# Audio Source Separation: Demixing Speech, Music, and Environmental Sounds with Deep Learning
Status: public
Confidence: medium (0.8) (verified)
Last verified: 2026-05-28
Generation: ai_structured

## TL;DR

Audio source separation estimates individual sources from a mixed audio signal. This repair lowers confidence and removes uncited performance and production-use claims.

## Core Explanation

The source-backed summary highlights three well-known deep-learning separation systems: Conv-TasNet for time-domain speech separation, Demucs for music separation, and Open-Unmix as an open reference implementation for music source separation.

## Further Reading

- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://doi.org/10.1109/TASLP.2019.2915167)
- [Music Source Separation in the Waveform Domain](https://arxiv.org/abs/1909.01174)
- [Open-Unmix - A Reference Implementation for Music Source Separation](https://doi.org/10.21105/joss.01667)

## Related Articles

- [AI for Audio Processing: Speech Recognition, Music Generation, and Sound Understanding](../ai-for-audio-processing-speech-recognition-music-generation-and-sound-understanding.md)
- [AI for Audio Processing: Sound Event Detection, Acoustic Scene Analysis, and Environmental Intelligence](../ai-for-audio-processing.md)
- [AI Democratization: Open-Source Models, Low-Code AI, and Accessible Machine Learning](../ai-for-democratization.md)