  Music Information Retrieval (MIR) has seen a recent surge in deep
learning-based approaches, which often involve encoding symbolic music (i.e.,
music represented in terms of discrete note events) in an image-like or
language like fashion. However, symbolic music is neither an image nor a
sentence, and research in the symbolic domain lacks a comprehensive overview of
the different available representations. In this paper, we investigate matrix
(piano roll), sequence, and graph representations and their corresponding
neural architectures, in combination with symbolic scores and performances on
three piece-level classification tasks. We also introduce a novel graph
representation for symbolic performances and explore the capability of graph
representations in global classification tasks. Our systematic evaluation shows
advantages and limitations of each input representation. Our results suggest
that the graph representation, as the newest and least explored among the three
approaches, exhibits promising performance, while being more light-weight in
training.
