Abstract
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 intrinsically, and research in the symbolic domain is lacking 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.
Emmanouil Karystinaios
Ph.D. Student
My research interests include Music Information Retrieval and Graph Neural Networks