Suno AI lets you summon a full song in minutes — drums, guitars, vocals, the works. It's a rush, until you actually listen closely. Then you hear it: a digital hiss wrapping around the vocal like static electricity, a metallic warble that makes the singer sound like a defective android, crackles scattered through the chorus like broken glass. The track had potential, but now it sounds like it was recorded inside a malfunctioning hard drive. I've spent more nights than I care to admit trying to salvage tracks that were 90% brilliant and 10% trash, and that 10% ruins everything. The good news? There's a process to fix this. A real, step-by-step workflow that treats Suno output for what it actually is: a damaged source that needs repair. It involves Suno's own tools, plus a few external plugins — some of them free, like Audacity. The goal isn't to turn garbage into gold. The goal is to strip away the artifacts so the good stuff underneath can breathe, so you can put the track on YouTube or Spotify without cringing every time the vocal comes in.
In short: separate stems first using Suno's 'Get stems' or multi-stem separation, clean vocals with Adobe Podcast Enhance at 50%, EQ out the hiss above 16kHz and mud around 400Hz, use Audacity's Noise Reduction with 12-18dB, and master carefully by listening to the weakest moments first. Download Audacity (free). Budget: none if you stick to free tools, Adobe Podcast is also free for basic use. Main tip: never try to fix the entire song at once, always work stem-by-stem.
The Core Strategy: Why You Must Separate Stems First
The first mistake everyone makes: dragging the whole song into Audacity and hitting it with a noise reduction sledgehammer. I did this. I destroyed a perfectly good instrumental section because I was trying to fix a crackling vocal. The problem is simple: artifacts don't live everywhere at once. That crackle might only be in the vocal stem. The hiss might only be haunting the synth pad. If you apply one fix to the entire mix, you're not cleaning — you're smearing damage across everything that was already fine. The correct move is to use Suno's built-in 'Get stems' feature, or if you've already exported, an external multi-stem separation tool. What you get is individual tracks: vocals, bass, drums, instruments, sometimes up to twelve separate files depending on how complex the arrangement is. Percussion, ambient effects, all isolated. This is where the real work begins, because now you can be surgical. You can take that one noisy vocal stem, hit it with heavy artifact reduction, and leave the rest of the mix untouched. It's the difference between fixing a broken window and demolishing the whole house.
Step 1: Initial Cleanup Inside Suno Studio
Before you even think about exporting, you need to do some cleanup on Suno's side. Open your song in Suno Studio and use the solo button. Listen to one stem at a time — just the vocal, then just the drums, then just the bass. You're hunting for specific problems: clicks that sound like someone tapping on the microphone, robotic warbles that make the singer sound like they're underwater, hiss that never stops, or a smeared ambience that turns everything into mush. Suno has tools for this: 'Remove FX' and 'Artifact Reduction'. The key is to start conservatively. Don't crank the slider all the way up and pray. Preview the change, then gradually increase the tool's sensitivity or threshold until the artifact fades. I learned this the hard way when I applied too much artifact reduction to a vocal and it came out sounding like it was being sung from inside a cardboard box. If the audio starts to sound worse or unnatural, ease the settings back. Sometimes the best result is blending the processed stem with the original at a lower level, so the fix is subtle instead of destructive.
Step 2: Advanced Vocal Enhancement with External Tools
After the initial cleanup and export, you're ready for professional post-production. I export the vocal stem as a WAV file and upload it to Adobe Podcast Enhance. The tool is designed to clean up podcast audio, but it works wonders on AI vocals. The process is almost too simple: drag the file in, adjust the enhancement slider to around 50%, and let it process. That 50% setting is a sweet spot. Go higher and the vocal starts to sound overly processed, like it's been through too many filters. Go lower and you're not removing enough of the background noise. The principle here is to treat the AI vocal as if it were recorded in a slightly noisy room. You're not trying to rebuild the vocal from scratch. You're just cleaning it up, removing the garbage in the background, and bringing the voice forward so it sits properly in the mix.
