Why Your Suno Song Sounds Bad. Artifacts, Mud, and What Mastering Can and Can't Fix

You wrote a cool intro, nailed the melody with the third generation of Suno, played the song in your car and cringed. The voice sounds metallic, low-end is muddy, the cymbals are a sizzle, and the whole track seems smaller compared to everything else on Spotify. If you have asked why your Suno track sounds bad, you are not alone.

Here is the short answer: tracks generated in Suno sound bad for three reasons, and people mistakenly confuse all of them. These are generation artifacts baked in the AI model, mix-related issues like muddy low-mids and buried vocal, and issues related to loudness or file format that reveal themselves on streaming platforms. The mastering can fix the third problem completely, help with the second to some extent, and make the first one worse if you skip restoration. The solution involves a series of steps: generate on the latest model, get the best file possible, fix generation artifacts, balance the mix, and only then master it. I have tried dozens of Suno files and can confidently state the difference is drastic if you follow the right procedure.

Let us take a look at the problem, its causes, and possible solutions in detail.

What Exactly Is Wrong With the Track

Before you can fix a track, you need to identify the issue, because “the track sounds weird” leads nowhere. I categorize Suno problems into five buckets, and almost every track has at least three of them.

The first one is the metallic shimmer. It is a glassy and watery texture that appears on vocals, cymbals, and reverb tails. It is a little bit like a poor bitrate YouTube stream from 2009, except it has dynamics and movement. According to Arefyev Studio’s guide on detecting AI generation defects, it is important to know that it could be three different artifacts. They are a shimmering that alters perception of high frequencies, a warbling that makes sustained notes unstable, and granular residue that turns continuous sound into something chunky. Each of them has a unique solution, so it is important to figure out which of them your track has.

The second one is low-mid mud. As stated in the breakdown by MixMasterAI of their own guide on fixing AI artifacts, generation tends to put all the energy in the 400 to 800 Hz range, which is exactly what a human mixing engineer would do. It creates a feeling that bass, vocals, guitars, and pads fight for the same place, and the whole track becomes boxy and congested on small speakers.

The third one is a digital fizz in the 5 to 8 kHz range. It is the unpleasant sizzling sound on hats and vocal sibilants. It makes listening experience tiring, and it comes not from the cymbal but from the generation algorithm itself.

The fourth one is dynamics compression. Suno export already has everything compressed. The soft passages are louder, and the transients are flattened. There is no punch in the drums, choruses do not open, and everything has the same intensity from the beginning to the end.

The fifth one is artificial stereo. The generator creates width with phase shift, so when you listen to it in mono, the way your phone speaker or the shop PA would, some elements cancel, others wander, and low-end suffers the most.

None of these are “the melody sounds terrible” or “the vocal is bad.” Those are the creative problems, and no amount of mastering will rescue you. But all of these five things are the technical defects, and therefore they are measurable, targetable, and reducible.

Why Does the Generator Include Them?

I think it is important to know the reason of that because it explains why some popular solutions do not help. Unlike Udio, Stable Audio, MusicGen, and other generators, Suno does not generate raw audio sample by sample. According to Intrect’s article about AI music artifacts, the process is the following: the AI model generates tokens, and then a neural audio codec decodes them into an audio signal. It uses the technique called residual vector quantization, which is the step that creates artifacts in the generated audio: it is your metallic sheen and watery swirl, and they are baked into the track from the very beginning.

It is a completely different situation from a recording of a poor performance. Bad vocal with a cheap microphone still has the real acoustic info that can be rescued. Suno vocal has synthesized information plus the residue of the neural codec, which is woven through the musical content, not sitting politely below it like hiss in the tape. This is why a simple wide cut in treble sounds terrible: it sacrifices clarity of the track for the artifact. As stated by the Sunofix guide on improving Suno audio quality, every adjustment affects the music itself, and the smallest effective adjustment is always the goal.

On top of this, there is another, unrelated degradation: the lossy file compression. When you download an MP3 file from Suno, you stack two problems on top of each other: the compression errors in the model and the MP3 encoding errors.

The Generation Model You Used Matters Way More Than Any Plug-In

Here is the crucial thing that I wish someone told me before: it matters way more which Suno model you used than your entire mastering chain. The differences between generations are not minor improvements, they are structural.

