OpenUnmix
OpenUnmix is an open-source deep learning system for music source separation. It provides a reference implementation and pre-trained models that can separate a stereo audio mix into its component stems, typically vocals, drums, bass, and other.
Developed by researchers at Deezer Research and released as an open project, OpenUnmix aims to support reproducible
Technical approach: OpenUnmix uses a neural network trained to estimate the magnitude spectrograms of each stem
Implementation and usage: The model is implemented in PyTorch and distributed as an open-source package with
Impact: OpenUnmix has served as a widely used baseline in music information retrieval and as a teaching