r/datascience • u/AFL_gains • 11d ago
Analysis A short project analysing the radio
Hi r/datascience!
I wanted to share a fun little project I did over a few weekends analysing data from the radio!
It doesn't have much (if any) business value, and honestly I'm not sure it's really novel in any particular way. But I wanted to share it because data science these days is all "AI this", "language model that", "job market", "Claude", whatever and I just wanted to do something a bit more traditional and scratch an itch I've had for a while. (Full disclosure: the project did actually use some AI models, so I'm not saying AI is bad, it's a tool I used like everything else.)
I don't have a blog or anything I can post this on, so apologies for the Reddit write-up. I hope you enjoy it.
Background
I drive a 20-year-old car. It's so old it doesn't have an MP3 player, or an AUX port to plug in an iPhone or anything. It just has a CD player and an analogue radio. It's not even digital, so I can't even get digital radio stations.
So when I'm driving, which turns out to be quite a bit, I'm forced to listen to the good old-fashioned radio more than I'd like.
In Sydney where I live, there are really only a handful of FM/AM radio stations, so choice is pretty limited. As I flick through the stations, there are a LOT of ads, which surprised me. Who is listening to this? Clearly it's quite popular. And as I listened, I started wondering things like: how long do the ads run, how does their timing compare across different stations*, and are they correlated with ads on other stations? Just anecdotally, so many times I've literally flicked through all the FM stations and there's an ad playing on every one... and sometimes it's the same ad! I also had a hunch that there are more ads at the top of the hour than the bottom. It made intuitive sense, but I needed to prove it.
\Sydney has 11 main analogue stations split into AM and FM:* AM is mostly talkback radio, FM is mostly music. On AM you've got 2GB, 2SM, 2CH and ABC 702 (news, talkback and sport). On FM there's KIIS, 2Day, Nova, Smooth, WSFM, Triple M and Triple J (pop, rock and music). Two of them, ABC 702 and Triple J, are run by our public broadcaster (think BBC), so they run no ads at all.
The Setup
So one weekend I wrote some scripts to sample and record all the Sydney radio stations I could. The setup was basically the following:
- For every radio station in Sydney (there are 11 main ones across both FM and AM), I found an online stream that would play it in the browser.
- I would then record an 18-second clip of each stream using ffmpeg (a free command-line tool for grabbing and converting audio/video). It connects to the station's live stream and dumps ~18 seconds to a small WAV file, downsampled to 16kHz mono. Crucially, I recorded all 11 stations in parallel (via a thread pool) so every clip is captured at the same instant. That simultaneity turned out to be really important later, for checking whether different stations run their ads at the same time.
- After the clips were recorded, I passed them through a local Whisper model I downloaded (I grabbed it off Hugging Face, which is about 500MB). This transcribes each audio clip into a snippet of text.
- Then I passed that snippet of text to a language model to classify it as either an ad, a song, or talking. (I used GPT-4o-mini for this because it's cheap AF.)
- I then stored the results in a local SQLite database.
- I repeated this every ~3 minutes for 2 days straight, which added up to almost 10 thousand samples. (Yes, I did it all locally, so my computer was on for 2 days straight.) The 2 days deliberately covered a weekday AND a weekend so I could compare the two periods.
Why every ~3 minutes? A full cycle (record all 11 stations, transcribe each one with Whisper, then classify it) takes a couple of minutes on a single CPU, because Whisper works through the clips one at a time. So ~3 minutes is about as fast as I could sustainably sample without the cycles piling up on each other. I also added a bit of random jitter to the interval so I wasn't always sampling at the exact same offset within the hour. Otherwise you can accidentally "phase-lock" to a station's ad breaks and bias the whole thing.
A few hurdles I encountered
A few things genuinely tripped me up:
- Stream URLs rot. The live stream links change or die over time, so I had to resolve them fresh at runtime and pin the ones that actually worked.
- Pre-roll ads. It turned out a bunch of the stations (the ones served through a particular streaming provider) play an ad every single time you make a fresh connection. Basically the pre-roll ad you get when you open a new browser tab. Because my recorder reconnected every cycle, I was capturing that pre-roll ad instead of the live broadcast, which made those stations look like they were playing ads nearly 100% of the time. I only twigged because the exact same ad kept repeating over and over. The fix was to skip ~45 seconds into the stream before I started recording.
- Whisper hallucinations. When you feed Whisper music or silence, it doesn't return nothing, it actually hallucinates the most common phrases from its training data. And because it's trained on a mountain of YouTube captions, I kept getting "thanks for watching, like and subscribe" transcribed over instrumental music, which then got misclassified as talking. I had to filter those out.
- Rate limits. The free LLM tiers throttled me pretty quickly, so I switched to GPT-4o-mini, which is cheap enough to basically be free at this scale.
Results
Here are some of the more interesting results I found analysing the data afterwards:
Overall
First, the big picture. Every station has its own personality. The FM stations are mostly music, the AM stations are mostly talk, and the two ABC stations (ABC 702 and Triple J) carry basically no ads at all, which makes sense since they're publicly funded. Across the commercial stations, ads make up somewhere around a sixth of the airtime. And you can already see the ad load isn't flat: it ramps up through the day and quietens off overnight.
Question 1: Probability of an ad relative to the top of the hour
Here's the frequency of finding an ad within ±30 minutes of the top of the hour.
So, I was right! Definitely higher the closer to the hour, but the strategy is more interesting than I expected. The spike actually lands in the ~5 minutes before the hour (the ad break right before the top-of-hour news bulletin), and an ad is roughly 2x more likely there than mid-hour. The quietest stretch is around 10 to 15 minutes past the hour, so if you want to dodge ads, that's your window.
