  Many people enjoy classical symphonic music. Its diverse instrumentation
makes for a rich listening experience. This diversity adds to the conductor's
expressive freedom to shape the sound according to their imagination. As a
result, the same piece may sound quite differently from one conductor to
another. Differences in interpretation may be noticeable subjectively to
listeners, but they are sometimes hard to pinpoint, presumably because of the
acoustic complexity of the sound. We describe a computational model that
interprets dynamics---expressive loudness variations in performances---in terms
of the musical score, highlighting differences between performances of the same
piece. We demonstrate experimentally that the model has predictive power, and
give examples of conductor ideosyncrasies found by using the model as an
explanatory tool. Although the present model is still in active development, it
may pave the road for a consumer-oriented companion to interactive classical
music understanding.
