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Machine learning of artistic fingerprints in jazz
Cheston, H., Bance, R., & Harrison, P. M. C. (2026). Machine learning of artistic fingerprints in jazz. Nature Machine Intelligence, 8(8), 1261–1274.
Artists are often recognizable through distinctive patterns—“fingerprints”—in their work. This paper trains machine-learning models to identify 20 iconic jazz pianists from 84 hours of recordings, including a multi-input architecture that analyses melody, harmony, rhythm, and dynamics separately. The best model reaches 94% accuracy across 20 classes, and the models reveal which musical elements most strongly distinguish individual artists.
Open-source model code and an interactive web app accompany the paper.
2026
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2019
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2018
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2016
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