Live · 297 channels monitored 24/7
The only unified detection layer for music reporting, rights verification, and business intelligence.
Combine the precision of watermarking with the scale and flexibility of fingerprinting to power both trusted reporting and next-generation music intelligence.
Industry first
Two detection methods.
One unified result.
Where fingerprinting fails — heavy post-processing, re-recordings, degraded signal — watermarks succeed, and vice versa. No other system runs both. The result is a detection rate single-method monitors can’t match.
Digital watermarking
Inaudible Digimarc payload embedded at ingest. Survives the broadcast chain — compression, EQ, EBU R128 normalization, time-stretch — and reads back even when audio quality degrades.
Neural fingerprinting
Proprietary AI-based acoustic fingerprinting. Neural audio embeddings and vector search across 2.9B reference vectors. Detects unwatermarked content and dramatically outperforms legacy fingerprinting.
The union effect
Coverage neither method achieves alone.
An AI music event detector runs in parallel — classifying music vs. speech vs. silence — so segment analysis is precise and false positives drop. Results unify, deduplicate, and resolve against the catalog.
WM ∪ NFP ∪ AI-MED
→ unified track-level dataset
Processing pipeline
From broadcast to a track-level match.
Six stages, all running continuously across every monitored channel. GPU-accelerated, AWS-based, ~15,000 recordings ingested per day.
Capture
Broadcast audio captured continuously across 297+ TV and radio channels.
L/R split
Audio split to left and right channels for independent inspection.
Cross-mix
L and R combine into a stereo sum mix and a mono difference mix — two passes per recording.
Triple-process
Each mix runs through Digimarc watermark read, NFP vector search, and AI music event detection in parallel.
Detection data
Results from all six pipes unify, deduplicate, and resolve against the 16M-track reference catalog.
Distribute
The unified dataset branches into cue sheets, the Beatwatch API, and analytics — wherever your team needs it.
By the numbers
Scale that makes 100% capture possible.
Not a panel. Not a sample. Every music cue across every monitored broadcast, catalogued and cross-referenced.
- 80+
- 217+
- 15k
- 16M
- 2.9B
Beatwatch
The analytics platform on top of the data.
Live detections, dimensioned data, drill-down to the recording. Query by channel, show, episode, promo, segment, daypart, brand, genre — all cross-referenced.
beatwatch.sourceaudio.com / detect / live
v2.4.1
Live detections — past 60 minutes
Streaming
- 2,840
- Detections / hr
- 1,612
- Unique tracks
- 184
- Programs touched
- 96.4%
- Avg confidence
Detection frequency — last 7 days × 24 hours
less more
Match feed
7 of 2,840
- 07:57:00 Sleeper · June Pines AMC NFP 0.87
- 07:56:53 Glasshouse · Vega Marlow ESPN NFP 0.94
- 07:56:46 Paper Mountains · Lowell Vinen CBS WM 0.96
- 07:56:39 Slow Burn · Tessera Sound TNT NFP 0.91
- 07:56:32 Glasshouse · Vega Marlow TNT WM 0.94
- 07:56:25 Black Coast · Solane NBC WM 0.98
- 07:56:18 Midnight Drive · Atlas & Sage WFUV WM 0.90
Integrations & API
Detection data, in your stack.
Start with automated cue sheets — the baseline every client gets. From there, layer on the API, webhooks, warehouse exports, or a custom LES integration as your team’s needs grow.
Automated cue sheets
Every detection rolls up into broadcast-ready cue sheets. Download as PDF or XLSX, or pull them straight from the API — no manual logging, no spreadsheet wrangling.
Beatwatch API
REST and GraphQL endpoints for every detection, station, program, and reference track. OAuth2 with scoped tokens.
Webhooks
Subscribe to detection events filtered by channel, show, track, or rights-holder. At-least-once delivery with idempotency keys.
Bulk export
Daily Parquet and CSV exports to your S3, GCS, or Azure bucket. Pre-joined to your catalog or to SourceAudio metadata.
LES / process integration
Custom ingest pipelines for clients with closed delivery systems. Fox Sports runs a tailored Detect pipeline today.
CLI and SDK
Python and TypeScript SDKs, a detect CLI for one-off queries, and Jupyter-friendly DataFrame helpers.
See every placement.
Know every use.
For questions or a personal demo, get in touch.