| --- |
| license: mit |
| tags: |
| - music-generation |
| - midi |
| - transformer |
| - symbolic-music |
| datasets: |
| - maestro |
| - asap |
| --- |
| |
| # Evaluating Tokenization Strategies for Expressive Classical Piano Performance Generation |
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| 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)*. |
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| We provide six composer/genre-conditional MIDI Transformers, each trained on a distinct tokenization scheme to systematically evaluate expressive piano performance generation. |
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| ## Model Variants & Tokenization Modes |
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| | 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 | |
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| ## Training Strategy |
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| * **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. |
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