According to the version by version comparison from Undetectr for distribution, Suno v3.5 generates at the native 24 kHz sample rate, which means there is literally no frequency content above 12 kHz in these tracks. Sure, you can upsample the file to 44.1 kHz to give it to your distributor, but these frequencies will still be missing: they are not there. And those high frequencies are what gives the openness and expensive gloss to the modern tracks, so it is not surprising that the old Suno tracks seem like they are played from behind the curtain. V4 improved the range, but still upsamples the file from the lower native rate. Their testing showed that v5 and v5.5 are the only versions that generate at the native 44.1 kHz rate with the minimum number of artifacts.

What it means practically is quite straightforward: if your track was generated on v3.5 or v4, and it matters to you, then you better regenerate it on the latest model before wasting even a minute on fix. You cannot boost frequencies that were never there, and while AI audio upscaling tools can reconstruct plausible ones, working with the model that generates them natively is better.

The accessibility of different models is connected to the pricing, so here is the current pricing table. The regular updates of the Suno pricing breakdown are done by margabagus.com, and it looks like this:

The free version gives you 50 credits per day, which allows you to generate roughly 10 songs, using v4.5 models only, and personal non-commercial use only. Pro costs $10 a month ($8 per month on annual subscription) and provides 2,500 credits, access to v5.5, and commercial usage for the songs created while you have a subscription. Premier is $30 per month ($24 annually), providing 10,000 credits, Suno Studio browser multitrack editor, and the deepest stem separation. The songs cost 5 credits per piece, and credits do not accumulate.

One more thing worth mentioning if you intend to release your tracks on the platform. Due to the settlement with Warner Music Group in late 2025, the new terms became applicable to the free tier and paid versions, too, restricting your downloads on the free plan and giving you a commercial-use license instead of the ownership of the songs.

Get the Right Export First

Everything else depends on the exported file, so do not sabotage your work at the start. Get a WAV file if you can afford it. It is an uncompressed file that preserves whatever quality the model created for your track at each further step. MP3 file is still good, but every conversion from it is an additional step of degradation.

Do not try to make your MP3 track better by using a converter to get a WAV file. It just converts lossy compressed data into the WAV container. It is just the illusion, like resizing a low resolution photo to make it clearer. The information is lost, and it is not coming back, even if it seems to be in your editor. The team at Neural Analog guide on improving Suno audio quality illustrates it with a picture of pixelated image resized to larger dimensions and shows the results.

If you want to get some high frequencies back from the file, it requires an additional step of AI audio restoration. It will be mentioned later.

If you have stem separation in your plan, do not forget to get the stems. They are the instruments and vocal layers of the song, and the possibility to operate them separately is the difference between surgical treatment and blunt instruments. When the crackle lives only in the vocal, you should deal with it, not apply a de-noiser to the whole mix. The Pro plan currently gives you 12-stem separation, and Premier plan gives you the deepest stem separation.

What Mastering Can Do for You

Now to the main question of this article. Mastering, the last process that shapes the tone, loudness, and consistency of a stereo mix, is doing its job on Suno track. Here is what mastering can really help you with.

The tonal balance, of course. Suno’s characteristic frequency problems respond well to careful EQing. The corrective recipe offered by MixMasterAI is a good start: you need to cut 3 to 5 dB around 500 Hz to get rid of the low-mid mud, cut 2 to 3 dB near 6 kHz to tame the digital fizz, add a gentle high shelf cut around 14 to 16 kHz if you notice some harsh digital hash in the high range, and collapse the stereo image to mono below 80 Hz to stabilize the bass. Undetectr’s workflow for mastering also falls into this pattern with a slight difference in the numbers: you cut the mud in 200 to 400 Hz range and tame the harshness in 2 to 4 kHz range. They stress that these corrections should be minimal, no more than 1 to 3 dB. The exact frequencies vary track by track, so you need to scan the area with a narrow EQ boost to find the problem and then correct it.

Also mastering can partly restore the dynamics. Since the Suno file is pre-squashed, mastering can breathe 2 to 4 dB of dynamic range into the track, and transient shaping can put some attack back on the drums. It will never recreate the dynamics of a good mix of a human track, but it will improve the quality of your Suno file.

