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Unsupervised Music Source Separation Using Differentiable Parametric Source Models

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# Unsupervised Music Source Separation Using Differentiable Parametric Source Models

This is the source code for the experiments related to the paper [Unsupervised Music Source Separation Using Differentiable Parametric Source Models](https://arxiv.org/abs/2201.09592).

It contains a re-implementation of parts of the DDSP library in PyTorch. We added a differentiable all-pole filter which can be parameterized by line spectral frequencies or reflection coefficients.

Please cite the paper, if you use parts of the code in your work.

## Links
[:loud_sound: Audio examples](https://schufo.github.io/umss/)

[:page_facing_up: Paper](https://arxiv.org/abs/2201.09592)

[:file_folder: CSD Database](https://zenodo.org/record/1286570#.Y0ZsbNJByUk)

[:microphone: Multiple-f0 estimation](https://github.com/helenacuesta/multif0-estimation-polyvocals)

## Requirements

The following packages are required:

pytorch=1.6.0
    matplotlib=3.3.1
    python-sounddevice=0.4.0
    scipy=1.5.2
    torchaudio=0.6.0
    tqdm=4.49.0
    pysoundfile=0.10.3
    librosa=0.8.0
    scikit-learn=0.23.2
    tensorboard=2.3.0
    resampy=0.2.2
    pandas=1.2.3

These packages can be found using the conda-forge and pytorch channels.
Python 3.7 or 3.8 is recommended.
From a new conda environment:

conda update conda
    conda config --add channels conda-forge
    conda config --set channel_priority strict
    conda config --add channels pytorch
    conda config --add channels pytorch
    conda install pytorch=1.6.0
    conda install matplotlib=3.3.1 python-sounddevice=0.4.0 scipy=1.5.2 torchaudio=0.6.0 tqdm=4.49.0 pysoundfile=0.10.3 librosa=0.8.0 scikit-learn=0.23.2 tensorboard=2.3.0 resampy=0.2.2 pandas=1.2.3

## Training

python train.py -c config.txt
    
    python train_u_nets.py -c unet_config.txt
    
## Evaluation

python eval.py --tag 'TAG' --f0-from-mix --test-set 'CSD'

Note : 'TAG' is the evaluated model's name. (Example: unsupervised_2s_satb_bcbq_mf0_1)
    
## Acknowledgment

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068.

## Copyright

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