Deconstructing Jazz Piano Style Using Machine Learning

For a visual artist, their style might include aspects such as subject choice, colour choice, and brush techniques; for a writer, it might include vocabulary, syntactic constructions, and narrative archetypes; for a composer, it might include harmonic progressions, rhythmic patterns, and melodic motifs. Individual differences across all these parameters, and more, come together to define each artist's unique style. Most of these stylistic parameters can theoretically be assessed by human experts. However, such assessments are necessarily slow and hence hard to apply at scale. Subjectivity is also a problem, since every human analyst comes with their own history of artistic exposure that will inevitably affect how they interpret artworks. Computational methods promise a more scalable and objective approach to this problem. Once a researcher has crafted an algorithm that captures a particular stylistic parameter -- for example, using entropy to capture vocabulary complexity -- then a computer can easily apply the algorithm to large datasets, and hence compare different artists using this parameter (Abry et al., 2013; Cheston et al., 2024b; Deepaisarn et al., 2023; Li et al., 2012).