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Generative models, analysis by synthesis, jazz, improvisation, assessment, performance
Abstract:
This paper pursues two goals. First, we present a generative model for (monophonic) jazz improvisation whose main purpose is testing hypotheses on creative processes during jazz improvisation. It uses a hierarchical Markov model based on mid-level units and the Weimar Bebop Alphabet, with statistics taken from the Weimar Jazz Database. A further ingredient is chord-scale theory to select pitches. Second, as there are several issues with Turing-like evaluation processes for generative models of jazz improvisation, we decided to conduct an exploratory online study to gain further insight while testing our algorithm in the context of a variety of human generated solos by eminent masters, jazz students, and non-professionals in various performance renditions. Results show that jazz experts (64.4% accuracy) but not non-experts (41.7% accuracy) are able to distinguish the computer-generated solos amongst a set of real solos, but with a large margin of error. The type of rendition is crucial when assessing artificial jazz solos because expressive and performative aspects (timbre, articulation, micro-timing and band-soloist interaction) seem to be equally if not more important than the syntactical (tone) content. Furthermore, the level of expertise of the solo performer does matter, as solos by non-professional humans were on average rated worse than the algorithmic ones. Accordingly, we found indications that assessments of origin of a solo are partly driven by aesthetic judgments. We propose three possible strategies to install a reliable evaluation process to mitigate some of the inherent problems.
