Machine learning of artistic fingerprints in jazz

TL;DR - Researchers developed a machine-learning pipeline that identifies 20 iconic jazz pianists from audio recordings with up to 94% accuracy. The results show that performers have measurable artistic fingerprints shaped by melody, harmony, rhythm, and dynamics.

• Analyzes audio recordings to attribute performances to individual pianists.
• Distinguishes among 20 jazz pianists with up to 94% accuracy.
• Links stylistic identity to multiple musical dimensions rather than a single feature.
• Demonstrates how machine learning can quantify individual artistic style.