The Northwestern University Source Separation Library (nussl) (pronounced "nuzzle") is a flexible, object oriented python audio source separation library created by the Interactive Audio Lab at Northwestern University. At its core, nussl provides implementations of common source separation algorithms as well as an easy-to-use framework for prototyping and adding new algorithms. The aim of nussl is to create a low barrier to entry for using popular source separation algorithms, while also allowing the user fine tuned control of low-level parameters.
NOTICE: nussl is currently in alpha. Please be mindful.
Please see the issues page before contacting the authors.
- Master contains the most recent stable version, the version that you get when you pip install nussl.
- Development contains the most recent work, but is not as stable as master. Most all of development work happens in this branch before being propagated to master.
- Experimental has many more implementations, but many have not been vetted or properly tested. Some methods in this branch require extra functionality that cannot be included in a pip install, such as the Vamp binary files or pytorch.
- gh-pages is used to auto generate our documentation using Sphinx.
- Other feature branches do exist from time to time as well.
Full documentation is available here.
Note: This package has been tested with python 2.7, but not python 3.x yet. python3 coming soon!
At its core, nussl contains implementations of the following source separation algorithms:
Spatialization algorithms:
- Degenerate Unmixing Estimation Technique (DUET)
- PROJET
Repetition algorithms:
- REpeating Pattern Extraction Technique (REPET)
- REPET using the cosine similarity matrix (REPET-SIM)
- Separation via 2DFT
General matrix decomposition/Component Analysis:
- Non-negative Matrix Factorization with MFCC clustering (NMF)
- Robust Principal Component Analysis (RPCA)
- Independent Component Analysis (ICA)
Benchmarking
- Ideal Mask
- High/Low Pass Filtering
Composite Methods
- Overlap Add
- Algorithm Picker (multicue separation)
Other Foreground/Background Decompositions
- Harmonic/Percussive Source Separation (HPSS)
- Melody Tracking separation (Melodia)
Deep Learning
- Deep Clustering
Your Algorithm Here
The nussl also has an evaluation framework, which provides interoperability between nussl and mir_eval (a python implementation of BSSEval) as well as implementations of other evaluation methods.
We recommend getting installing anaconda prior to using this package.
Installation is easy if you have pip (recommended):
$ pip install nussl
If you are using nussl for your research project, we please ask that you cite it using one of the following bibtex citations:
@inproceedings {nussl
author = {Ethan Manilow and Prem Seetharaman and Bryan Pardo},
title = "The Northwestern University Source Separation Library",
publisher = "Proceedings of the 19th International Society of Music Information Retrieval
Conference ({ISMIR} 2018), Paris, France, September 23-27",
year = 2018
}
@Misc{nussl,
author = {Ethan Manilow and Prem Seetharaman and Fatemeh Pishdadian and Bryan Pardo},
title = {{NUSSL}: the Northwestern University Source Separation Library},
howpublished = {\url{https://github.com/interactiveaudiolab/nussl}},
year = {2018}
}
See the contribution guide for detailed information. But the basics are: bug fixes/enhancements/etc have the standard github process; but, when adding new algorithms, contributors must provide benchmark files, paper references, and trained models (if applicable).
Ethan Manilow (website), Prem Seetharaman (website), Fatemeh Pishdadian (website)
Former:
Corey Grief (website), Daniel Felix Kim, Ben Kalish
Contact Ethan Manilow (ethanmanilow [at] u [dot] northwestern [dot] edu) with any questions or issues. Please look at the "issues" page before reporting problems.
nussl 0.1.6* is under an MIT License
The MIT License (MIT)
Copyright (c) 2018 Interactive Audio Lab
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.