Step 3: Surgical EQ and Filtering for a Cleaner Mix
Now comes the frequency-level cleanup. I use Audacity because it's free, but any DAW with a decent EQ will work. The first move is to cut the high-frequency hiss. Load the vocal stem, go to Effect > Filter Curve EQ, and cut everything above 16,000 Hz. Most people can't even hear that range properly, and in AI audio, it's where the unnatural digital air lives. Gone. Next, cut the mud zone. Somewhere between 200 and 500 Hz, usually around 400 Hz, there's a buildup that makes the vocal sound boomy and unclear. A small cut here cleans up the low-mid range without thinning out the vocal. Then there's the harsh zone. If the vocal sounds brittle or metallic, like someone singing through a tin can, make a small, narrow cut somewhere between 2 and 4 kHz. Some people use dynamic EQ in the 2-7 kHz range for this, which is more advanced but also more effective. Finally, de-essing. AI vocals often have piercing 'S' or 'Sh' sounds that feel like needles in your ears. A de-esser plugin targets those specific frequencies and tames them without affecting the rest of the vocal. It's a small fix, but it makes a huge difference when you're listening on headphones.
Step 4: Taming Dynamics and Removing Persistent Noise
Some artifacts are stubborn. They survive the EQ cuts and the vocal enhancement. For these, you need Audacity's Noise Reduction tool. The workflow is precise: find a small section of the track that has only the artifact noise. This could be silence at the start or end of the stem, where you can hear the hiss or hum without any music or vocal covering it. Select that section, go to Effect > Noise Reduction > Get Noise Profile. Now Audacity knows what the problem sounds like. Select the entire track, go back to Effect > Noise Reduction, and apply it. I use a Reduction setting of 12-18 dB and a Sensitivity of 6. Push it too hard and the vocal turns into mush. Push it too soft and the noise remains. For more advanced issues, there's a plugin called Soothe2. It's not free, but it automatically finds and tames resonances — annoying frequencies that ring out and make the vocal sound unnatural. I use it sparingly, only when the vocal has a specific frequency that's driving me insane and manual EQ isn't cutting it.
Step 5: How to Master Your Mix Without Amplifying Artifacts
Mastering is where most people ruin their track. They think mastering means "make it louder," so they slam a limiter on the mix and push the volume until it clips. The problem? Louder doesn't mean better if the louder version reveals every artifact you just spent hours trying to hide. The golden rule: fix all obvious mix problems first. If the track is clipping, bring it down. If the highs are harsh, EQ them. If there's hiss, clean it. Only then do you even think about increasing loudness. When you're ready, import the clean pre-master and check that the volume isn't going into the red. Set your monitoring level so you're not fooled by loudness. Now, here's the critical part: don't start listening at the best-sounding chorus. Start with the weakest moments of the track. The quiet intro, the sparse verse, the section where the artifacts are most obvious. These moments define how far you can push the loudness. Apply a limiter gently. Push the volume a little, then A/B compare it with the original. Repeat in small increments. The moment the artifacts become noticeable or distracting, stop. That's your ceiling. Pushing past it means you're making the track louder at the expense of making it worse.
Final Checks, Normalization, and Exporting for Release
Before you release the track, there's a final checklist. First, loudness normalization. Select the entire track in Audacity, go to Effect > Loudness Normalization, and set the target to -14 LUFS. This is the standard for YouTube and Spotify. It ensures the track won't distort when played on different devices and won't sound too quiet compared to other songs. Export the final file as WAV. Not MP3, not some compressed format. WAV is lossless, which means you're not introducing any new artifacts at the export stage. Now comes the ultimate test: listen to the final track on as many different systems as possible. Phone speakers, car stereo, cheap earbuds, high-quality headphones. If it sounds good on all of them, you're done. Also, check the mix in mono. Some streaming platforms and devices play audio in mono, and if your track falls apart when you collapse the stereo field, you have a problem. I've had tracks that sounded perfect in stereo and turned into a muddy mess in mono. Better to catch that now than after you've uploaded it.
FAQ: Quick Answers to Common Suno Cleaning Questions
Someone always asks: what if I don't have paid tools like Soothe2? The answer is simple. Focus on the free tools. Audacity's EQ, Noise Reduction, and a free Limiter plugin can accomplish 90% of this workflow. The key is using them correctly, not having the most expensive software. I've heard tracks cleaned up with nothing but Audacity that sound better than tracks processed with thousands of dollars of plugins used poorly. Another common question: how much artifact reduction is too much? Your ears are the final judge. If the vocal or instrument starts to sound thin, watery, or unnatural, you've gone too far. Always A/B compare with the original. You should be improving the sound, not just changing it. And finally: can this workflow be used for other AI music generators like Udio? Absolutely. The core principles — treating AI audio as a damaged source, separating stems, using targeted EQ and noise reduction — are universal. The specific artifacts might vary, but the process remains the same. Clean the stems individually, fix the frequencies that are broken, and master conservatively. That's the workflow, regardless of which AI spat out the audio in the first place.