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

Last update: Oct 19, 2022

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Deep Learning umss

Overview

Unsupervised Audio Source Separation Using Differentiable Parametric Source Models

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.

Links

🔊 Audio examples

📄 Paper

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
tensorboard==2.3.0

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'

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.

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

Related tags

Overview

Unsupervised Audio Source Separation Using Differentiable Parametric Source Models

Links

Requirements

Training

Evaluation

Acknowledgment

Copyright

Owner

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