Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy

Abstract

We present a new rule learning algorithm named PLCG — a kind of ensemble learning method — that can find simple, robust partial theories (sets of classification rules) in complex data where neither high coverage nor high precision can be expected. The motivating application problem comes from an interdisciplinary research project that aims at discovering fundamental principles of expressive music performance from large amounts of complex real-world data (measurements of actual performances by concert pianists). It is shown that PLCG succeeds in finding some surprisingly simple and robust performance principles, some of which represent truly novel and musically meaningful discoveries. A more systematic experiment shows that PLCG learns significantly simpler theories than more direct approaches to rule learning, while striking a compromise between coverage and precision.