---
license: mit
tags:
- music-generation
- midi
- transformer
- symbolic-music
datasets:
- maestro
- asap
---

# 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.