How to monitor music airplay across radio stations using an API

Labels, distributors, and rights organizations leave royalties unclaimed every year, and the cause is usually the same: incomplete airplay data. Radio plays songs constantly, across thousands of stations, and tracking that activity by hand does not scale. A music recognition API makes it possible to monitor many stations at once, automatically, in real time.

Why traditional airplay monitoring falls short

Radio never stops. Stations broadcast 24/7 across hundreds of markets, and a single major-market station can play several hundred songs a day. Multiply that across countries, languages, and formats and the data problem becomes unmanageable.

Traditional monitoring leans on station-provided playlists, listener reports, or third-party services with limited reach. These methods miss a lot, especially from:

Independent and college radio stations
International markets
Streaming radio platforms
Overnight and weekend programming

Performance rights organizations such as ASCAP and BMI distribute royalties based on airplay, and when airplay goes untracked, artists and labels do not see their full share.

How recognition APIs enable scalable monitoring

A music recognition API solves the scale problem by identifying songs from audio streams automatically and in real time. Rather than depending on station cooperation or manual logging, the system listens to radio feeds continuously and returns structured metadata for each track it detects.

The underlying technology is audio fingerprinting: each recording has a digital signature, and when audio from a stream matches one, the API returns the title, artist, album, label, and more within seconds. AudD recognizes against a catalog of 160 million songs.

The pieces of API-based monitoring

Audio ingestion. AudD accepts live stream URLs directly — HLS, Icecast, DASH, and m3u/m3u8 — plus shortcuts for Twitch and YouTube live, so you can monitor FM/AM rebroadcasts, internet radio, and streaming platforms from one integration.
Real-time processing. Stream recognition runs continuously and delivers results as songs play, which is what makes live dashboards and immediate alerts possible.
Metadata enrichment. Beyond identification, results carry label information, and — on a Startup plan or higher — the ISRC and a match score, all of which matter for royalty work. Provider links (Apple Music, Spotify, Deezer, and more) are available on request.
Scalable capacity. Stream monitoring is billed per concurrent stream, so you scale by adding sources, and global monitoring stays economically viable.

Building your airplay monitoring system

Step 1: choose your audio sources

Before writing code, identify which stations and markets matter for your catalog:

Geographic priorities — where do your artists have the strongest presence or the most growth potential?
Format relevance — which stations match your genres and audience?
Commercial value — major markets and high-listener stations drive the biggest royalty impact.
Technical accessibility — can you reliably reach a clean stream URL?

Many stations publish public streaming URLs; others require partnerships or specialized services to access their feeds.

Step 2: register streams with the API

AudD’s streams surface is built exactly for this. You register a source once and receive results as songs play, rather than polling. The flow uses stream methods on api.audd.io:

setCallbackUrl — register where results should be POSTed (set once).
addStream — add a source by url plus a radio_id integer you choose to identify the station.
Receive results via callbacks (AudD POSTs to your URL) or longpoll (you pull events). Manage sources with getStreams, setStreamUrl, and deleteStream.

from audd import AudD

audd = AudD("your-api-token")  # get a token at dashboard.audd.io

audd.streams.set_callback_url("https://yourapp.example/audd-callback")
audd.streams.add(url="https://stream.example/community-fm", radio_id=4012)

By default a result callback fires after a song finishes and includes the total played time; set callbacks="before" on addStream to be notified the instant a song starts (without the played-time total). Stream notifications also flag problems with codes 650 (cannot connect to the stream) and 651 (only white noise, no music) — wire these into your alerting so a dead feed does not look like a quiet one.

Step 3: design your data architecture

Airplay monitoring generates a lot of data; organize it from the start. Useful fields include:

Temporal — exact play time, duration, and date.
Station — call sign, market, format.
Song — title, artist, album, label, and (Startup plan or higher) ISRC.
Context — show or program information where you have it.

Design for both real-time alerting and historical analysis. Time-series databases handle the volume and query patterns of airplay data well.

For custom-catalog matches, the result carries the integer audio_id you assigned when you uploaded the track — useful when you are tracking your own releases against a catalog you control. Any field the API returns that the SDK does not model as a typed property is available on the result’s model_extra map in Python (extras in Node).

Step 4: build monitoring and alerting

Real-time notifications are especially valuable during active campaigns and new releases. Useful alerts include:

First-time airplay of a new release
Unusual spikes in play frequency
Airplay appearing in new markets or formats
Competitor activity and emerging trends

Dashboard visualizations help stakeholders read airplay patterns and geographic distribution without digging through raw rows.

Advanced strategies

Multi-market campaign tracking. For artists with international reach, running monitoring across regions reveals how a promotional push performs market by market, and where organic growth happens without one. Cross-referencing airplay with streaming, social, and sales data gives a fuller picture of what radio exposure actually drives.

Competitive intelligence. Tracking competitor releases alongside your own catalog surfaces which songs are gaining traction and what is trending — input for A&R, marketing timing, and how you approach radio programmers and playlist curators.

Rights and royalty workflows. Structured, timestamped airplay records with ISRC and label data feed directly into the reporting you submit to performance rights organizations and publishing administrators, replacing slow, error-prone manual logging.

Overcoming common challenges

Audio quality and accuracy. Radio streams carry compression, ads, and DJ voiceovers, all of which complicate recognition. Test your target sources before full deployment; some feeds may need cleaner ingestion. The fingerprinting approach is built to recognize through noise, and a per-match score (Startup plan or higher) lets you set a confidence floor.

Scale and cost. Stream monitoring bills per concurrent stream, so cost scales with how many sources you watch at once. Prioritize stations and markets by business impact rather than trying to cover everything, and concentrate intensive monitoring on high-value time slots.

Legal and compliance. Monitoring practices must comply with broadcasting regulations and copyright law in every market you operate in. Some regions require explicit permission to monitor a stream; others have rules on data retention. For international operations, work with legal counsel before scaling.

Measuring success

The outcomes from effective monitoring are concrete:

Royalty recovery — performance royalties collected from airplay that previously went untracked.
Campaign effectiveness — the relationship between promotional activity and airplay results.
Market expansion — data-driven decisions about geographic and demographic targeting.
Competitive positioning — share within specific formats and regions.

ROI comes from weighing monitoring cost against additional royalty collection, better campaign performance, and the value of reliable data.

Getting started

The practical approach is a focused pilot: target your most important markets and releases, measure the added royalty collection, then expand. Stream monitoring’s per-concurrent-stream billing makes it easy to start small and grow.

AudD provides the technical foundation — 160 million songs and real-time stream recognition built for continuous, global tracking. Get a token at dashboard.audd.io and start with the recipe below.

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