Real-Validated UAV Audition Under Rotor Ego-Noise for Low-False-Alarm Human Detection
Abstract: Detecting human acoustic cues from UAV-mounted microphones could support acoustic search and rescue, but rotor ego-noise often masks speech, cries, coughs, and other human sounds at extremely low SNRs. We study UAV human-audible-presence detection under this real operating constraint. Models are trained on a reproducible synthetic mixture pipeline built from public audio, but selected and evaluated on real DroneAudioSet recordings using a metadata-defined audibility filter, recording-grouped Dev/Test splits, and group-bootstrap confidence intervals. Our results show that synthetic accuracy is a weak and non-monotonic proxy for real UAV transfer: a from-scratch SE-ResNet appears competitive on synthetic mixtures but collapses on real ego-noise, while frozen audio foundation models and lightweight adapters transfer more reliably. We further evaluate a BEATs adapter family with rotor-aware conditioning and domain regularization. The Real-Dev-selected EgoRAP-DA configuration achieves the best locked-test low-false-alarm recall among the candidates, but its advantage over a vanilla adapter is not statistically significant under paired group bootstrap. The main contribution is therefore a real-validated benchmark and evaluation protocol showing that honest progress in UAV audition requires real, group-level validation rather than synthetic scores alone.