Open-Unmix - A Reference Implementation for Music Source Separation
Open-Unmix - A Reference Implementation for Music Source Separation - Published in JOSS (2019)
Science Score: 95.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 13 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
3 of 6 committers (50.0%) from academic institutions -
○Institutional organization owner
-
✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords from Contributors
Repository
Repository for the open-unmix JOSS submission
Basic Info
Statistics
- Stars: 7
- Watchers: 3
- Forks: 2
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
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
- Code: open-unmix-pytorch
- Status: feature complete
- Tag:
v1.0.0 - Pretrained models: UMXHQ and UMX
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
museval objective evaluation
- Code: museval
- Tag:
v0.3.0 - Status: released on pypi in version 0.3.0
norbert: wiener filter implementations
- Code: norbert
- Status: released on pypi in version 0.2.1
- Tag:
v0.2.1 
Paper
to create the paper locally
bash
docker run -v $PWD:/data openbases/openbases-pdf pdf
Owner
- Name: sigsep
- Login: sigsep
- Kind: organization
- Website: http://sigsep.github.io/
- Repositories: 23
- Profile: https://github.com/sigsep
Open Resources for Audio Source Separation
JOSS Publication
Open-Unmix - A Reference Implementation for Music Source Separation
Authors
Inria and LIRMM, University of Montpellier, France
Sony Europe B.V., Germany
Inria and LIRMM, University of Montpellier, France
Sony Corporation, Japan
Editor
Ariel Rokem
Tags
audio music separation deep learningGitHub Events
Total
Last Year
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Fabian-Robert Stöter | f****r@i****r | 46 |
| Stefan Uhlich | s****h@e****m | 23 |
| Fabian-Robert Stöter | f****t | 12 |
| Antoine Liutkus | a****s@i****r | 7 |
| Daniel S. Katz | d****z@i****g | 1 |
| Ariel Rokem | a****m@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 10
- Total pull requests: 4
- Average time to close issues: 4 days
- Average time to close pull requests: about 6 hours
- Total issue authors: 3
- Total pull request authors: 3
- Average comments per issue: 3.6
- Average comments per pull request: 0.75
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- bmcfee (5)
- hagenw (4)
- arokem (1)
Pull Request Authors
- StefanUhlich-sony (2)
- danielskatz (1)
- arokem (1)