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

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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.

Copyright

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

Owner
PhD Student in Music Information Retrieval working on Audio Source Separation. MIP-Frontiers fellow
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