We’ve all been there.
You spend dozens of hours mixing a track. Every delay throw is automated. The kick and bass finally stop fighting each other. You bounce the final file, jump into your car for the ultimate “car test,” and press play.
It sounds… small.
Compared to the commercial track that played right before it, your song feels quieter, flatter, and somehow unfinished. That was my reality for years. I’m an indie musician—not a professional mastering engineer. I understand mixing, but mastering has always felt like a different discipline entirely: the final step that turns a good mix into something that actually holds up in the real world.
Hiring a professional mastering engineer can easily cost more than I can justify per track. So recently, I decided to experiment with something I had avoided for a long time: AI Mastering.
This is not a sponsored post. Just an honest breakdown of what worked, what didn’t, and what I learned.
The Loudness Anxiety (and What Actually Matters)
Before uploading anything, I had to reset my expectations.
If you come from a developer or data background, mastering is a bit like preparing software for production. Your track needs to behave consistently across environments: car speakers, phone speakers, headphones, club systems.
One concept that confused me for years was LUFS (Loudness Units Full Scale). Many streaming platforms normalize playback loudness. That means pushing your master as loud as possible doesn’t necessarily give you an advantage—it often just gets turned down automatically.
Once I understood this, my mindset shifted. The goal wasn’t maximum loudness. It was consistency, balance, and translation.
Why I Tried AI Mastering in the First Place
Like many independent creators, my workflow has slowly become more hybrid. I mix my own tracks, reference constantly, and rely on tools to speed up decisions when my ears are tired.
I had a synthwave track sitting on my drive that sounded fine but lacked cohesion. The mix was clean, but it didn’t feel “glued.” I didn’t have the time (or patience) to fully dive into advanced mastering chains, and I needed something fast and objective.
What I was looking for was simple:
- Speed
- A fresh, unbiased set of “ears”
- A result that was good enough to release, not perfect
The Experiment: My Actual Workflow
I tested a well-known web-based AI mastering service. I won’t name it here—most of them work in broadly similar ways anyway.
Step 1: Preparing the Mix
This part matters more than people admit. AI mastering doesn’t fix bad mixes.
- Format: 24-bit WAV
- Peak level: Around -6 dB
- Master bus: No limiter, minimal processing
Step 2: Upload
Drag, drop, wait a couple of minutes. That part was almost unsettlingly simple.
Step 3: Choosing a Direction
Instead of being fully “one-click,” the tool offered a few tonal profiles. I chose a slightly brighter option to compensate for a muddy low-mid area in my mix.
The Results: What Surprised Me (and What Didn’t)
I A/B tested the AI master against my original mix.
What Worked Well
The EQ decisions were better than I expected. The AI cleaned up low-mid buildup around the 300 Hz range and added clarity on the top end without sounding brittle.
Loudness-wise, the track landed in a competitive range for the genre. More importantly, it sounded consistent across different playback systems.
Where It Fell Short
The limitations were obvious once I listened closely.
- Transient control: My snare lost some punch. The limiter reacted aggressively to peaks, something a human engineer would likely adjust by ear.
- Stereo width: The track sounded wider, but mono compatibility suffered. On phone speakers, some vocal presence disappeared. These weren’t deal-breakers, but they were reminders that the AI doesn’t “understand” musical intention—it reacts to patterns. While exploring other creative tools during this process, I also noticed how AI is spreading beyond audio alone. Projects like MusicArt, which focuses on visual generation, made it clear that automation is touching every part of the creative stack—not just sound.
So, Who Is AI Mastering Actually For?
After mastering several tracks this way, here’s my honest takeaway.
AI mastering makes sense when:
- You’re releasing demos, singles, or online-only tracks
- Budget is tight (or nonexistent)
- You want a fast reference to identify balance issues in your mix
It’s probably not the right choice when:
- You’re preparing a vinyl release or high-stakes commercial project
- Your music relies heavily on subtle dynamics (jazz, classical, acoustic)
- You need collaborative feedback rather than an automated result
Final Thoughts
AI mastering didn’t replace my creativity. It acted more like a fast, slightly rigid assistant. In a few minutes, it got my track most of the way toward being release-ready.
For an independent creator, that gap—the space between “almost finished” and “good enough to share”—is often where projects die. In my case, AI mastering helped close that gap.
Just one reminder: trust your ears more than the waveform.
This article reflects my personal experience and is not affiliated with or endorsed by any mastering service.
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