engine/correlate.py recovers where each hand-cut loop was cut from: a loop is a
literal slice of a separated stem, so normalized cross-correlation of the waveform
has a razor peak at the true offset (true slice ~1.0, foreign ~0.03). build_provenance.py
runs it over the whole corpus → provenance.json.
Result (honest): 482/1690 loops matched (29%) across 28 kits — only the recent
demucs-derived kits have a source in separated/; the rest predate the workflow, and
we quantify that rather than pretend. Recoveries are exact and name-confirming:
bumbum←MC Fioti "Bum Bum Tam Tam", diams_dj←Diam's "DJ", like_sugar←Chaka Khan
"Like Sugar", praise←Fatboy Slim. Cross-stem alignment 0.34 — the measured answer to
§0's open question (loops share a window across stems about a third of the time).
Two bugs caught and fixed during the build, both load-bearing:
- NAIVE all-pairs full-res NCC took 56 MIN on 1690×120. Fix: coarse-to-fine —
rank every stem by NCC on a frame-RMS *energy envelope* (phase-robust, ~40× cheaper),
then run the precise sample-domain NCC only on the top-3 candidates. Minutes, not an hour.
- SILENT-WINDOW BLOWUP: catastrophic cancellation in the sliding-variance cumsum
drove the normaliser to ~0 in quiet stem regions, so silence scored ∞ and
masqueraded as a perfect match (an early run reported a bogus 55%/0.69 — ~440 false
positives). Fix: floor the window norm at 5% of the stem's global energy and clamp
NCC to [-1,1]. Scores are now all ≤1.0 (max 1.000, median 0.969); the honest 29%
is what survived. Regression test added.
forkserver gotcha: py3.14's default mp start method re-imports the module per worker
(empty globals), so the preloaded-stems COW trick silently fails — pinned mp fork
context. 8 correlate tests (NCC offset/foreign/gain-invariance/silent-window, envelope
prefilter, locate top-k). Feeds the tierlist cut-critique and the finder recall eval (#7).
