Experience our new AI powered Web and Mobile app building platform 🚀rocket.new. Build any app with simple prompts- no code required.
Sign in
Sign in
Use prompts to turn ideas into music app screens in minutes
Frameworks we support
Integrations we support
Heard a song and needed its name fast? Apps like Shazam solve that in seconds. How do they do it? Learn how these apps work and how to build your own from scratch.
Caught a tune in a café and couldn’t place the name?
That moment sparks instant curiosity.
Apps like Shazam, Hey SoundHound, and Apple Music help millions figure it out within seconds.
But how do these apps match a short audio clip to the exact song so fast?
This article breaks down the process of building an app like Shazam. You’ll learn how audio fingerprinting works, how to design smooth user flows, and how to set up the backend to support real-time recognition.
This is where you start if you aim to create a standout music recognition app.
Understand how Shazam identifies songs using audio fingerprints
Learn tech stack options like ShazamKit, ACRCloud, and custom pipelines
Explore features from real-time recognition to integration with Spotify and Apple Music
Discover how to design smooth, user-friendly mobile interfaces
Get insights into monetization and post-launch scaling strategies
Before you code a single line, you must deeply understand the music app ecosystem.
Compare your concept with existing players like:
| App Name | Notable Feature | Monetization |
|---|---|---|
| Shazam | Audio fingerprinting + Apple Music integration | Free + Apple ecosystem |
| Hey SoundHound | Voice activation ("Say Hey SoundHound") | Ads + Premium |
| Musixmatch | Real-time synced lyrics + streaming sync | Subscription |
Who are your target users: casual music lovers, DJs, or social media influencers?
How will you monetize: ads, Spotify subscription affiliate links, or paid premium features?
What will differentiate your app from Shazam?
At the heart of music identification is a method called audio fingerprinting.
Record a short audio clip (5–10 seconds of the song playing).
Use FFT (Fast Fourier Transform) to create a spectrogram — a visual map of frequencies over time.
Extract spectral peaks and turn them into a unique fingerprint.
Match that fingerprint against a massive fingerprint database.
Retrieve the track, artist, and even lyrics.
This technology lets the Shazam app recognize songs even with background noise.
Want to build your own Shazam-like app with advanced features in record time? Use rocket.new to create a fully functional, beautifully designed music recognition app—no coding, no complexity.
You can either build the engine from scratch or use existing platforms.
| Platform | Use Case |
|---|---|
| ShazamKit | For iOS apps to access Apple’s database |
| ACRCloud | Cross-platform support + fast integration |
| EchoPrint | Open-source audio fingerprinting |
Pros: Faster time to market
Cons: Limited customization, dependency on providers
Use libraries like:
FFTW or NumPy for audio processing
Custom fingerprinting based on Avery Wang’s method
Databases: Hashed fingerprints in SQL/NoSQL (like MongoDB or Redis)
This gives you full control over music identification, fingerprint storage, and offline mode.
A complete app like Shazam consists of two key layers: mobile frontend and cloud backend.
Tap-to-identify button (like the orange button in SoundHound)
Real-time waveform animation during recording
Results screen with links to Apple Music or Spotify
History of identified tracks — your own personal history
| Component | Function |
|---|---|
| Fingerprint DB | Store & retrieve unique fingerprints |
| Metadata Store | Holds artist, album, lyrics, etc. |
| User Database | Track preferences, history, SoundHound account |
| API Layer | Handles audio matching & returns results |
Offline Mode: Preload a subset of the database for rural areas or flights.
User experience is where a great app can stand out.
Clear tap-to-scan interface
Visual feedback during listening
Integration with streaming platforms like Apple Music or Spotify
Add songs to a playlist or build playlists directly
Share via messaging services or post to social media
Sing or hum to search
Scrollable lyrics
Mood-based explore music suggestions
Track your personal history and preferences
To ensure smooth performance:
Test in noisy environments and with overlapping audio
Check latency: aim for < 1 second response
Use unit tests for audio engine & UI testing for app flow
Optimize for multiple devices (Android, iOS, tablet, Apple Watch)
Apps like SoundHound and Shazam rely on multiple revenue streams.
Free tier with ads
Premium subscriptions: unlimited scans, offline mode
Affiliate partnerships: redirect to Spotify playlist, Apple Music, or Amazon Music
Partnering with brands for sponsored results of the hottest music
Once the core system is live, consider adding:
Mood detection (happy, chill, etc.)
Machine Learning models for smarter search & recommendations
Sync with your Spotify account to track your music journey
Predict new favorites based on user history
Enable "Say Hey SoundHound" for voice activation
An app like Shazam isn’t just about identifying songs—it’s about solving a common user problem in seconds. When people hear a track and don’t know the title or artist, your app can give them answers quickly. Combine audio fingerprinting with a smooth interface and let users connect directly to platforms like Spotify and Apple Music.
Music fans are searching for smarter ways to connect with what they hear. This is a great time to plan, build, and launch something that stands out. Start shaping your idea, pick the right tools, and create an app that becomes the go-to for music recognition.
