How I Tried Building an AI Drum Enhancement Algorithm (And Why I Stopped)

The Problem With AI-Isolated Drums

I'll be the first to admit it: I'm NOT a machine learning engineer. My background is in conversion optimization and marketing, with music production as a serious hobby. But sometimes the most interesting projects come from that perfect intersection of skills and curiosity. 🔍

The issue is simple but significant. AI music generators like Suno, Udio, and Riffusion rely on drum isolation algorithms to train their models and provide downloadable stems. But the drums they produce sound TERRIBLE to professional ears.

If you've ever tried to use AI-isolated drums in a professional setting, you've heard the artifacts - weird flanging effects, missing transients, mushy low end, and that distinct "underwater" quality. These problems make the drums unusable for serious production work.

Project Scope & Goals

My question was straightforward: Can someone with zero machine learning experience vibe code a model that improves AI-isolated drums? I documented this journey not because I had all the answers, but because the process itself proved fascinating.

The goal was to build a machine learning algorithm that could take low-quality, AI-isolated drum stems (like those from Demucs) and enhance them to match the quality of actual isolated drum tracks. Think of it as "drum upscaling" rather than isolation.

I wasn't trying to extract drums from mixed tracks - that problem has already been tackled by tools like Demucs. Instead, I focused on taking those existing AI isolations and making them sound professional enough for actual music production use.

Creating a Training Dataset

The first major hurdle was building a suitable dataset. To train the model properly, I needed thousands of examples that contained both:

• The "ground truth" - actual isolated drum tracks from multitrack recordings
• The "AI version" - the same drums processed through Demucs or similar AI isolation tools

Finding this data was an adventure in itself. I scoured publicly accessible links where producers had shared both master tracks and isolated drum stems. In many cases, I had to combine separate drum tracks (kick, snare, toms, cymbals) into a complete drum bus.

After weeks of data collection and cleaning, I've assembled a training dataset of approximately 4,000 songs. Each song contains the original master recording and the true drum isolation - not artificially stemmed, but actually soloed out in the DAW during mixing.

One clever trick I developed: using the Demucs algorithm itself to verify whether a track was truly a drum part. By analyzing volume peaks and comparing the outputs against other stem types, I could programmatically flag tracks as likely drum parts.

Tools I'm Using

For this project, I'm leaning heavily on AI assistance while learning machine learning concepts on the fly. My core toolkit includes:

1. Development Environment

• VS Code - My primary code editor
• Cline.bot - For small, controlled code modifications
• Vast.ai - For renting GPU power when training the full model

2. AI Assistance

Claude Sonnet has been my primary AI assistant. I've developed a specific workflow:

• Give Claude EXTENSIVE context about what I'm building
• Always challenge its first suggestions (they're rarely optimal)
• Use a custom script to gather all current code to feed back to Claude when improvements are needed
• Switch between normal and extended thinking modes depending on complexity

My approach is more experimental than academic. Instead of trying to understand every nuance of machine learning theory, I'm focused on practical outcomes - feeding Claude my requirements, evaluating the outputs, and iteratively improving.

Lessons in Machine Learning (So Far)

The biggest revelation is that existing libraries often weren't suitable for my specific needs. Most audio processing models are built for 16kHz mono audio, but professional drum tracks need full stereo and higher sample rates.

Rather than trying to adapt existing libraries, my approach evolved to feeding Claude detailed requirements and letting it help craft a more custom solution. This was my most critical learning: be SPECIFIC about what your model needs to accomplish.

My evaluation process is primarily auditory. Instead of obsessing over loss functions and metrics, I listen to the outputs and tell Claude what aspects need improvement. This domain expertise in audio production is proving more valuable than theoretical ML knowledge.

Audio Comparison: AI vs True Drums

To give you a sense of what I'm working with, here's a comparison between AI-isolated drums (using Demucs) and the true drum stems. Listen for the loss of punch, clarity, and stereo image in the AI version:

Not bad. But sometimes Demucs struggles. My model aims to bridge this quality gap, transforming the second example to match the first as closely as possible.

Key Takeaways & Lessons Learned

While I ultimately discontinued this project, the learning experience was invaluable. The biggest revelation was understanding the true complexity gap between current AI capabilities and what's needed for production-quality audio enhancement.

The technology to create truly professional drum enhancement exists in theory - it's a combination of advanced model architectures, massive training datasets, and computational resources. However, the barrier to entry for a solo developer without significant ML research background proved too high.

My biggest takeaway: research your tools and the state of the technology EXTENSIVELY before starting. Understanding not just what's theoretically possible, but what's practically achievable given your resources and expertise, can save countless hours of effort.

While this project didn't reach its intended destination, the journey itself was incredibly educational. If you're considering diving into AI audio processing or ML projects without a formal background, I hope my experience provides some valuable perspective on both the possibilities and limitations of current technology.