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In this work we present a new approach for the task of predicting fingeringsfor piano music. While prior neural approaches have often treated this as asequence tagging problem with independent predictions, we put forward achecklist system, trained via reinforcement learning, that maintains arepresentation of recent predictions in addition to a hidden state, allowing itto learn soft constraints on output structure. We also demonstrate that bymodifying input representations -- which in prior work using neural models haveoften taken the form of one-hot encodings over individual keys on the piano --to encode relative position on the keyboard to the prior note instead, we canachieve much better performance. Additionally, we reassess the use of rawper-note labeling precision as an evaluation metric, noting that it does notadequately measure the fluency, i.e. human playability, of a model's output. Tothis end, we compare methods across several statistics which track thefrequency of adjacent finger predictions that while independently reasonablewould be physically challenging to perform in sequence, and implement areinforcement learning strategy to minimize these as part of our training loss.Finally through human expert evaluation, we demonstrate significant gains inperformability directly attributable to improvements with respect to thesemetrics.

Checklist Models for Improved Output Fluency in Piano Fingering Prediction