# Output examples

**Elegy in C-sharp minor** — 608 notes, every one composed by the model (plan-driven: it states a
theme, develops it by fragmenting, inverting and transposing, then brings it back transformed).

Both files below contain **identical pitches**. Nothing was added, removed or re-pitched. The only
difference is *how they are played*.

| file | |
|---|---|
| `elegy_01_score_as_composed.mid` / `.mp3` | raw model output: no pedal, near-flat velocities, onsets square on the beat |
| `elegy_02_performed.mid` / `.mp3` | the same 608 notes performed — melody voiced above the accompaniment, chords rolled by choice, rubato around a felt pulse, 196 pedal events caught at harmony changes |

## Why the pair matters

Play `01` and then `02`. The gap between them is the most useful thing we learned building this
model: **a dry-sounding symbolic generation is usually not a composition failure but an unperformed
one.** Pitch content and performance are separable layers, and performance carries far more of the
perceived quality than we expected.

There's an acoustic reason as well as a musical one. Staggering a chord's attacks by roughly 20–70 ms
spreads its beating partials out in time, which measurably lowers sensory roughness, so rolled chords
aren't merely more expressive than struck ones, they're *cleaner*. Quantising a dense passage onto a
grid makes it rougher, not just stiffer.

If you're evaluating this model (or any symbolic music model) on raw output, you're hearing about
half of what's there.