94.3% accuracy at -5dB SNR · sub-50ms inference · 14 threat categories
Overview
A defence installation needed a system to monitor acoustic signals across a wide perimeter and classify potential threats — vehicles, footsteps, digging, gunfire — in real-time without visual detection.
Challenge
Acoustic signal classification in outdoor environments with high ambient noise is an unsolved hard problem. The system needed to achieve >92% classification accuracy at SNR levels as low as -5dB. All processing had to be on-device — no network latency tolerable.
Solution
We developed a custom CNN-Transformer hybrid model trained on 400 hours of labelled acoustic data collected at the installation. Whisper ASR architecture was adapted for non-speech audio classification. Deployed on edge computing nodes with sub-50ms inference. 14 threat categories with confidence scoring.
Outcome
94.3% classification accuracy at -5dB SNR. Sub-50ms inference latency. Successfully detected 3 real unauthorised intrusion attempts during field trials. Currently in active deployment.