  We propose a new graph convolutional block, called MusGConv, specifically
designed for the efficient processing of musical score data and motivated by
general perceptual principles. It focuses on two fundamental dimensions of
music, pitch and rhythm, and considers both relative and absolute
representations of these components. We evaluate our approach on four different
musical understanding problems: monophonic voice separation, harmonic analysis,
cadence detection, and composer identification which, in abstract terms,
translate to different graph learning problems, namely, node classification,
link prediction, and graph classification. Our experiments demonstrate that
MusGConv improves the performance on three of the aforementioned tasks while
being conceptually very simple and efficient. We interpret this as evidence
that it is beneficial to include perception-informed processing of fundamental
musical concepts when developing graph network applications on musical score
data.
