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README.md
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---
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license: mit
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library_name: drum_dynamics
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tags:
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- drums
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- midi
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- velocity
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- dynamics
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- music
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- lightgbm
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- tabular
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datasets:
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- e-gmd
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metrics:
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- mae
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- rmse
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---
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# Dynamics Needed — LightGBM velocity baseline
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**Version:** 0.1.0
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Gradient-boosted-tree baseline that predicts per-note velocities ("dynamics")
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for MIDI drum tracks from tabular note features. Trained on the Expanded Groove
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MIDI Dataset (E-GMD). Part of the *Dynamics Needed* thesis project.
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## Intended use
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Given a MIDI drum track with flat/undynamic velocities, predict a
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"best-fitting" velocity per note to restore human-like dynamics. This is the
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tabular baseline; see the MDN transformer model for the probabilistic variant.
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## Training data
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[E-GMD](https://magenta.tensorflow.org/datasets/e-gmd) (Expanded Groove MIDI
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Dataset), evaluated on the held-out test split.
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## Metrics (test split)
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| model | MAE | RMSE |
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|---------------|----------------------|-----------------------|
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| Global-mean | 29.668 | 34.638 |
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| Lookup table | 21.393 | 28.380 |
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| **LightGBM** | 18.020 | 24.072 |
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Per-track Pearson (LightGBM): 0.706
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## Limitations
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This is a **point** (single-value) predictor and a **baseline** — published for
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reproducibility and comparison, not as a final production model.
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- **Flattens dynamics.** It regresses toward the conditional mean (std ratio
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~0.69), so it cannot reproduce the full velocity distribution / ghost-note
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tails. Restoring that spread is the job of the probabilistic transformer heads.
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- **Absolute-loudness generalization gap.** On drummers unseen in training,
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point MAE degrades (~19 → ~28); a player's overall loudness is not inferable
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from structure alone. Relative dynamics (per-track / within-bar ranking)
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transfer better than absolute level.
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- **Known data artifact.** E-GMD's multi-kit rendering remaps pads to different
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voices per kit, which biases some per-voice results; a single-kit rebuild is a
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pending fix. See the project's `docs/methodology/kit-remapping-artifact.md`.
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- **No listening test yet.** Numbers here are offline metrics; perceptual
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A/B validation is future work.
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## License
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Set to `mit` by default — change to match the thesis's chosen license before
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publishing.
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