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Identifying such traits has important applications in authorship attribution, education, cultural heritage research and historical analysis. Here we focus on music, a domain with a rich tradition of theoretical and mathematical analysis. We train a variety of supervised learning models to identify 20 iconic jazz musicians from a curated dataset of 84\u2009h of recordings. In particular, we introduce a multi-input architecture that represents four musical domains separately: melody, harmony, rhythm and dynamics. This design allows us to accurately identify individual performers (our best model obtains 94% accuracy across 20 classes) and to examine which musical elements most strongly distinguish between individual artists. We release open-source implementations of our models and an accompanying web application for exploring our results.<\/jats:p>","DOI":"10.1038\/s42256-026-01279-9","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:03:26Z","timestamp":1786979006000},"page":"1261-1274","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning of artistic fingerprints in jazz"],"prefix":"10.1038","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7891-5532","authenticated-orcid":false,"given":"Huw","family":"Cheston","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8521-8461","authenticated-orcid":false,"given":"Reuben","family":"Bance","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9851-9462","authenticated-orcid":false,"given":"Peter M. 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