How we measured
12 AI-generated exports, ITU-R BS.1770-4 gated loudness + dBTP true peak, measured client-side in the TrackGleam engine, July 2026.
Muddy Udio tracks usually have two measurable problems: excess energy between 200 and 500 Hz, and loudness below streaming level. The fix is three moves — cut the low-mid buildup, restore the top end, then master to -14 LUFS with a -1.0 dBTP true-peak ceiling. You can do all three free, in your browser, without uploading anything.
"Muddy" is a feel word, but a meter makes it concrete. Three readings tell you most of the story. First, energy between 200 and 500 Hz: when that band carries more than its share, everything sounds boxy and congested — bass notes blur into guitars, and vocals lose their outline. Second, spectral tilt: muddy tracks are usually dark overall, with the top octaves underrepresented relative to a commercial release. Third, loudness: mud and low level travel together, because low-mid buildup eats your loudness budget without adding any perceived punch.
The loudness half isn't hypothetical. We measured 12 AI-generated music exports from our test library: the median was -15.2 LUFS integrated (range -16.4 to -12.3), and 8 of the 12 sat below the -14 LUFS level streaming platforms normalize toward. Three of the 12 also exceeded the -1.0 dBTP true-peak ceiling — so a typical AI export manages to be quieter than streaming level and at risk of clipping after lossy encoding at the same time. Median loudness range was 6.6 LU.
12 AI-generated exports, ITU-R BS.1770-4 gated loudness + dBTP true peak, measured client-side in the TrackGleam engine, July 2026.
Honest answer first: Udio doesn't publish its rendering internals, so nobody outside the company can say precisely why. But the pattern is consistent with how these systems work. Generative audio models render the whole track as one finished picture — there's no mix session where an engineer carved 300 Hz out of the guitars to make room for the bass. Most musical energy naturally lives in the low-mids, so when every instrument is generated at once, that's where the pileup lands. Add the reverb these models tend to bake into everything (reverb tails stack heavily in the 200-500 Hz region) and a lossy export format on top, and you get congestion that no one EQ'd on purpose — because no one EQ'd anything at all.
The good news: buildup that was never deliberately mixed in responds well to broad, corrective moves at the master stage. You're not fighting someone's artistic decision — you're removing an artifact.
Three moves, in order. Move 1 — cut the mud: narrow EQ cuts between 200 and 500 Hz where the buildup is worst, plus a high-pass below about 30 Hz to clear inaudible rumble that wastes headroom. Small cuts, one to three decibels, do more than you'd expect. Move 2 — restore the top: a gentle high-shelf or dynamic EQ to bring back air. Go easy — AI-generated top end turns harsh and fizzy quickly, which is why dynamic EQ (which only acts when the harshness appears) beats a static boost. Move 3 — set loudness: true-peak limiting to -1.0 dBTP, then bring integrated loudness to -14 LUFS.
You don't need a DAW for any of this. Drop your file into TrackGleam and the engine runs the full chain in your browser — including aifix presets tuned specifically for AI-generation artifacts — and measures gated BS.1770-4 loudness on every master. Then judge the result with the volume-matched A/B: it plays before and after at the same perceived level, so "louder" can't masquerade as "better" — the oldest trick in mastering. It's free, there's no signup, and the audio never leaves your device. One prep tip: start from the best file Udio gives you, ideally WAV. Never feed an MP3 through another lossy encode if you can avoid it.
The complaints cluster differently. Suno users mostly describe a baked-in reverb "sheen" and a top end that's dull until it's suddenly harsh — we cover that triage in why your Suno song sounds off. Udio complaints center more on congested low-mids and soft transients. Same fix family — corrective EQ, careful top-end recovery, loudness to spec — but the emphasis shifts, which is why a preset tuned for one generator's artifacts isn't automatically right for the other. If you're working with Suno exports, the Suno mastering guide walks the same workflow with that generator's quirks in mind.
-14 LUFS integrated with a -1.0 dBTP ceiling covers almost every platform. Spotify documents its -14 LUFS normalization and 1 dB of peak headroom on its own support page; the figures for other platforms below reflect industry consensus, summarized well in iZotope's streaming guide.
| Platform | Playback loudness | True-peak ceiling | Notes (as of July 2026) |
|---|---|---|---|
| Spotify | -14 LUFS | -1 dBTP recommended | Documented by Spotify; louder masters are turned down |
| Apple Music | about -16 LUFS | -1 dBTP | Sound Check turns tracks down, not up; a -14 master plays about 2 dB lower |
| YouTube | about -14 LUFS | -1 dBTP | Normalizes on most playback surfaces |
| Amazon Music | about -14 LUFS | -2 dBTP preferred | Strictest peak headroom of the major platforms |
Verified July 2026 — we re-check these quarterly.
Because normalization turns loud masters down, pushing past -14 LUFS buys you nothing on these platforms — it just trades away dynamics. If you want the full per-platform breakdown, see our LUFS streaming targets guide; for why peaks that look fine can still clip after encoding, see true peak explained.
Mastering is finishing, not remixing. It works on the whole stereo file, so it can fix tonal mud — the 200-500 Hz buildup, the dark tilt, the low loudness. It can't fix balance problems inside the track: a vocal that's genuinely buried under the arrangement, a bassline fighting the kick, or reverb so heavy it's part of the performance. It also can't fully undo severe codec damage from repeated lossy encoding. If your track has those problems, the fastest fix is usually to regenerate: adjust the prompt toward "dry," "clear," or "bright" descriptors, pick the cleanest take, and master that. Two minutes of regeneration beats an hour of trying to polish a render that was compromised at the source.
Usually excess energy between 200 and 500 Hz plus baked-in reverb, both artifacts of how AI generators render a whole track at once. Corrective EQ cuts in that band, a high-pass below about 30 Hz, and careful top-end recovery clear most of it.
Usually yes for tonal mud — low-mid buildup, dark tilt, and low loudness all respond to master-stage EQ and limiting. It cannot fix balance problems inside the track, like a buried vocal; for those, regenerate the track instead.
Cut narrow between 200 and 500 Hz where the buildup is worst — small 1-3 dB cuts go a long way — and high-pass the inaudible rumble below about 30 Hz. Then restore air gently above 8 kHz, ideally with dynamic EQ so the AI top end does not turn harsh.
-14 LUFS integrated with a -1.0 dBTP true-peak ceiling covers Spotify and YouTube. Apple Music normalizes near -16 LUFS and Amazon Music prefers -2 dBTP of peak headroom (as of July 2026), but a -14 / -1.0 master plays back correctly everywhere.
No. Tonal mud is fixable on the full stereo mix at the master stage. Stems only become necessary for balance problems between elements, like turning a vocal up relative to the instruments.
Not with TrackGleam — the entire mastering chain runs in your browser on your device, and the audio never touches a server. There is also no signup and no watermark on the free master.
Master a track free — no signup, nothing uploads
Includes the AI Fix presets for AI-generated tracks.
Master your Suno songs free in the browser — no signup, no upload. Hit -14 LUFS and -1.0 dBTP for Spotify, fix mud and sheen, and keep a real WAV.
True peak (dBTP) measures peaks between samples. Why 0 dBFS masters still clip after lossy encoding, and why -1.0 dBTP is the streaming-safe ceiling.
Every guide and comparison, in one place.