-free networks with strong clustering of nodes within the network. We describe a method to generate random musical compositions from these networks (essentially, as a weighted random walk on the network) and find that scores generated in this manner are also perceived to be "good" and are qualitatively similar to the specific score from which the generating network was produced.