Audio Classification & Emergency Detection

Overview#

A 911 dispatcher receives a call from a caller who says nothing. There is silence, then what might be a struggle. In the background, a rapid series of sharp sounds. The Audio Classification module identifies those sounds as gunshots within milliseconds, assigns a risk score of 94, and flags the call for immediate P1 escalation, all before the dispatcher has finished listening. That automated detection can be the difference between a 60-second response and a two-minute response.

The Audio domain provides real-time acoustic analysis for emergency response operations. It detects critical audio events in emergency call recordings and live audio streams, enabling faster response to life-threatening situations through automated classification and risk scoring.

Key Features#

• Gunshot Detection: Identifies gunshot acoustic signatures in audio streams with confidence scoring.
• Scream and Distress Detection: Recognises high-pitch distress vocalisations to flag potential assault or emergency situations.
• Explosion Detection: Detects explosion signatures indicating immediate mass-casualty threats.
• Siren Detection: Identifies emergency vehicle siren patterns for situational context.
• Risk Score Aggregation: Produces a 0-100 risk score weighted by event severity for call prioritisation.
• Confidence Scoring: Provides normalised confidence values (0.0-1.0) for each detected acoustic event.
• Multi-Event Classification: Analyses audio for multiple simultaneous event types in a single pass.
• Low-Latency Processing: Delivers results quickly enough to support real-time emergency triage.
• Body-Worn Camera Support: Analyses live audio from officer body-worn cameras for safety monitoring.
• AI-Powered Audio Analysis: Complements signal-based detection with AI transcription and sentiment analysis for deeper insight.

Use Cases#

Emergency call centres use audio classification to automatically flag high-risk 911 calls containing gunshots or screams, enabling dispatchers to prioritise life-threatening situations immediately rather than processing calls in arrival order.

Law enforcement agencies monitor body-worn camera audio streams in real time during active incidents to enhance officer safety through automated threat detection, alerting supervisors when acoustic signatures indicate escalating violence.

Multi-modal call prioritisation systems combine audio risk scores with text sentiment analysis and keyword detection to route calls to the appropriate priority level, giving dispatchers a composite picture of each caller's situation.

Automated alerting systems use acoustic signature detection to trigger dispatcher notifications when critical audio events are identified in active calls, ensuring no high-risk event goes unnoticed during high-volume periods.

Integration#

The Audio domain works with the PSAP operations module for emergency call triage, the sentiment analysis module for combined text and audio risk assessment, the body-worn camera module for real-time officer monitoring, and the prioritisation engine for multi-modal call routing.

Open Standards#

• Linear PCM / ITU-T G.711: The audio analysis engine ingests raw 16-bit mono PCM buffers (normalised from signed 16-bit integer range), the encoding form defined by ITU-T G.711 and used throughout telephony and PSAP call recording pipelines.
• NENA i3 (NENA-STA-010 / NENA-STA-021): Audio classification results feed the NG9-1-1 call-handling pipeline that implements NENA i3, including EIDO incident components and RFC 7852 Additional Data blocks for structured call metadata.
• IETF RFC 7865 / SIPREC: Call audio reaches the classifier via a SIPREC recording layer (RFC 7865 session metadata, RFC 6873 stream extensions) that captures SIP-based emergency calls from PSAP infrastructure.
• IETF RFC 4119 / PIDF-LO: Caller location context delivered alongside audio is parsed from Presence Information Data Format Location Object (PIDF-LO) XML carried in SIP INVITE messages, enabling geo-aware call prioritisation.
• APCO Project 25 (P25) / ETSI DMR / ETSI TETRA: The radio talkgroup audio monitor that feeds the classifier supports P25 (APCO-25), DMR, and TETRA encoded radio transmissions from dispatch and field units.
• WGS-84 (EPSG:4326): Sensor positions used in the companion acoustic triangulation pipeline, which supplies corroborating gunshot location data, are expressed in WGS-84 geodetic coordinates with haversine and local ENU conversions.
• GraphQL (June 2018 specification): All audio analysis mutations and results are exposed over a GraphQL API (Strawberry schema), including the analyzeAudioBuffer mutation that accepts Base64-encoded PCM payloads.

Last Reviewed: 2026-02-23 Last Updated: 2026-04-14