  In the pursuit of developing expressive music performance models using
artificial intelligence, this paper introduces DExter, a new approach
leveraging diffusion probabilistic models to render Western classical piano
performances. In this approach, performance parameters are represented in a
continuous expression space and a diffusion model is trained to predict these
continuous parameters while being conditioned on the musical score.
Furthermore, DExter also enables the generation of interpretations (expressive
variations of a performance) guided by perceptually meaningful features by
conditioning jointly on score and perceptual feature representations.
Consequently, we find that our model is useful for learning expressive
performance, generating perceptually steered performances, and transferring
performance styles. We assess the model through quantitative and qualitative
analyses, focusing on specific performance metrics regarding dimensions like
asynchrony and articulation, as well as through listening tests comparing
generated performances with different human interpretations. Results show that
DExter is able to capture the time-varying correlation of the expressive
parameters, and compares well to existing rendering models in subjectively
evaluated ratings. The perceptual-feature-conditioned generation and
transferring capabilities of DExter are verified by a proxy model predicting
perceptual characteristics of differently steered performances.
