An Assessment of Learned Score Features for Modeling Expressive Dynamics in Music

The study of musical expression is an ongoing and increasingly data-intensive endeavor, in which machine learning techniques can play an important role. The purpose of this paper is to evaluate the utility of unsupervised feature learning in the context of modeling expressive dynamics, in particular note intensities of performed music. We use a note centric representation of musical contexts, which avoids shortcomings of existing musical representations. With that representation, we perform experiments in which learned features are used to predict note intensities. The experiments are done using a data set comprising professional performances of Chopin's complete piano repertoire. For feature learning we use Restricted Boltzmann machines, and contrast this with features learned using matrix decomposition methods. We evaluate the results both quantitatively and qualitatively, identifying salient learned features, and discussing their musical relevance.