This is the source code for the experiments related to the paper Unsupervised Audio Source Separation Using Differentiable Parametric Source Models.

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.

🔊 Audio examples

📄 Paper

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
tensorboard==2.3.0

python train.py -c config.txt

python train_u_nets.py -c unet_config.txt

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

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 2021 Kilian Schulze-Forster of Télécom Paris, Institut Polytechnique de Paris. All rights reserved.