  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.
