Introducing Music Tagger V2
Major upgrade to our metadata tagging engine that dives deep into subgenres and complete instrumentation,...
Streamline music
review
Yet for music libraries, digital stores, and catalog managers, sifting through thousands (or even millions) of tracks often means listening in full just to identify standout moments like choruses, loops, or hooks. This manual process drains time and energy from teams that need to move fast.
Without a smarter, faster way to surface a track’s most compelling sections, discovery lags, selection slows, and catalog value remains underused. Speed and precision in previewing aren't just nice to have: they're essential for maximizing engagement and making the most of your content library.
Accelerate track browsing with structural audio intelligence
Content creators and music buyers want to cut through the noise and listen smarter. With Music Tagger, we analyze the musical structure of each track to automatically detect repeating sections (like choruses, verses, or drops) and extract a 30-second summary that represents the most relevant part of the track. In parallel, we generate rich metadata including tempo, mood, key, instrumentation, and rhythm patterns.
Segment cues for faster track evaluation
What can music
catalog and stores expect?
From production music libraries to sync platforms to digital stores, track preview features help accelerate track evaluation and increase placement potential. A&R, music supervisors and customers can focus their listening time on the most impactful segments, while catalogs and platforms offer a smoother experience to clients and curators.
Ready to speed up music discovery?
Let us show you how automated structure analysis and preview generation can secure your platform’s adoption and boost the average ticket.
Frequently Asked Questions
Can it detect choruses or repeating parts ?
Yes. The system detects structural similarities across the track to identify key musical segments like choruses, loops, drops, and more.
How is the 30-second highlight generated ?
Our model identifies the most representative section based on structure, rhythm, and harmony analysis.
What metadata do you provide ?
Mood, genre, BPM, key, instrumentation, structure, tempo profile, energy, and more.
Can this integrate with our CMS or DAM?
Absolutely. Our API plugs into any existing content management or metadata pipeline.
Trusted by music leaders
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