A Traditional Approach to Symbolic Piano Continuation

Recent developments in sequence modeling have allowed continuation to be viewed as an autore-gressive task, to be modeled with a suitable tokenization scheme and a powerful sequence model like the ubiquitous Transformer [1]. A nonexhaustive list of prior work in this vein includes the Music Transformer [2], Museformer [3], FIGARO [4], and MuseCoco [5]. Most research in symbolic music modeling has so far focused on generalizing these techniques to--and improving performance on--long-sequence, multitrack, multi-instrument, and/or text-or attribute-controllable generative tasks. Typically, specialized techniques must be developed for these foundation models to handle these harder tasks, such as fine-and coarse-grained attention for long sequences [3], and text feature extraction techniques [4] and attribute augmentation [5] for controllability.