Rhythmic qualities of jazz improvisation predict performer identity and style in source-separated audio recordings.

Great musicians have a unique style and, with training, humans can learn to distinguish between these styles. What differences between performers enable us to make such judgements? We investigate this question by building a machine learning model that predicts performer identity from data extracted automatically from an audio recording. Such a model could be trained on all kinds of musical features, but here we focus specifically on rhythm, which (unlike harmony, melody and timbre) is relevant for any musical instrument.