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Repository for the open-unmix JOSS submission
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Open-Unmix Paper

This repository combines the software contributions for open-unmix, a reference implementation for deep learning based music source separation.

We choose PyTorch to serve as a reference implementation for this submission due to its balance between simplicity and modularity. Furthermore, we already ported the core model to NNabla and plan to release a port for Tensorflow 2.0, once the framework is released. Note that the ports will not include pre-trained models as we cannot make sure the ports would yield identical results, thus leaving a single baseline model for researchers to compare with

Software Packages

Open-Unmix for Pytorch

musdb dataset parser

A python package to parse and process the MUSDB18 dataset, the largest open access dataset for music source separation.

  • Code: musdb
  • Tag: v0.3.1
  • Status: released on pypi in version 0.3.1
  • DOI

museval objective evaluation

  • Code: museval
  • Tag: v0.3.0
  • Status: released on pypi in version 0.3.0
  • DOI

norbert: wiener filter implementations

  • Code: norbert
  • Status: released on pypi in version 0.2.1
  • Tag: v0.2.1
  • DOI

Paper

to create the paper locally

docker run -v $PWD:/data openbases/openbases-pdf pdf
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