Wondering what genre a song is, or how to tag your own release? This genre finder listens to the audio itself, not a database entry, so it works on unreleased demos, obscure tracks and genre-bending records that streaming metadata labels too broadly. You get a primary genre, a sub-genre, and a probability split across every style the AI hears, for example 60% neo-soul, 25% R&B and 15% jazz. The written report explains why: the drum feel, harmony, production choices and vocal delivery that point to each label, the era or movement the sound comes from, and which elements blend genres together.
Example result
Input
A 2-minute MP3 of an unreleased song with a synth bassline, gated drums, breathy vocals and a saxophone solo.
Output
Primary genre: synth-pop. Sub-genre: 80s revival / retro synth-pop. Style characteristics: the gated reverb snare, analog-style bass arpeggio and bright polysynth pads are classic mid-80s signatures. The tempo sits around a mid-tempo dance groove rather than a club tempo. Influences and fusion: the sax break and lush chord extensions pull toward sophisti-pop, while the heavy sidechain pumping on the pads is a modern dance touch that dates the production after 2015. Era and movement: fits the synthwave and retro revival wave rather than an original 1980s record, mainly because of the loudness and low-end weight. Tagging suggestion: synth-pop first, with synthwave as a secondary tag.
Scores
How it works
How Music Genre Classifier works
- 01
Upload or record the track
Sign in, open the tool and add an audio file, or record a clip in the browser. You can choose which AI model runs the classification.
- 02
Listen for style markers
The model pays attention to rhythm and groove (swing, trap hi-hats, four-on-the-floor kicks), instrumentation, chord language, sound design and vocal style, since these are the cues that separate neighboring genres.
- 03
Place it in time and lineage
It links what it hears to an era or movement, such as 90s boom bap or 2010s synthwave, and notes influences borrowed from other scenes and how closely the track follows or breaks genre conventions.
- 04
Split the probabilities
The report ends with genre scores that add up to 100, so a crossover track shows several genres with their share instead of being forced into one box.
Use cases
Who it is for
Independent artists tagging a release
Distributors and streaming pitch forms ask for a primary and secondary genre. See how the audio actually reads before you choose, so the track lands on playlists where it fits.
Curators and A&R sorting submissions
Get a quick, consistent label for incoming demos, and use the probability split to spot crossover songs that could work on more than one playlist.
Producers checking a reference
If you are aiming for a specific sub-genre, run your mix next to a reference track and see whether the AI hears the same style markers in both.
Curious listeners
Heard something on a radio stream or in a video with no track name? Record a clip and learn which genre and era it belongs to, so you know what to search for next.
Tips
Get better results
- Use a section that represents the song, usually the chorus or main groove, rather than a quiet intro.
- Upload a clean file when you can. A clip recorded from a phone speaker loses the bass and drum detail that separates many electronic genres.
- Read the lower percentages too; they often name the influence that makes the track sound fresh.
- For a song that changes style halfway, run each section separately and compare the splits.
- Treat the result as a starting point for tags, then check it against two or three artists you think are similar.
Limitations
What it can't tell you
Genre labels are fuzzy and scenes disagree about them, so the output is an AI estimate of how the track sounds, not an official classification. The tool does not identify the song title or artist; use a song identifier for that. Very short clips, live recordings with crowd noise, or tracks that deliberately mix many styles can produce a flatter probability split.
Frequently Asked Questions
Music Genre Classifier questions
What genre is this song?
Upload the song or a 30 to 90 second clip of its main section, and the genre finder returns a primary genre, a sub-genre and a percentage for each style it detects. The written notes explain which sounds point to each label, so you can judge the answer yourself rather than trusting a single word.
How is this different from the genre shown on Spotify or Apple Music?
Streaming services mostly assign genres to artists, not to individual songs, so a rapper's one acoustic ballad still gets filed under hip hop. This music genre identifier analyzes the audio of the specific track, which also makes it useful for songs that have no metadata yet, such as your own demos.
Can it tell sub-genres apart?
Yes, sub-genre classification is part of every report. It looks at details such as drum programming, tempo feel, bass sound and vocal treatment, which is what separates, for example, deep house from tech house or drill from trap. Close neighbors often share the probability split, and the report says why.
Why do the genre percentages add up to 100?
They show how the AI divides its confidence across candidate genres. A track that scores 90% in one genre is a clear fit, while a 40/35/25 split tells you it blends styles. Treat the numbers as relative likelihoods from the model, not as measurements of how much of each genre is in the song.
Does it identify the artist or song name?
No. This song genre identifier describes style, era and influences only. To find out what a song is called and who made it, use the song identifier tool instead, then come back here if you want to know more about its genre.
Do I need an account?
Yes. Genre analysis runs on AI models, so it requires signing up and a paid plan with monthly credits. There is no trial tier for AI tools; the pricing page lists the current plans.
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Music Genre Classifier uses credits from a paid LindaleAI plan. See pricing