Title: A Computational Study of the Role of Tonal Tension in Expressive Piano Performance,
Abstract: Expressive variations of tempo and dynamics are an important aspect of music
performances, involving a variety of underlying factors. Previous work has
showed a relation between such expressive variations (in particular expressive
tempo) and perceptual characteristics derived from the musical score, such as
musical expectations, and perceived tension. In this work we use a
computational approach to study the role of three measures of tonal tension
proposed by Herremans and Chew (2016) in the prediction of expressive
performances of classical piano music. These features capture tonal
relationships of the music represented in Chew's spiral array model, a three
dimensional representation of pitch classes, chords and keys constructed in
such a way that spatial proximity represents close tonal relationships. We use
non-linear sequential models (recurrent neural networks) to assess the
contribution of these features to the prediction of expressive dynamics and
expressive tempo using a dataset of Mozart piano sonatas performed by a
professional concert pianist. Experiments of models trained with and without
tonal tension features show that tonal tension helps predict change of tempo
and dynamics more than absolute tempo and dynamics values. Furthermore, the
improvement is stronger for dynamics than for tempo.