Event‑Level Acoustic Emergency Detection to Support Operator Awareness in Public Transport

Abstract

We built a non visual emergency sound detection system for public transport cabins that reports event level alerts (no faces, no identities). The system consists of two stages. First, Anomalous Sound Detection (ASD) learns what normal cabin audio sounds like (e.g., bus, tram, metro, car) and identifies audio segments that deviate from this background. In this context, an anomaly corresponds to sounds such as human distress, crying, glass breaking, or sirens that stand out from engine noise and passenger chatter. ASD scans continuous audio and flags candidate segments using an anomaly score derived from reconstruction error and a threshold calibrated on normal data.Second, Sound Event Detection (SED) processes only these flagged segments and assigns an emergency related label (human distress, crying, glass break, siren).The system was evaluated on synthetic cabin audio scenes that reflect real cabin conditions, including polyphonic overlaps and SNR levels. The proposed models outperform standard baseline models. For ASD, a CNN autoencoder achieved AUC 96.98% and F1 91.71%, compared to 91.82% AUC and 81.87% F1 for a fully connected autoencoder. For SED, EfficientSED exceeded CRNN baselines (F1 59.55% vs. 50.49%, ER 0.75 vs. 0.94, PSDS1 0.40 vs. 0.33).The research results show that acoustic centric AI can provide timely and stable emergency cues under realistic public transport noise and overlap conditions, supporting operator monitoring without visual sensing.

Keywords: Acoustic Event Detection, Emergency Monitoring, Public Transport, Human-In-The-Loop Systems, Operator Support

DOI: 10.54941/ahfe1008222