Overview#
A surveillance recording arrives in evidence: forty minutes of ambient noise, overlapping voices, and a critical exchange buried in the middle. Without acoustic analysis tools, an investigator might spend hours manually scrubbing through the audio, or miss the exchange entirely. The Media Audio Analysis module processes that recording in minutes, isolating speakers, enhancing degraded speech, and flagging acoustically significant moments for review.
This capability applies broadly across law enforcement interviews, body-worn camera audio, intercepted communications, and military signals intelligence. Whether the source is a covert recording, a call centre intercept, or a battlefield audio log, the module extracts structured intelligence from sound.
Key Features#
- Speech-to-text transcription with speaker diarisation and precise timestamp alignment
- Speaker identification and cross-file verification across audio evidence collections
- Environmental sound detection and classification for scene reconstruction
- Audio enhancement: noise reduction, frequency equalisation, and speech clarity improvement
- Acoustic pattern analysis for investigative intelligence and signal correlation
- Batch audio processing for large collections without manual queuing
- Multi-language transcription support for diverse operational contexts
- Audio timeline annotation with synchronised playback and marker export
Use Cases#
- Transcribing recorded calls and custody interviews with per-speaker labelling for case review
- Identifying the same speaker across separate recordings to build attribution chains
- Enhancing audio quality in surveillance recordings to recover intelligible speech from degraded sources
- Detecting environmental sounds in evidence audio, such as gunshots, vehicles, or specific machinery, for scene reconstruction
- Processing military audio logs for signals intelligence and operational debrief support
Integration#
Media Audio Analysis connects with the transcription services module, evidence management workflows, and investigation case files. Processed outputs, including transcripts, speaker profiles, and acoustic event markers, are packaged with cryptographic integrity checksums and stored in Cloudflare R2 for tamper-evident retention.
Open Standards#
- RIFF WAVE / Linear PCM: Audio evidence is ingested and processed as uncompressed 16-bit mono linear PCM within RIFF WAVE containers; the service reads raw PCM byte buffers and opens WAV streams directly for acoustic feature extraction.
- ISO 8601: All timestamps on transcripts, speaker segments, acoustic detections, and evidence event logs are serialised as ISO 8601 date-time strings, enabling unambiguous temporal ordering and cross-system interoperability.
- SHA-256 (FIPS 180-4): Evidence packages produced from processed audio recordings carry SHA-256 cryptographic integrity hashes for tamper-evident retention, with verification results returned alongside each package.
- GraphQL (June 2018 specification): The analysis API surface, including audio buffer submission, classification results, and speaker-segment queries, is exposed entirely as typed GraphQL mutations and queries via the Strawberry schema layer.
- OAuth 2.0 (RFC 6749) and JSON Web Tokens (RFC 7519): Every audio analysis operation is gated behind OAuth 2.0 bearer token authentication; access tokens are validated as JWTs before any classification or evidence export proceeds.
- WebRTC (W3C / IETF): Real-time audio streaming from body-worn cameras and live interception sources into the analysis pipeline uses WebRTC as the low-latency transport, enabling continuous acoustic classification without manual file upload.
Last Reviewed: 2026-02-05 Last Updated: 2026-04-14