Exploring Pianist Performance Styles with Evolutionary String Matching

We propose novel machine learning methods for exploring the domain of music performance praxis. Based on simple measurements of timing and intensity in 12 recordings of a Schubert piano piece, short performance sequences are fed into a SOM algorithm in order to calculate ‘performance archetypes’. The archetypes are labeled with letters and approximate string matching done by an evolutionary algorithm is applied to find similarities in the performances represented by these letters. We present a way of measuring each pianist’s habit of playing similar phrases in similar ways and propose a ranking of the performers based on that. Finally, an experiment revealing common expression patterns is briefly described.