Gunshot Detection & Acoustic Sensor Networks

Urban gunfire is dramatically underreported. Research from multiple metropolitan areas confirms that 80-90% of gunfire incidents produce no 911 call, meaning the vast majority of shootings go entirely undetected by law enforcement. Even when calls do come in, caller-reported locations are inaccurate by an average of 1-3 city blocks, and the delay between gunfire and dispatch notification averages 3-7 minutes—an eternity for evidence preservation and witness identification.

Acoustic classification in urban environments is technically challenging. Gunshots must be distinguished from vehicle backfires, fireworks, construction impulsive noise (nail guns, pneumatic hammers), door slams, and other transient acoustic events that share spectral characteristics with firearms discharge. False alert rates above 5% rapidly erode agency trust and dispatch willingness to respond. The system must also differentiate between caliber classes and identify shot count, as these details inform the tactical response posture.

Sensor deployment at city scale requires ruggedized hardware that operates reliably in extreme weather (-20°F to 120°F), resists vandalism, communicates over constrained network links (cellular LTE or municipal Wi-Fi), and runs for years with minimal maintenance. Each sensor must maintain precise time synchronization (sub-millisecond) for multilateration to produce accurate geolocation. Power consumption, network reliability, and mechanical durability are as critical as algorithmic accuracy.

We deploy compact acoustic sensor nodes built on ruggedized ARM-based platforms (custom hardened Raspberry Pi CM4 or NVIDIA Jetson Nano carrier boards) with MEMS microphone arrays, GPS/GNSS receivers for time synchronization, and LTE-M cellular backhaul. Sensors are mounted on utility poles, streetlight arms, and building facades at 300-500 meter intervals, providing overlapping acoustic coverage. Each node runs an edge classification model that performs initial gunshot/non-gunshot discrimination locally, transmitting only candidate gunfire events to the central processing cluster to minimize bandwidth consumption.

The central detection engine receives candidate events from multiple sensors and performs multilateration using Time Difference of Arrival (TDoA) calculations. With timestamps synchronized to sub-microsecond precision via GPS PPS (Pulse Per Second) signals, the system triangulates the shot origin to within 10-15 meters in dense sensor coverage areas. A secondary classification model running on GPU infrastructure analyzes the full-resolution audio waveform from the nearest sensors, determining caliber class (handgun, rifle, shotgun), shot count, and cadence (single shots, semi-automatic, automatic fire), enriching the detection event before alert dispatch.

Detection events are published to the agency's CAD system via standard interfaces and simultaneously displayed on the RTCC dashboard (integrated with our cap-04 RTCC platform). The GIS display shows the shot location with a confidence ellipse, nearest camera feeds are automatically cued, and patrol units are alerted with precise GPS coordinates for navigation. Post-event, the system stores all raw audio segments for evidentiary use, tagged with chain-of-custody metadata including sensor calibration certificates, time synchronization verification, and detection algorithm version.

Each sensor node captures audio at 48 kHz / 24-bit via a dual MEMS microphone array (ICS-43434 or equivalent) with a dynamic range of 29-120 dBA SPL. The edge classification model is a 1D convolutional neural network operating on 100ms Mel-spectrogram frames, trained on a dataset of 200,000+ labeled urban acoustic events. The model runs in TensorFlow Lite on the ARM Cortex-A72 processor, consuming under 500mW during continuous monitoring. When a candidate gunshot is detected (confidence > 0.85), the node transmits the detection timestamp, raw audio buffer (2 seconds pre-trigger, 3 seconds post-trigger), GPS coordinates, and sensor health telemetry via MQTT over LTE-M to the central processing cluster.

Multilateration uses a hyperbolic positioning algorithm that solves an overdetermined system of TDoA equations via iterative least-squares optimization. With a minimum of 3 detecting sensors (typically 4-6 sensors detect a single event in urban deployments), the algorithm produces a 2D position estimate and a confidence ellipse derived from the geometric dilution of precision (GDOP) of the detecting sensor constellation. Time synchronization relies on GPS PPS signals disciplining a local TCXO oscillator to maintain sub-microsecond accuracy during GPS outages of up to 30 minutes, with NTP over the LTE backhaul as a tertiary time source.

The secondary classification stage runs on a centralized GPU server using a deeper CNN architecture (ResNet-18 backbone) that analyzes the full-resolution audio from the 3 nearest sensors. This model classifies shots into caliber categories (small-caliber handgun, large-caliber handgun, intermediate rifle, full-power rifle, shotgun) with 89% accuracy, and estimates round count with 95% accuracy for events with up to 10 rounds. The audio spectral signature is also compared against a database of known weapon profiles for investigative lead generation. All detection metadata is formatted per the National Institute of Justice (NIJ) gunshot detection interoperability specification and published to the CAD system via NIEM 5.0 event messages.