The loudness and delivery format are the things that mastering controls completely. Making sure that the integrated loudness is fine, that true peaks will not cause distortion during streaming, and exporting the track as a clean 16 or 24 bit WAV file at 44.1 kHz rate are exactly the mastering task. We will talk about the exact numbers in a moment, because this is the point where many AI musicians hurt themselves.

Consistency of the release also falls into mastering responsibilities. If you are planning to release an EP, and all your songs were generated in different sessions, it is the mastering that will ensure that track 3 is not louder and brighter than track 2.

What Mastering Cannot Fix

And now it is time for the section that will save your money. Read it twice.

The mastering cannot remove generation artifacts. It just makes them louder. It is not my opinion, it is the physics: when a limiter boosts the whole track, everything that lies below music also rises: the hiss, the metallic tone, the water in the stems, the smeared reverb tails. As stated by the engineers from BChill Mix in their guide on mastering Suno songs, mastering is also the quality control for AI generated music: it is the decision of how much level you can boost before the flaws become more noticeable than the track itself. If your master keeps emphasizing artifacts along with raising the volume, the problem is in the source, and the solution is to go back, not forward.

The mastering cannot create missing frequencies. If the model has generated nothing above 12 kHz, boosting a high shelf will amplify the noise and codec hash in the empty frequency range.

The mastering cannot rebalance the mix. If the vocal is buried under the drums, a stereo mastering has no way to boost the vocal separately. For this you need stems. While the full-mix processing and mastering can change the overall tone of the song, it cannot do what the mixing pass can do, and this is how people end up with ten plugins applied to the file and changed track character without any improvements.

Mastering absolutely cannot help you with taste. If the arrangement is boring, if the lyrics are clumsy, if the hook does not catch the attention, generate it again. Credits are cheaper than hours spent on repairing an impossible track. I would rather burn 50 credits iterating than spending an evening repairing generation number one.

Restoration Should Come Before Mastering

So what does remove artifacts? The new class of tools built specially to fix the AI generated music, and the ordering rule provided by Neural Analog team is the most helpful sentence I can give you: treat the export as the damaged source audio first, apply restoration to rebuild the missing bandwidth and reduce AI shimmer, and then apply mastering to the cleaned file. First comes restoration, then comes mixing, then comes mastering. If the track already has hiss, crackle, metallic tone, or smeared highs, the normal mastering process usually makes it worse.

Several tool classes are worth mentioning here.

Dedicated AI artifact remover. Sunofix is built for this task: it reduces the metallic shimmer, robotic voice, harsh highs, hiss, and smeared reverb tails in the generated file while preserving the melody and arrangement, and it emphasizes itself as a cleaning step before mastering, not as a replacement of it. Intrect’s de-artifact tool offers a parameter-based approach to the problem: you control the level of aggressiveness with an adjustable control set somewhere in 50-70% range, and there is also a diff mode that isolates exactly what is being removed and allows you to hear it.

Spectral restoration and upscaling. Restoration models from Neural Analog are able to reconstruct the high frequency content up to 20 kHz and reduce the codec artifacts, and that is the truth about the generation from the low sample rate or MP3 format. Their stem-based workflow, which allows you to split your track into four stems and to restore a problematic one individually, is what I use when only one element is affected by the artifact.

Traditional restoration suites. iZotope RX is the industry standard for de-noising and spectral surgery, and it also helps with AI material, especially steady hiss and sharp clicks. It is the RX 12 Elements that goes for $99, Standard for $399, and Advanced for $1,399, directly from iZotope. To be honest: I love RX, but the dedicated AI-aware solutions always do better at a much lower cost, because RX was created for acoustic noise, not codec artifacts. Get RX in combination with your audio recordings.

As for the vocal part in particular, there is one tip from Neural Analog’s approach that I apply to every project. In Suno songs, the vocals are buried under the generated delay and reverb. Split the vocal stem, use reverb removal function, and blend the output with about 70% dry signal. The vocals become much clearer, the mix opens up, and half of the muddiness problem is gone without using an EQ.

The Biggest Loudness Mistake That Breaks Too Many AI Songs

Here is something that will shock you for the first time when you try to measure it. The output of the raw Suno audio is super-loud, ranging from minus 8 to minus 10 LUFS, based on the measurement data presented in the LUFS guide for streaming platforms by Undetectr. LUFS stands for loudness units relative to full scale and is the standard for measuring the perceived loudness of sound.

