  Emotion and expressivity in music have been topics of considerable interest
in the field of music information retrieval. In recent years, mid-level
perceptual features have been suggested as means to explain computational
predictions of musical emotion. We find that the diversity of musical styles
and genres in the available dataset for learning these features is not
sufficient for models to generalise well to specialised acoustic domains such
as solo piano music. In this work, we show that by utilising unsupervised
domain adaptation together with receptive-field regularised deep neural
networks, it is possible to significantly improve generalisation to this
domain. Additionally, we demonstrate that our domain-adapted models can better
predict and explain expressive qualities in classical piano performances, as
perceived and described by human listeners.
