Adds reproducible tooling to test whether genuinely-independent rtl_433 test
captures can relieve the statistical classifier's minority-class starvation
(train: Weather 12, Security 2). Local Flipper corpora are byte-identical
mirrors, so rtl_433 is the only independent on-disk source.
- rtl433_iq_demod.py: .cu8 (interleaved uint8 IQ) -> Flipper-style signed µs
pulse train via amplitude/OOK demod. A quality-gate self-rejects FSK/degenerate
captures. Recovered pulse widths match rtl_433's own -A analysis.
- experiment_rtl433_augment.py: conservative folder->category map + an HONEST
evaluation — train on Flipper (group-disjoint) +/- rtl_433, score on held-out
FLIPPER (regression guardrail) and held-out rtl_433 (new capability).
RESULT: adding rtl_433 to training DEGRADES the Flipper gate metric
(balanced 0.625 -> 0.412), collapsing Remote Control (20/26 -> 7-10/26) as
cross-domain samples bleed into Flipper's remote region and their volume swamps
it. Small capped adds keep Flipper within seed-noise while gaining large
rtl_433-domain Weather recognition — a coverage-vs-precision tradeoff, not a
clean win. Per gate-metric discipline the production model is NOT regenerated;
the tooling is kept to re-measure in-domain once real Flipper weather/security
captures arrive via the platform.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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