Modelling the creative process of jazz improvisation
The ever-increasing accessibility of large music datasets makes it feasible to create data-driven models of musical styles using machine learning algorithms. Such models can be used to characterise the style(s) represented in the collection, increasing our understanding of the art form, or for computational tasks such as classification of new data, or for creative tasks such as the generation of new music in a similar style to that which was modelled. Building on the work of the "Dig That Lick" project, which focusses on the extraction of melodic patterns from jazz recordings, and a new partnership with a major jazz festival, this PhD will develop models of improvisational style, relating the melodic material to the underlying harmonic and rhythmic structure of the piece. The research will shed light on the relationship between individual and collective style, and the extent to which novelty is a feature of improvisation.
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