The AI sound is often a production problem
A growing amount of AI-generated music does not fail because the underlying idea is unusable. It fails because the production gives it away. The song may have a plausible topline, a workable groove and a convincing style reference, yet still sound synthetic because the master is crushed, the high end fizzes, the low mids blur together or the ending collapses in the final moments.
That distinction matters. If the problems listeners associate with AI music are partly production flaws rather than permanent characteristics of generative systems, then creators have more control than the debate often allows. The question is not only whether a model such as Suno can produce a strong first draft. It is whether the person using it can evaluate that draft like a producer, reject weak sections and finish the recording with the same discipline expected in conventional music production.
Pixabay is a useful case study because its role in digital content creation gives its reviewers unusual exposure to this material. The platform is widely used by creators looking for music for videos, websites, social media and other projects, and its ecosystem increasingly includes AI-generated work. According to guidance from the Pixabay team, reviewers repeatedly encounter specific technical traits that make AI tracks feel unfinished, artificial or difficult to use.
What Pixabay’s reviewers keep hearing
One of the most common issues is excessive compression. In practical terms, compression reduces the difference between quieter and louder parts of a recording. Used well, it can add control and impact. Used too heavily, everything becomes equally loud, and the track loses the dynamic movement that helps conventional recordings breathe. Pixabay’s guidance identifies this as a recurring weakness in AI masters. For creators, the lesson is simple. Do not assume the exported generation is a finished master. If the file already sounds pinned to the ceiling, it may need restoration of dynamics before any final limiting is applied.
Clipping and distortion are related problems. AI outputs can already be loud, and pushing them harder during mastering can create harsh distortion or intersample peaks that are not obvious on a basic meter. Pixabay recommends checking true peaks and treating around -1 dBTP as a practical mastering reference, while making clear that this is standard mastering advice rather than an official rejection threshold. That nuance is important. The point is not to chase a magic number. The point is to prevent a generated file from becoming more brittle and fatiguing at the final stage.
Frequency balance is another giveaway. The Pixabay team points to artificial fizz or harshness around roughly 5 to 8 kHz, as well as muddy buildup in the low mids around 400 to 800 Hz. Those ranges will not be wrong in every song, and heavy-handed equalisation can damage a track. Still, a gentle cut in the right place can reduce the glassy sheen that often makes AI music feel less like a recording and more like an approximation of one. The same applies to metallic timbres, random clicks, glitches, unstable stereo width and mechanical timing. These are not always compositional failures. Often they are production faults that need editing, regeneration or restraint.
Suno should be treated as a writing room, not a mastering engineer
This is especially relevant to Suno and similar generative music platforms. A strong Suno output can feel deceptively complete because it arrives with vocals, arrangement, instrumentation and a mastered loudness profile already in place. That convenience is also the risk. Creators may hear a convincing first chorus and treat the file as releasable, when the better workflow is to regard the generation as raw material.
A more careful process starts with selection. Generate enough alternatives to avoid becoming attached to the first usable result. Export the strongest version, then listen in a digital audio workstation such as Reaper, Audacity or another editor where visual metering and precise editing are possible. Check for clipping, compare loud and quiet sections, listen in mono, and isolate moments where the vocal, drums or bass begin to lose definition. Where the platform allows section replacement or regeneration, it may be better to replace a flawed passage than to rescue poor source audio with processing.
This connects with a broader pattern we recently examined in our analysis of Suno v6 and improving AI music production. As models become more musically capable, the weakest link often shifts from basic generation to judgment, arrangement and finishing. Better tools do not remove the need for production taste. They raise the standard for what an acceptable AI-assisted release should sound like.
The ending is where the illusion often breaks
Pixabay’s most interesting observation concerns endings. According to its reviewers, the final 10 to 30 seconds are often where AI-generated tracks begin to unravel. A song can sound convincing for several minutes, then suddenly introduce an out-of-key note, weaken the rhythm, change an instrument timbre, become muddier, form an awkward loop or simply stop in a way that feels model-generated rather than musically resolved.
Pixabay says this has been particularly noticeable in some Suno submissions. That does not mean every Suno track has a weak ending, nor does it prove a permanent flaw in the platform. It does show why creators should not judge a generation by its opening minute. Compare the first chorus with the final chorus. Listen for changes in drums, bass, vocals, stereo image and clarity. If the ending deteriorates, trimming it, arranging a cleaner fade or regenerating the final section may produce a better result than trying to mask a structural failure.
This is also where human decision-making becomes visible. A producer knows when a track has overstayed its welcome, when a final hook needs reinforcement and when a generated outro undermines the rest of the song. AI can supply options, but the release decision still belongs to the creator.
Less audible AI makes transparency more important
None of this should be confused with hiding AI involvement. Pixabay asks contributors to label AI-generated work appropriately, and that principle matters across the wider music ecosystem. The productive lesson is different. AI generation and music production are becoming separate stages of one workflow. A model can provide musical raw material, while a human selects, edits, equalises, controls dynamics, masters and decides whether the result has artistic value.
That challenges the assumption that all AI music has an obvious and permanent sound. Some current criticism depends on familiar artifacts, including strange vocals, excessive compression, synthetic textures, weak endings and incoherent arrangements. Those are real problems, but many are engineering problems, and engineering problems tend to improve. As creators learn to identify and remove them, the audible fingerprints of AI generation should become less reliable.
The cultural question then becomes harder. A finished track might include a human-written melody, AI-generated vocals, regenerated instrumentation, conventional mixing and human mastering. At that point, asking whether it is AI music may be less useful than asking which parts were generated, who made the creative decisions and what intention guided the final work. If listeners cannot reliably hear whether AI was involved, AI involvement alone may not determine the subjective value of the music. People may judge whether a track moves them, fits a scene, communicates an idea or shows meaningful creative direction.
At the same time, disappearing audible differences make disclosure more important, not less. If the origin of a recording cannot be inferred from sound alone, transparency becomes one of the few ways audiences, platforms and collaborators can understand how the work was made. Lunar Boom advocates that people and artists who clearly use a lot of AI in their music production process should at least be honest about it.
