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cnmat/MIDI_tokenization_models

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Evaluating Tokenization Strategies for Expressive Classical Piano Performance Generation

This repository contains official artifacts for the paper "Evaluating Tokenization Strategies for Expressive Classical Piano Performance Generation", submitted to the 29th International Conference on Digital Audio Effects (DAFx26).

We provide six composer/genre-conditional MIDI Transformers, each trained on a distinct tokenization scheme to systematically evaluate expressive piano performance generation.

Model Variants & Tokenization Modes

Subfolder	Tokenization Mode	Target Features
`note`	`note`	Onset, Pitch & duration
`note_pedal`	`note_pedal`	Onset, Pitch, duration, sustain pedal
`note_velocity`	`note_velocity`	Onset, Pitch, duration, velocity
`note_velocity_beat`	`note_velocity_beat`	Onset, Pitch, duration, velocity, beat markers
`note_velocity_pedal`	`note_velocity_pedal`	Onset, Pitch, duration, velocity, sustain pedal
`full`	`full`	Complete expressive performance attributes

Training Strategy

—Pre-training: Lakh MIDI + MAESTRO dataset (Note: Lakh MIDI was incorporated during pre-training to improve generation diversity, expanding upon the baseline setup described in the paper).
—Fine-tuning: ASAP (Aligned Scores and Performances) dataset.