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The University of Southampton
MusicPart of Humanities

Research project: Jazz as Social Machine

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Music is data. AI researchers are beginning to use this data to create machines that make music. This project takes jazz improvisation as a (thorny) example of a problem that haunts AI-generated art: what happens to the human? The answers to this question will be in the form of scholarly and creative reflection. Alan Turing Fellowship, 2019-2021

Good jazz improvisation is the result of a complex social interaction. The improviser’s approach to the “changes” or horizontal chord structures of song tunes or “standards” - classic jazz’s underlying data set - depends on individually acquired historical awareness. A great jazz solo ingeniously “quotes” previous jazz solos and may even shift stylistic register or improvisational language midstream for expressive purposes. Such advanced improvisational constructions are in turn preserved (often by mechanical media) and become part of a given jazz community’s “memory” of a tune. Thus an excellent improvisation “annotates” the original tune and is situated within that tune’s history.

One might claim that this intertextual quality of a great jazz improvisation parallels the validation of financial value by blockchains. The blockchain - numerical data that aggregates over time - requires ever more computer power to authenticate financial transactions. Likewise, to “know” the improvisational history of a tune, an AI would require significant amounts of training data. The result would entail statistically governed appearances of “historical” material in idiomatically and communicatively effective locations or contexts. The problem seems complex: even if it had access to the entire history of jazz, how could an AI, on the basis of statistics alone, know what to do and when?

Associated research themes

Web Science Institute

Related research groups

Musicology and Ethnomusicology
Music Performance Research
Composition and Music Technology
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