first relationship with music listening began at 6, rotating through the albums within the lounge’s Onkyo 6-disc player. , , . There was at all times one song I kept rewinding to, though I didn’t know its name. 10 years on, moments of the song returned to memory. I searched through forums, ‘, ‘’, on the lookout for years with no success. Then, sooner or later at university, I used to be in my friend Pegler’s dorm room when he played it:
That long search taught me how necessary it’s to have the option to seek out the music you like.
Before streaming and smart assistants, music discovery relied on memory, luck, or a friend with good music taste. That one catchy chorus may very well be lost to the ether.
Then got here a music-lover’s miracle.
Just a few seconds of sound. A button press. And a reputation in your screen.
Shazam made music recognisable.
The Origin: 2580
Shazam launched in 2002, long before apps were a thing. Back then it worked like this:
You’d dial 2580# in your mobile (UK only).
Hold your phone as much as the speaker.
…Wait in silence…
And receive a SMS telling you the name of the song.
It felt like magic. The founding team, Chris Barton, Philip Inghelbrecht, Avery Wang, and Dhiraj Mukherjee, spent years constructing that illusion.
To construct its first database, Shazam hired 30 young employees to run 18-hour shifts, manually loading 100,000 CDs into computers and using custom software. Because CD’s don’t contain metadata that they had to type the names of the songs manually, referring to the CD sleeve, to eventually create the corporate’s first million audio fingerprints — a painstaking process that took months.
In an era before smartphones or apps, when Nokia’s and Blackberry’s couldn’t handle the processing or memory demands, Shazam had to remain alive long enough for the technology to catch as much as their idea. This was a lesson in market timing.
This post is about what happens within the moment between the faucet and the title, the signal processing, hashing, indexing, and pattern matching that lets Shazam hear what you’ll be able to’t quite name.
The Algorithm: Audio Fingerprinting
In 2003, Shazam co-founder Avery Wang published the blueprint for an algorithm that also powers the app today. The paper’s central idea: If humans can understand music by superimposing layers of sound, a machine could do it too.
Let’s walk through how Shazam breaks sound all the way down to something a machine can recognise immediately.
1. Capturing Audio Sample
While you hit the Shazam button, the app records a 5–10 second snippet of the audio around you. That is long enough to discover most songs, though we’ve all waited minutes holding our phones within the air (or hiding in our pockets) for the ID.
But Shazam doesn’t store that recording. As a substitute, it reduces it to something far smaller and smarter: a fingerprint.
2. Generating the Spectrogram
Before Shazam can recognise a song, it needs to grasp what frequencies are within the sound and after they occur. To do that, it uses a mathematical tool called the Fast Fourier Transform (FFT).
The FFT breaks an audio signal into its component frequencies, revealing which notes or tones make up the sound at any moment.
Why it matters: Waveforms are fragile, sensitive to noise, pitch changes, and device compression. But frequency relationships over time remain stable. That’s the gold.
In the event you studied Mathematics at Uni, you’ll remember the struggles of learning the Discrete Fourier Transform process.Fast Fourier Transform (FFT) is a more efficient version that lets us decompose a posh signal into its frequency components, like hearing all of the notes in a chord.
Music isn’t static. Notes and harmonics change over time. So Shazam doesn’t just run FFT once, it runs it repeatedly over small, overlapping windows of the signal. This process is referred to as the Short-Time Fourier Transform (STFT) and forms the idea of the spectrogram.
The resulting spectrogram is a metamorphosis of sound from the amplitude-time domain (waveform) into the frequency-time domain.
Consider this as turning a messy audio waveform right into a musical heatmap.
As a substitute of showing how loud the sound is, a spectrogram shows what frequencies are present at what times.
A spectrogram moves evaluation from the amplitude-time domain to frequency-time domain. It displays time on the horizontal axis, frequency on the vertical axis, and uses brightness to point the amplitude (or volume) of every frequency at each moment. This lets you see not only which frequencies are present, but additionally how their intensity evolves, making it possible to discover patterns, transient events, or changes within the signal that usually are not visible in an ordinary time-domain waveform.
Spectrograms are widely utilized in fields similar to audio evaluation, speech processing, seismology, and music, providing a robust tool for understanding the temporal and spectral characteristics of signals.
3. From Spectrogram to Constellation Map
Spectrograms are dense and contain an excessive amount of data to match across hundreds of thousands of songs. Shazam filters out low-intensity frequencies, leaving just the loudest peaks.
