Learning Jazz Pianist Style with Cross-Attention Conditioning
Jazz pianists develop distinctive traits that experienced listeners can often identify within seconds, yet the features underlying this recognition resist formal description. We study jazz pianist style through the lens of a pretrained symbolic music transformer, showing that its learned representations already encode pianist identity well enough for highly accurate classification across two benchmarks. We then augment the transformer with cross-attention over learned pianist identity embeddings, enabling it to generate music conditioned on a specific artist's style. Two evaluation protocols confirm that the generator captures meaningful stylistic structure: a sliding-window classifier co...
| Authors | Drew Edwards, Akira Maezawa, Simon Dixon |
|---|---|
| Primary category | cs.SD |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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