  This paper describes a computational model of loudness variations in
expressive ensemble performance. The model predicts and explains the continuous
variation of loudness as a function of information extracted automatically from
the written score. Although such models have been proposed for expressive
performance in solo instruments, this is (to the best of our knowledge) the
first attempt to define a model for expressive performance in ensembles. To
that end, we extend an existing model that was designed to model expressive
piano performances, and describe the additional steps necessary for the model
to deal with scores of arbitrary instrumentation, including orchestral scores.
We test both linear and non-linear variants of the extended model n a data set
of audio recordings of symphonic music, in a leave-one-out setting. The
experiments reveal that the most successful model variant is a recurrent,
non-linear model. Even if the accuracy of the predicted loudness varies from
one recording to another, in several cases the model explains well over 50% of
the variance in loudness.
