Investigating the Viability of Masked Language Modeling for Symbolic Music Generation in abc-notation

The dominating approach for modeling sequences (e.g. text, music) with deep learning is the causal approach, which consists in learning to predict tokens sequentially given those preceding it. Another paradigm is masked language modeling, which consists of learning to predict the masked tokens of a sequence in no specific order, given all non-masked tokens. Both approaches can be used for generation, but the latter is more flexible for editing, e.g. changing the middle of a sequence. This paper investigates the viability of masked language modeling applied to Irish traditional music represented in the text-based format abc-notation. Our model, called abcMLM, enables a user to edit tunes in arbitrary ways while retaining similar generation capabilities to causal models. We find that generation using masked language modeling is more challenging, but leveraging additional information from a dataset, e.g., imputing musical structure, can generate sequences that are on par with previous models.
QC 20240604Part of ISBN 978-3-031-56991-3; 978-3-031-56992-0