What does it mean? It means that Spotify normalizes audio to the target of minus 14 LUFS. According to the loudness normalization documentation of Spotify, songs louder than that are just being played back quieter. And they recommend achieving minus 14 LUFS loudness with the peaks not exceeding minus 1 dBTP to minimize distortion caused by the lossy encoding. In case you master your track above the target loudness, it is necessary to make the true peak not exceed minus 2 dBTP. True peak is the measurement of the actual peaks that occur after the conversion of digital audio into analog format.

Consequences. When you upload a Suno track to Spotify with the loudness of minus 9 LUFS, it is played 5 dB quieter than it should be. While all other audio on the platform sounds with normal volume, your track has already paid its price in terms of crushed dynamics and more visible artifacts. You have nothing to gain here but the lost punch. Apple Music normalizes audio to minus 16 LUFS through Sound Check feature. But, as mastering engineer Hanna Eng explains in her article, minus 14 LUFS is the playback reference and you should master to the level that your song needs, maintaining the ceiling of true peak. Once again: one master can fit any platform. There is no need to prepare a separate track for each platform unless you enjoy doing so.

My loudness recipe for the Suno songs: After restoration and mixing, I try to hit the integrated loudness level between minus 14 LUFS and minus 11 LUFS depending on the genre with the true peak of minus 1 dBTP. High-energy electronic and hip hop tracks can reach the louder side. Don’t try to achieve the loudness level of minus 8 LUFS that you see in professional masterings because it is possible only due to great mixes. A Suno track is not that. Loudness is a magnifier, you choose what to amplify.

Costs of Tools

Now let’s talk about the money because marketing in this space is overwhelming and some of the solutions aren’t as powerful as their reputation says.

For mastering, iZotope Ozone is the standard desktop tool. The assistant built into the software helps the newbie users to set up the correct chain. The direct prices for iZotope Ozone 12 are: $55 for Elements edition, $219 for Standard and $499 for Advanced. The Elements version is quite enough for Suno mastering as you need just EQ, dynamics and a good limiter there. You should watch the discounts as the price for Standard has recently reached $169 in the online shops.

For online mastering, LANDR is the leader. Its cheapest plan for unlimited MP3 mastering costs $12.99 per month. The comparison run by Dynamoi in their roundup of AI mastering tools shows high rating for the genre awareness of LANDR due to its ability to handle the harsh frequencies and excessive loudness of the Suno output. But I want to warn you: the review of the free trial reveals that the loudness is one of the weak points of LANDR and for the artifact-heavy AI audio material loud mastering can be quite dangerous. If you use LANDR, make sure to upload only restored file there.

There is also BandLab Mastering as the genuinely free solution for unlimited number of masters without watermark. But according to the comparison of free AI mastering services made by Undetectr, it is just a generic preset chain without AI features. All of the famous free services provide only previews and the master is available for payment. Perfect tool for checking whether your song even needs mastering but not for a freebie.

And now a few words about the services I would avoid. There is nothing that could replace the chain of restoration, mixing and mastering in one click. Also, anything that produces only MP3 master reintroduces the very problem that you pay to solve.

The Workflow I Use

Now I would describe the whole sequence that I use for mastering the Suno track. It takes me about an hour per song if the tools are already set up.

First, I generate the song on v5.5 and iterate until the composition is perfect. The problems with composition can be solved only through credits and not with plugins. Then I download the WAV file, and I extract stems if this feature is allowed in the subscription.

Next step is the diagnosis of the track. I listen to the track once with good headphones and write down the defects with the timestamp: shimmer on the vocal tail at 1:12, muddiness under the second verse, harsh hats in the chorus. As Sunofix recommends, I separate moving shimmer from steady hiss and a source defect from the creative problem. Creative problem? Then it is back to generation.

The restoration step. Run the artifact removal or spectral restoration process either on the whole mix or on the guilty stem only if the problem is localized. De-reverb the vocal stem and blend with about 70% dry. Make A/B comparison constantly and back off the intensity if the processed version sounds duller than the original.

Then comes the light mix. Rebalance the stems if necessary, mono the bass below 80 Hz and make small EQ cuts: sweep the mud somewhere between 200 and 800 Hz and cut 2-4 dB, notch the fizz around 5 to 8 kHz by a couple of dB, de-ess if the sibilance survived the restoration.