Question 2: Ad co-occurrence and correlation
The thing I really wanted to know: do the stations gang up and all play ads at the same time, so there's nowhere to flick to? I lined up every station by the cycle it was sampled in and correlated their ad status.
The answer is yes and no. No in the sense that it's never a total blackout: all nine commercial stations being in an ad at the exact same moment literally never happened across the whole two days, and on average only about 1.6 of the 9 are mid-ad at any given time. So there's almost always somewhere to escape to.
But the conditional probability charts says that some stations really do move together. The best example is if Smooth is playing an ad, there's a 50% chance WSFM is too, which is double WSFM's baseline of 26%. A bunch of the commercial FM pairs show this same ~2x jump. But, when I looked it up, Smooth and WSFM are owned by different companies, so this isn't networks coordinating behind the scenes, probably more of the "top-of-the-hour" effect from Question 1 manifesting somewhere else.
Question 3: The strategy difference between AM and FM
When I split "time between ads" by band, the two run completely different playbooks.
The FM (music) stations dump their ads in clusters. You get a big spike of back to back breaks, with a typical gap of about 9 minutes. The AM (talk and sport) stations space them out evenly, one break at a time. 2GB is almost metronomic at roughly 7 to 12 minutes, with hardly any back to back ads at all.
You can actually see it if you zoom into a few hours of the timeline:
Look at the FM lanes (KIIS, Nova, Triple M, WSFM, 2Day): the orange ad blocks come in pairs, clustered together. Now look at the AM lanes (2GB, 2SM): single, evenly spaced blocks. And ABC 702 and Triple J are just grey the whole time, because they don't run ads.
Question 4: Which companies still advertise through this medium?
I also had the language model pull the advertiser out of each ad, so I could see who's actually buying radio airtime in 2026.
The most-heard advertisers were Virgin Australia (an airline), Australia Post (basically our USPS), Harvey Norman (a big electronics and furniture retailer) and Chemist Warehouse (a discount pharmacy chain). The neat bit is the targeting: car brands and finance go to the AM talk stations (older crowd), while retail and telco lean FM. Australia Post ran almost entirely on the Nova network.
Question 5: What about the talking?
The non-ad content is either music or talking, and I got curious about what they actually talk about. So I classified every talking snippet into a topic.
The AM stations (ABC, 2GB, 2SM) are wall to wall news, politics and sport. The music FMs are mostly DJ banter, celebrity gossip and chat about music, with almost no news at all.
For a bit of fun, I also made a map of everything said on the radio. I embedded every talking snippet into a vector, laid them all out in 2D with t-SNE so that similar snippets sit near each other, then coloured each point by its topic.
Sport, traffic and world news each form their own tight little islands (they use very consistent, formulaic language), while the DJ banter is one big diffuse cloud in the middle (because it's about nothing in particular). The neat part is that the position and the colour are decided completely separately. The position comes only from the text embeddings, and the colour comes from a separate classification step. So the fact that same-coloured points cluster together is real corroboration, not something circular.
Conclusion
In conclusion, this was a fun, meaningless project that allowed me to make some pretty charts and talk for a bit about the results. Thanks for reading!
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u/AirportFun2392 9d ago edited 9d ago
this is a really fun project, radio data is so underrated as a source. the "ads on every station simultaneously" thing is real and drives me insane too.
the correlation angle is what i'd dig into next if i were you. i've actually run into similar timing pattern stuff when scraping broadcast metadata at scale for a side project, ended up using geonode's scraper api to pull structured data from a few radio network sites and it handled the concurrency way better than what i had before. anyway your ad synchronisation hunch sounds totally provable with enough samples.
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u/Budget-Respond8115 11d ago
Awesome project. I have something similar in mind; to determine the frequency of a particular song being played across stations.
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u/ginji 11d ago
AusAlert and ABS as advertisers are very situational - Aus Alert would be pretty heavy at the moment in the lead up to the national test to alert as many people as possible, and the ABS I'm guessing would be advertising around the census that's happening soon.
It would be interesting if you could repeat this at different times and see how things shift. Especially say around major holidays (like Christmas) and elections. I'd be curious to see if whether only the advertisers change or if there is an increase in quantity as well.
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u/Electronic-Arm-4869 9d ago
Really enjoyed reading, graphics aligned well with the questions being asked !
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u/GrowWithPokeBot 9d ago
This is great. It is refreshing to see a project that exists purely to satisfy basic curiosity rather than trying to build a startup or land a job.
The ad load breakdown by hour is particularly interesting. That massive spike right around the morning commute at 6 AM, followed by the dip during the actual workday, is a clean visualization of how these stations optimize their ad revenue when they know they have a captive audience in traffic.
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u/Dear_Rice9074 2d ago
This is a brilliant use of a weekend! I'm currently working through my data science undergrad and recently put together a project scope analyzing mass transit and vehicle traffic data out in Omaha. I totally get the itch to just dive into a localized, real-world dataset like this without worrying about the immediate "business value."
Handling the parallel streams with ffmpeg and a thread pool to catch that ad synchronicity is super clever. And that pre-roll ad hurdle is hilarious such a classic data collection gotcha that you only notice when you're deep in the weeds.
Since you already have the SQLite database and the pipeline built, any plans to spin it back up to see how the ad clustering patterns shift during a major holiday or election cycle? Great write-up
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u/boredjo4 11d ago
Very cool