OCCDEC deploys AI-powered acoustic sensors that calibrate to each location's unique noise signature. Every device becomes smarter over time — reducing false positives by up to 85% compared to static detection systems.
From raw microphone input to actionable intelligence — every step is designed for accuracy and speed.
Jetson Nano devices continuously capture audio at 16kHz with overlapping 3-second windows. USB microphones with 0.5s overlap ensure no acoustic event is missed between chunks.
On first deployment, each device runs a 30-second calibration capturing the acoustic fingerprint of its environment — traffic patterns, HVAC hum, bird calls, wind. This builds a unique spectral noise profile per device.
OCCDEC ExclusiveBefore classification, the device's noise floor is subtracted from the audio signal using STFT-based spectral subtraction. This removes environment-specific interference, dramatically improving signal clarity in noisy urban deployments.
OCCDEC ExclusiveGoogle YAMNet runs directly on the edge device — no cloud latency. Classifies 521 audio events, mapped to OCCDEC's 15 threat classes. Inference time: ~180ms per chunk on Jetson Nano.
Per-class confidence thresholds adjust automatically based on environment type. Gun shots in a quiet suburb need only 0.32 confidence; casual speech in a noisy city needs 0.65. Critical events always get the most sensitive thresholds.
OCCDEC ExclusiveEvents passing edge detection are sent to OCCDEC Cloud for multi-model verification. Five independent AI models — including xAI Grok, Claude, Gemini, and custom retrained models — vote on the classification. Majority consensus required.
5-Model ConsensusLow-confidence events and raw/enhanced disagreements are automatically queued for human review. Annotators approve, reject, or flag events using keyboard shortcuts. Ground truth labels feed back into the training pipeline.
Active Learning LoopApproved ground-truth annotations are exported (JSON/CSV) to retrain the edge model. Each retraining cycle makes every device smarter. Inter-annotator agreement metrics ensure label quality.
Continuous ImprovementRetrained models are pushed to all edge devices over-the-air. Each device re-calibrates its noise profile against the new model. The system gets measurably better with every deployment cycle.
Every OCCDEC device learns its environment. Watch how noise profiling adapts detection to different deployment contexts.
OCCDEC leads in adaptive intelligence — no other platform learns each device's unique environment.
| Feature | OCCDEC | ShotSpotter | Flock Safety | HALO Sensor |
|---|---|---|---|---|
| Per-Device Noise Learning | ✓ | ✗ | ✗ | ✗ |
| Multi-Model AI Ensemble | ✓ (5 models) | ✗ | ✗ | ✗ |
| Edge Processing | ✓ | Cloud Only | Partial | ✓ |
| Active Learning Pipeline | ✓ | ✗ | ✗ | ✗ |
| Self-Hosted Option | ✓ | SaaS Only | SaaS Only | SaaS Only |
| Adaptive Thresholds | Per-class | Fixed | Fixed | Basic |
| Human Review Workflow | ✓ | 24/7 Center | ✗ | ✗ |
| Continuous Retraining | ✓ | ✗ | ✗ | ✗ |