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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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.

Copyright

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

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Comments
  • Evaluations Out-Of-The-Box

    Evaluations Out-Of-The-Box

    Starting from the base source code on a clean installation, I made it possible to quickly start making evaluations.

    Changelist: -made addendums to the main ReadMe (cleared up the install, gave more links) -added directions on where to put the audio files -added the f0 mixtures calculated with the multi-pitch estimator -added the energy file -added the US-F and the US-S models -commented unneccessary lines that asked for CREPE files in data.py -removed an outdated ddsp.synthetic_data import -added tqdm for evaluations (this can be enabled with a parsed --show-progress argument) -added a python file to run multiple evaluations on multiple models

    opened by liam-kelley 0
  • Request to release the pretrained model

    Request to release the pretrained model

    Hello how are you?

    I would like to know if it is possible to release the pre-trained model, along with that how to make the inference to process in my own music,

    Another observation is that it is not possible to visualize the audio examples in "Audio examples",

    Thank you very much for the hard work in creating this network!

    opened by lucasbr15 0
Owner
PhD Student in Music Information Retrieval working on Audio Source Separation. MIP-Frontiers fellow
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