How to improve a Suno song's audio quality, step by step

October 24, 2026 · All posts

A Suno export is usually close to finished, but "better" can mean three different things: a level that is too low, AI artifacts in the top end, or dynamics that a limiter has already flattened. Each needs a different fix, and the order matters. This guide walks through the steps in the order that works, with the numbers behind each one.

What is actually wrong with your track

We measured 248 Suno exports that people uploaded for mastering. They show where the problems usually are:

• Loudness is rarely the problem. The median export sits at −14.3 LUFS, right next to Spotify's −14 LUFS. Only 6% are louder than −10 LUFS.
• Dynamics are the weak spot. The median loudness range is 5.8 LU, and 40% of exports are under 5 LU. Verses and choruses can sit at almost the same level.
• Artifacts are the usual complaint. Glassy or metallic highs, ringing cymbals and smeared transients come from the generator, not from a bad mix.

So "make it louder" is usually the wrong first move. Use the table to pick the fix that matches what you hear.

Step 1. Start from the best file you can get

Download WAV when your Suno plan allows it. An MP3 can still be mastered, but its encoding losses cannot be undone, and they make artifacts harder to clean. Before you spend a download, read Suno download limits (2026).

Step 2. Measure before you touch anything

Three numbers describe the file: integrated loudness (LUFS), true peak (dBTP) and loudness range (LRA). The free LUFS checker shows all three, with no account needed. How to read loudness numbers explains what each one tells you and what it misses.

Step 3. Clean the AI artifacts before you shape or limit

A limiter raises everything that is quiet, including the shimmer you want gone. So clean first. Echoteka's cleanup looks for the marks a generator leaves in the audio:

• narrow tones above 15 kHz, cut with very narrow filters;
• small level jumps at the seams between generated blocks;
• a row of fixed spectral peaks, handled with a pitch shift of 5.5 to 8.5 cents, which keeps the length exact.

Cleanup is not mastering. In our data, on 2,230 cleaned tracks, the median change in integrated loudness was −0.02 LU, and 89% of the 1,966 tracks with a loudness reading stayed within ±0.5 LU. What AI artifact cleanup actually changes has the full table.

Cleanup cannot fix everything. The metallic sheen that comes from the model itself stays. A better prompt or a new generation is the fix for that, not post-processing.

Step 4. Fix the mud and the harshness with a light hand

After cleanup, listen for two things. The first is a boxy low-mid range, where the mix sounds closed in and the vocal loses clarity. The second is harsh sibilance on vocals or hi-hats. Use a gentle, narrow cut for the mud and a de-esser for the harshness, rather than a broad high-shelf boost.

Suno mixes are already balanced for a finished sound, so big EQ moves usually do more harm than good. A reference track in the same genre is the fastest way to check tone and stereo width.

Step 5. Shape the dynamics, then set the loudness

Aim for about −14 LUFS for Spotify and YouTube. Spotify publishes −14 LUFS as its level, and independent measurements put YouTube at about the same. A Suno song pushed to −9 or −10 LUFS gains nothing on streaming. The platform turns it down, and the hot master has already taken away the difference between verse and chorus. The Suno loudness report shows where exports sit.

If a master comes out flat, check the before/after report for the loudness range. When loudness has cost too much movement, Echoteka can offer a second version with more dynamics kept. Too loud and flat? explains how to listen to it.

Step 6. Set the true-peak ceiling at −1 dBTP

Streaming encoders can create peaks higher than the file you upload, so keep true peak at or below −1 dBTP. Echoteka's limiter works to that ceiling. On the cleaned files in our data, the median true peak was −1.36 dBTP, and all 1,765 tracks that were measured came out at or below −1 dBTP. True peak explained covers why the margin matters.

Step 7. Compare at the same loudness

A louder version always sounds better for the first ten seconds, so the comparison has to be fair. Match the loudness of the original and the master, then listen on headphones and on a phone speaker as well as on your monitors. The before/after report gives loudness, true peak, loudness range, crest factor and stereo for both files, so you can check the numbers as well as your ears.

Mistakes that undo the work

• Normalising to 0 dBFS and uploading as is. The peaks sit at the ceiling and distort after encoding.
• Stacking limiters: the generator's own loudness, a maximiser plugin and a limiter in the distributor's tool.
• Mastering stems one by one. Master the full mix. If you split stems, clean the separation noise first and then master the mix.
• Judging by the waveform. A fat waveform is not a better master. The numbers and your ears are.

Doing it in Echoteka

Run the steps in order in the lab. Clean the AI artifacts if the track has the marks, then master the cleaned file for the release you are making. Cleanup is 1 free a day, then 1 credit per run. A full master is one free every day, with no account needed, and more masters use credits.

FAQ

Can I improve the sound inside Suno itself? Only by changing the prompt or the seed and generating again. That can give a different result, but it does not fix loudness, tone or the artifacts in the export. Those need the finished file to be processed.

Will mastering remove the synthetic feel completely? No. Cleanup weakens the marks a generator leaves, and mastering makes the track sound finished. It does not turn a weak arrangement into a strong one, and it does not change where the music came from.

Is mastering the same as mixing a Suno track? No. A Suno export is a finished stereo mix, with no separate tracks to balance. Mastering works on that mix as a whole: loudness, tone, peaks and cleanup.

Should I clean before or after mastering? Before. Compression and limiting raise whatever is already in the file, so cleaning afterwards means fighting harshness that has already grown louder.