Evaluating Tokenization Strategies for Expressive Classical Piano Performance Generation
Expressive piano performance generation needs symbolic pitch, timing, and dynamics. We evaluate six tokenization strategies for a Transformer that generates classical piano performances. Our tokenizations add velocity, beat annotations, and sustain pedal, from note-only to full representations. We pretrain on MAESTRO, then finetune on ASAP with beat-level annotations. The model uses anticipatory-style note encoding with cross-attention on composer and genre. FAD on the ASAP test set shows that note + velocity + pedal and full modes achieve the lowest mean FAD (1.76 and 1.97). Both beat the note-only baseline (3.10). Beat tokens show mixed, category-dependent effects and do not improve the best modes on average.