What were you expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music
This work addresses the challenge of modeling expressive musical performances for applications in music analysis and synthesis, but it is incremental as it builds on existing models and focuses on a specific dataset.
The paper tackled the problem of predicting expressive tempo and dynamics in classical piano music performances by using expectancy features from a computational model of auditory expectation, finding that these features significantly improved tempo predictions.
In this paper we present preliminary work examining the relationship between the formation of expectations and the realization of musical performances, paying particular attention to expressive tempo and dynamics. To compute features that reflect what a listener is expecting to hear, we employ a computational model of auditory expectation called the Information Dynamics of Music model (IDyOM). We then explore how well these expectancy features -- when combined with score descriptors using the Basis-Function modeling approach -- can predict expressive tempo and dynamics in a dataset of Mozart piano sonata performances. Our results suggest that using expectancy features significantly improves the predictions for tempo.