This software package is designed to separate the contributions of different musical instruments in an audio recording. The algorithms are documented in:
- Sören Schulze, Emily J. King: Sparse Pursuit and Dictionary Learning for Blind Source Separation in Polyphonic Music Recordings. EURASIP J. Audio Speech Music Process. 2021, 6 (2021). doi:10.1186/s13636-020-00190-4
- Sören Schulze, Johannes Leuschner, Emily J. King: Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients. Signals 2.4, pp. 637--661 (2021). doi:10.3390/signals2040039
Please cite at least on of these papers if you use the software in any academic context. Further description is available on the website of the mathematics department of Colorado State University. There is also the precompiled API documentation.
The code was tested with Python 3.7.7, NumPy 1.19.1, SciPy 1.5.0, Cython 0.29.21, PyFFTW 0.11.1, Matplotlib 3.2.2, Tensorflow 2.2.0, and Tensorflow-probability 0.9.0. The sparse pursuit method does not require Tensorflow in its default settings, so its import may be commented out.
Both methods require at least 16 GB of RAM and the neural network was trained on an NVIDIA GeForce GTX 1080 Ti.
In order to compile the Cython code (needed for the sparse pursuit method),
Makefiles are provided in musisep/audio and musisep/dictsep.
To compile the Sphinx documentation, run make html in the docs directory.
In summary, you can run the following commands from the main directory:
make -C musisep/audio
make -C musisep/dictsep
make -C docs html
The input data can be obtained from: https://www.math.colostate.edu/~king/software/Musisep-data.zip. It should be unzipped to the main directory.
For the sparse pursuit method, invoke:
python3 -m musisep.dictsep
from the main dictionary. The neural network training is run via:
python3 -m musisep.neuralsep
However, the file musisep/neuralsep/__main__.py must be adjusted
for the different samples.
Copyright (C) 2018-2021 Sören Schulze
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.