# Demucs Projects at AI Tinkerers

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> Technology record last updated: 2026-02-25T19:48:40Z
> Generated: 2026-09-22T08:41:00Z

Demucs: The state-of-the-art AI model for music source separation, using a Hybrid Transformer architecture to isolate individual audio stems.

Demucs (Deep Extractor for Music Sources) is a powerful, open-source model developed by Meta AI (Facebook Research) for high-fidelity audio source separation. It operates directly on the raw waveform, bypassing traditional spectrogram-based methods to minimize artifacts. The latest version, Hybrid Transformer Demucs (HTDemucs), utilizes a dual-domain U-Net and cross-domain transformer to achieve a competitive 9.20 dB SDR on the MUSDB HQ test set, a benchmark for separating music into constituent tracks: vocals, drums, bass, and accompaniment. This makes it the go-to tool for musicians and researchers needing clean, fast extraction of stems for remixing or analysis.

- Official technology site: https://github.com/adefossez/demucs
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [AI and music - entertaining people's ears with AI](https://toronto.aitinkerers.org/talks/rsvp_V0tbwYU7b48)

I will be presenting my work on AI music. First, I will demonstrate that low parameter count and efficiency could be achieved for AI music generation. This comes from my recent publication on music diffusion that is conditioned on the vocal. Second, I will showcase my project Xing Xing. It is a karaoke singing app that separates tracks and has live transcription. It uses vaiious off-the-market AI tools and models. It demonstrates scalability and different ways that musical AI could be applied

- Event context: AI Tinkerers Toronto - December Meetup sponsored by Auth0 and TribalScale! — 2025-12-03 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_V0tbwYU7b48

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