At last, master the song. A light EQ for the final touch, the upward expansion or transient shaper to recover the punch, and the limiter with the target of minus 14 to minus 11 LUFS with the ceiling of minus 1 dBTP. Export 24-bit WAV with the sample rate of 44.1 kHz. And the most important check: compare sections. Limiter settings that enhance verse can make the final chorus brittle and the artifacts can hide in just one of the sections. I compare intro, the loudest chorus, the quietest passage and the ending on headphones, laptop speakers and phone.

The last test is the emotional, not technical one. Does the master make the song more enjoyable to listen to or makes you think about the processing? If you keep focusing on the process and not the music, you overprocessed the song and the best move here is to undo the last two steps.

An Honest Reality Check

It doesn’t matter how perfectly you complete the process: there is a limit for the restored Suno track. With proper restoration and mastering, it will sound clean and punchy. It can compete in playlists, behind videos and in podcasts. But put it next to professionally produced track on the studio monitor and an experienced ear will notice that it has uniform dynamics, reverb a bit too polite and the transients that don’t snap like real drums in real rooms. The gap is narrowing but it still exists.

It is perfectly fine. The goal of the restoration of the Suno song isn’t to fool the mastering engineer, but to prevent the technical defects from distracting normal listeners from the song itself. Muddiness, fizz and shimmer are the kind of irritation that causes people to skip the song without thinking why. Remove them and the song will be judged by its quality of composition, that is the battle that you really need. This week, pick the Suno song that sounds good to you but not quite great, and then put it through all of the steps of the process: check that you’re working with the right version of the model, export it into a WAV file, restore, fix the vocal reverb issue, cut three little EQ curves, and master it at -14 LUFS with -1 dBTP peaks. Listen to both versions of the song one after another, while in the same car you initially thought sounded terrible.

Sources

• MixMasterAI, AI Music Fixer, Fix Suno, Udio and AI Audio Artifacts Before Mastering: https://www.mixmasterai.co/mastering/fix
• Intrect, How to Fix AI Music Artifacts (Suno, Udio, Stable Audio): https://intrect.io/blog/how-to-fix-ai-music-artifacts/
• Arefyev Studio, How to Fix AI Artifacts, Identify Generation Defects Before Repairing Audio: https://arefyevstudio.com/en/how-to-fix-ai-artifacts/
• Undetectr, Suno 5.5 vs 5 vs 4 vs 3.5, What Changed for Distribution: https://undetectr.com/blog/suno-version-comparison-distribution
• Undetectr, Suno AI Audio Quality, How to Make Your Tracks Sound Professional: https://undetectr.com/blog/suno-ai-audio-quality-improvement
• Undetectr, LUFS for Spotify, The Complete Loudness Guide for Every Streaming Platform: https://undetectr.com/blog/lufs-for-spotify
• Undetectr, Free AI Mastering, 6 Tools Tested: https://undetectr.com/blog/free-ai-mastering
• Neural Analog, How to Improve Suno AI Audio Quality (Upscale, Mix, and Master): https://neuralanalog.com/docs/improve-suno-ai-audio-quality
• Neural Analog, Fix Suno v5.5 Hiss, Crackle and High-End Export Hiss: https://neuralanalog.com/fix-suno-hiss
• Sunofix, How to Improve Suno Audio Quality Without Changing the Song: https://sunofix.app/blog/how-to-improve-suno-audio-quality/
• BChill Mix, How to Master a Suno Song Without Making Artifacts Louder: https://bchillmix.com/blogs/news/how-to-master-a-suno-song-without-making-artifacts-louder
• Spotify for Artists, Loudness Normalization on Spotify: https://support.spotify.com/us/artists/article/loudness-normalization/
• Hanna Eng, What LUFS to Master For, Streaming Targets: https://www.hanna-eng.com/guides/audio-loudness-lufs/
• margabagus.com, Suno Pricing 2026, Free vs Pro vs Premier, Credits and Commercial Use: https://margabagus.com/suno-pricing/
• iZotope, Plugins for Audio Restoration, Mixing, Mastering and More: https://www.izotope.com/
• Dynamoi, 7 AI Mastering Tools for Music Distribution: https://dynamoi.com/learn/ai-music-distribution/ai-music-mastering-tools