Symbolic Music Representations for Classification Tasks: A Systematic Evaluation
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, 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.
Keywords
Cite
@article{arxiv.2309.02567,
title = {Symbolic Music Representations for Classification Tasks: A Systematic Evaluation},
author = {Huan Zhang and Emmanouil Karystinaios and Simon Dixon and Gerhard Widmer and Carlos Eduardo Cancino-Chacón},
journal= {arXiv preprint arXiv:2309.02567},
year = {2023}
}
Comments
To be published in the Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR 2023), Milan, Italy