Towards computer-assisted understanding of dynamics in symphonic music
This work addresses the challenge of objectively understanding expressive variations in classical music for listeners and researchers, though it is incremental as the model is still in development.
The authors tackled the problem of quantifying differences in conductor interpretations of symphonic music by developing a computational model that analyzes dynamics in performances relative to the musical score, demonstrating its predictive power and ability to identify conductor idiosyncrasies.
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.