An analysis of style-types in musical improvisation using clustering methods

Abstract

Research on creativity examines both the processes and products of creativity. An important avenue for analyzing creativity is by means of spontaneous improvisation, although there are major challenges to characterizing the output of improvisation due to the variable nature of the products. In the case of musical improvisation, structural approaches have used methodologies like musical transcription to look for recurring or variable musical features across a corpus of improvisations, while creativity-centered approaches have had experts make ratings of the novelty of the improvisations. One important concept missing from many analyses of improvisation is the idea that the products of a corpus can be organized into a series of “style types”, where each type differs from others in certain key structural features. Clustering methods provide a reliable quantitative means of examining the organization of style types within a diverse corpus of improvisations. In order to look at the potential of such methods, we examined a corpus of 72 vocal melodic improvisations produced by novice improvisers. We first classified the melodies acoustically using a multidimensional musical-classification scheme called CantoCore, which coded the melodies for 19 distinct features of musical structure. We next employed the simultaneous use of multiple correspondence analysis (MCA) and k-means cluster analysis with the data, and obtained three relatively discrete clusters of improvisations. Stylistic analysis of these clusters revealed that they differed in key features related to phrase structure and rhythm. Cluster analyses provide a promising means of describing and analyzing the products of creativity, including variable structures like spontaneous improvisations.