Machine-learning models have identified distinctive melodic, harmonic and rhythmic patterns that can distinguish 20 leading jazz pianists with up to 94 percent accuracy.

Artificial intelligence can identify the individual artistic fingerprints of jazz pianists by analysing the musical patterns embedded in their improvisations, according to a new study published in Nature Machine Intelligence.

Researchers Huw Cheston, Reuben Bance and Peter M.C. Harrison trained a series of machine-learning models on 84 hours of recordings by 20 prominent jazz pianists. The study aimed not simply to determine who was playing an unidentified passage, but to investigate the musical characteristics that make one performer recognisably different from another. The researchers describe these recurring characteristics as an artistic “fingerprint”.

Oscar Peterson, Ray Brown (bass) and Ed Thigpen (drums). Concertgebouw, Amsterdam, 1959. Photo © Wim van Rossem / Wikimedia Commons

The dataset comprised 1,629 performances drawn from two existing collections of transcribed jazz recordings, including both unaccompanied piano performances and piano solos from trio recordings. The researchers converted the recordings into symbolic musical representations and examined melody, harmony, rhythm and dynamics.

The most sophisticated model identified the pianist correctly in 94 percent of cases. A more interpretable model...