Representing Melodic Relationships Using Network Science
50 Pages Posted: 1 Jun 2022
Music is a complex system consisting of many dimensions and hierarchically organized information—the organization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music. Using a large corpus of transcribed improvisations, we constructed a network model in which 5-pitch sequences were linked depending on consecutive occurrences, constituting 116,407 nodes (sequences) and 157,429 edges connecting them. We found that the network exhibited structural properties that resemble “scale-free” networks (i.e., networks with degree distribution following a power law). We then investigated whether mathematical graph modeling relates to musical characteristics in real-world listening situations via a behavioral experiment paralleling those used to construct semantic networks in language. We found that as distance within the network increased, participants judged melodic sequences as less related. Moreover, the relationship between distance and reaction time (RT) judgments was quadratic: participants slowed in RT up to distance four, then accelerated; a parallel finding to research in language networks. This study offers insights into the hidden network structure of improvised tonal music and suggests that humans are sensitive to the property of melodic distance in this network.
Keywords: music, network science, improvisation
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