Gunshot Detection System
A real-time audio classification pipeline for detecting gunshots in urban environments, with mobile app integration for instant community alerts.
The Challenge
Community safety in urban environments requires rapid detection and response to gunshot incidents. Traditional methods rely on human reporting, which introduces delays and inconsistency.
An automated system needs to be fast, accurate, and respect privacy while providing actionable alerts to community members and authorities.
Constraints
- Sub-second detection latency for immediate alerting
- High precision to minimize false alarms
- Privacy-preserving (no voice/conversation recording)
- Edge inference for reduced bandwidth and privacy
- Battery-efficient mobile deployment
Solution Architecture
Implementation Details
Audio Classification Model
CNN trained on curated acoustic datasets including gunshots, fireworks, car backfires, and urban noise. Transfer learning from audio classification backbone.
Edge Deployment
Model quantized to INT8 for TensorFlow Lite, enabling on-device inference with <50ms latency and minimal battery impact.
Privacy Architecture
All audio processing happens on-device. Only detection events (no audio) are transmitted. Location data is anonymized to neighborhood level.
False Positive Reduction
Multi-stage filtering: amplitude trigger, model confidence threshold, temporal smoothing, and location correlation to reduce alerts from similar but benign sounds.
Results
<500ms
End-to-end detection latency
94%
True positive rate
<2%
False positive rate
The system was designed with community input and deployed with full transparency about what data is collected and how it's used. Privacy-by-design was a core requirement, not an afterthought.
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