Free AI mastering, built by an audio engineer.
MixMasterAI runs the mastering chain I'd build by hand for an AI-generated track — reference matching, genre-aware EQ, multiband compression, true-peak limiting, LUFS normalization — automated and tuned for the specific artifacts Suno, Udio, Mureka, and ElevenLabs leave in their output.
Built by
Collins Asein — Founder & Audio Engineer
I'm a music producer and audio engineer who's been working with AI music since the early Suno alpha builds. I built MixMasterAI to solve the mastering problem that AI generators created — pre-limited tracks with metallic high-frequency artifacts that fall apart on Spotify because there's no headroom to work with. Every mastering decision in the pipeline below is one I make by hand when I master a track manually; the platform applies them automatically and tunes them for the AI output you give it.
Read full bio →Mastering pipeline
What MixMasterAI actually does to your audio
Five stages, applied in order. Every stage cites a real industry standard or a measurable signal — nothing is "black box AI magic." The whole chain runs in under 60 seconds and delivers a 24-bit WAV plus a 320 kbps MP3.
Reference matching
Your upload is analyzed against a library of professionally mastered reference tracks in the same genre. Frequency balance, dynamic range, stereo width, and peak levels are measured against the reference distribution.
Genre-aware EQ
EQ corrections are applied based on what the reference set tells us about the target genre. Hip-hop gets different mid-range handling than indie rock; lo-fi gets different high-frequency treatment than EDM.
Multiband compression
Multiple frequency bands are compressed independently to control the dynamics of each region — keeping the bass tight, the mids upfront, and the highs clean without smashing the whole signal.
True-peak limiting
A true-peak limiter holds the ceiling at -1 dBTP. This headroom protects against inter-sample peaks that can clip when streaming services re-encode your file to lossy formats (Spotify Ogg, Apple AAC).
LUFS normalization
Final integrated loudness is normalized to your target — Spotify -14, Apple Music -16, YouTube -14, broadcast -23. Measurement uses ITU-R BS.1770-4, the same standard every major DSP runs internally.
Editorial standards
How I keep technical content accurate
Every guide gets a manual review before it publishes and a periodic re-review after that. Technical claims cite the upstream standard whenever one exists — ITU-R BS.1770-4 for loudness, EBU R128 for broadcast, ACX for audiobooks, the platform's own published spec for streaming targets. AI music tools update quickly, so every page surfaces a "last reviewed" date and gets re-checked when AI generators ship a new version.
Privacy
How your audio is handled
Mastering jobs upload your file to our server, process it in a temporary workspace, and delete the source after delivery. The 30+ in-browser tools at /tools never upload anything — they decode and process audio entirely client-side using the Web Audio API and ffmpeg.wasm. No account is required for either.
Get in touch
Bug reports, feedback on a master, technical questions, content corrections — all get answered.
hello@mixmasterai.coUpload a track to
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Supports WAV · FLAC · MP3 · M4A · AIFF