Exploring Pianist Performance Styles with Evolutionary String Matching

We propose novel machine learning methods for exploring the domain of music perfor- mance 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 simi- larities 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. Keywords: Self Organizing Map, Evolutionary Algorithm, Approximate String Matching, Expressive Music Performance