Most separation tools are wrappers around a handful of models. Knowing which is which tells you what quality to expect.
Deezer's release that made separation mainstream. A spectrogram-based U-Net that predicts a mask for each source.
Strengths: fast, light, easy to run, still fine for rough work.
Weaknesses: noticeable artifacts, especially watery vocals. Works on magnitude spectrograms and reuses the original phase, which is a significant part of the artifact.
Still widely embedded in older tools.
Meta's model family, and the significant advance: it works directly on the waveform rather than the spectrogram.
That matters because waveform models handle phase natively rather than reconstructing it. The result sounds meaningfully more natural — less of the phasey quality that characterises spectrogram separation.
Hybrid Demucs added a spectrogram branch alongside the waveform branch, taking advantage of both. Hybrid Transformer Demucs (HTDemucs) added transformer layers for better long-range context.
Strengths: the best widely-available open model, natural-sounding output, actively developed.
Weaknesses: computationally heavier, slow on CPU without a GPU.
Newer models split the spectrum into bands and process each with dedicated subnetworks, recognising that vocals, bass, and cymbals need different treatment. Strong results, particularly on vocals.
An open reference implementation designed for research reproducibility. Not the best-sounding, but valuable as a well-documented baseline.
Most separation products build on Demucs variants or proprietary models trained on similar principles, often with:
- Larger private training sets — the single biggest quality factor
- More stems — separating guitar and piano rather than lumping them into "other"
- Post-processing to reduce artifacts
- Server GPUs for speed
The differences between good commercial tools are usually smaller than their marketing suggests, because they share architectural lineage.
Demucs is open source, free, and runs locally — which also means your audio never uploads. It needs Python and works far faster with a GPU, but CPU works.
For anyone processing a lot of material, or working with confidential audio, this is the option worth knowing about.
Separation quality across good tools is closer than it looks. The differentiator is usually what happens next: whether you get four files in a folder, or stems in a project you can immediately edit — which is where the actual work of fixing separation artifacts happens.
Related reading: how stem separation works, best stem separation tools, and AI music tools and stem bleed.
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