This creates a constellation map, a visible scatterplot of standout frequencies over time, just like sheet music, even though it jogs my memory of a mechanical music-box.
4. Creating the Audio Fingerprint
Now comes the magic, turning points right into a signature.
Shazam takes each anchor point (a dominant peak) and pairs it with goal peaks in a small time window ahead — forming a connection that encodes each frequency pair and timing difference.
Each of those becomes a hash tuple:
(anchor_frequency, target_frequency, time_delta)
What’s a Hash?
A hash is the output of a mathematical function, called a hash function, that transforms input data right into a fixed-length string of numbers and/or characters. It’s a way of turning complex data right into a short, unique identifier.
Hashing is widely utilized in computer science and cryptography, especially for tasks like data lookup, verification, and indexing.
For Shazam, a typical hash is 32 bits long, and it be structured like this:
- 10 bits for the anchor frequency
- 10 bits for the goal frequency
- 12 bits for the time delta between them
This tiny fingerprint captures the connection between two sound peaks and the way far apart they’re in time, and is powerful enough to discover the song and sufficiently small to transmit quickly, even on low-bandwidth connections.
5. Matching Against the Database
Once Shazam creates a fingerprint out of your snippet, it must quickly discover a match in its database containing hundreds of thousands of songs.
Although Shazam has no idea where within the song your clip got here from — intro, verse, chorus, bridge — doesn’t matter, it looks for relative timing between hash pairs. This makes the system robust to time offsets within the input audio.
Shazam compares your recording’s hashes against its database and identifies the song with the best variety of matches, the fingerprint that best lines up along with your sample, even when it’s not an actual match resulting from background noise.
The way it Searches So Fast
To make this lightning-fast, Shazam uses a hashmap, a knowledge structure that enables for near-instant lookup.
A hashmap can discover a match in O(1) time, which means the lookup time stays constant, even when there are hundreds of thousands of entries.
In contrast, a sorted index (like B-tree on disk) takes O(log n) time, which grows slowly because the database grows.
This balance of time and space complexity is referred to as Big O Notation, theory I’m not prepared of bothered to show. Please confer with a Computer Scientist.
6. Scaling the System
To keep up this speed at global scale, Shazam does greater than just use fast data structures, it optimises how and where the info lives:
- Shards the database — dividing it by time range, hash prefix, or geography
- Keeps hot shards in memory (RAM) for fast access
- Offloads colder data to disk, which is slower but cheaper to store
- Distributes the system by region (e.g., US East, Europe, Asia ) so recognition is fast regardless of where you might be
This design supports 23,000+ recognitions per minute, even at global scale.
Impact & Future Applications
The apparent application is music discovery in your phone, but there’s one other major application of Shazam’s process.
Shazam facilitates Market Insights. Each time a user tags a song, Shazam collects anonymised, geo-temporal metadata (where, when, and the way often a song is being ID’d.)
Labels, artists, and promoters use this to:
- Spot breakout tracks before they hit the charts.
- Discover regional trends (a remix gaining traction in Tokyo before LA).
- Guide marketing spend based on organic attraction.
Unlike Spotify, which uses user listening behaviour to refine recommendations, Shazam provides real-time data on songs people actively discover, offering the music industry early insights into emerging trends and popular tracks.
What Spotify Hears Before You Do
medium.com
On December 2017, Apple bought Shazam for a reported $400 million. Apple reportedly uses Shazam’s data to reinforce Apple Music’s suggestion engine, and record labels now monitor Shazam trends like they used to watch .
In the longer term, there is anticipated evolution in areas like:
- Visual Shazam: Already piloted, point you camera at an object or artwork to discover it, useful for an Augmented Reality future.
- Concert Mode: Discover songs live during gigs and sync to a real-time setlist.
- Hyper-local trends: Surface what’s trending ‘on this street’ or ‘on this venue’, expanding community-shared music taste.
- Generative AI integration: Pair audio snippets with lyric generation, remix suggestions, or visual accompaniment.
Outro: The Algorithm That Endures
In a world of ever-shifting tech stacks, it’s rare for an algorithm to remain relevant for over 20 years.
But Shazam’s fingerprinting method hasn’t just endured, it’s scaled, evolved, and grow to be a blueprint for audio recognition systems across industries.
The magic isn’t just that Shazam can name a song. It’s the way it does it, turning messy sound into elegant math, and doing it reliably, immediately, and globally.
So next time you’re in a loud, trashy bar holding your phone as much as the speaker playing just remember: behind that tap is a lovely stack of signal processing, hashing, and search, designed so well it barely had to alter.
