Using AI and machine learning to study expressive music performance: project survey and first report
This article presents a long-term inter-disciplinary research project situated at the intersection of the scientific disciplines of Musicology and Artificial Intelligence. The goal is to develop AI, and in particular machine learning and data mining, methods to study the complex phenomenon of expressive music performance. Formulating formal, quantitative models of expressive performance is one of the big open research problems in contemporary (empirical and cognitive) musicology. Our project develops a new direction in this field: we use inductive learning techniques to discover general and valid expression principles from (large amounts of) real performance data. The project is currently starting its third year and is planned to continue for at least four more years. In the following, we explain the basic notions of expressive music performance, and why this is such a central phenomenon in music. We present the general research framework of the project, and discuss the various challenges and research opportunities that emerge in this framework. We then briefly describe the current state of the project and list the main achievements made so far. In the rest of the paper, we discuss in more detail one particular data training approach (including a new algorithm for learning characterisation rules) that we have developed recently. Preliminary experimental results demonstrate that this algorithm can discover very general and robust expression principles, some of which actually constitute novel discoveries from a musicological viewpoint.
machine learning expressive music performance data mining