feedBack/docs/benchmarks/note_detect_v1/BENCHMARK.md

FeedBack Note Detect Benchmark — v1

A short test piece for tuning FeedBack's note_detect plugin. Eight exercises, each isolating a specific detection failure mode. Run with Detect enabled, play through, then export the diagnostic JSON (Settings → Plugins → Note Detection → Download Diagnostic JSON, or the button on the end-of-session summary modal).

• Tempo: 90 BPM
• Tuning: E standard (no capo)
• Audio: metronome click track only (downbeat = louder + higher tone). Play over the click — note_detect listens to your guitar signal, not the audio in this file.
• Duration: 139 s

Sections

Section	Tests	Watch in the diagnostic
A. Open strings (low→high→low)	Basic mono detection on each open string	pure (mic/audio chain), per-string accuracy
B. 5th-fret positions	Fretted-note detection across all 6 strings	per-string variance
C. 12th-fret octaves	Higher-frequency detection — YIN's octave-up risk	sharp bin spiking
D. Sustained notes (4 s)	The active held-on-pitch glow	sharp/flat drift while held
E. Hammer-on / pull-off	Transient detection without a fresh pick attack	pure (no transient registered)
F. Power chords (2-string)	Chord leniency on sparse voicings	chordPartial
G. Open major chords	Chord leniency on dense voicings (E, A, D, G)	chordPartial
H. Bends (half- + whole-step)	Single-note pitch tolerance with pitch in motion	sharp bin

Reporting

Share the JSON (schema note_detect.diagnostic.v1). It includes:

• Hit/miss totals split single-note vs chord
• Primary-cause bin per miss (pure / chord-partial / early / late / sharp / flat)
• Per-string hit rate
• Signed timing- and pitch-error percentiles (p10 / median / p90)
• Detection settings snapshot (method, tolerances, leniency)
• Per-judgment event log (capped at 2000 events) with the chart note's technique flags so each miss can be re-binned by SUS/B/H/etc. offline
• benchmark_hint: {title, artist, arrangement} — filter on these to bucket reports from different runs of this benchmark.

Source

Built by docs/benchmarks/note_detect_v1/build_benchmark.py in the feedBack repo. Tweak the exercise list there and regenerate.