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For reproducing the paper Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders.

The repo is based on the (old) project template.

Reproducing steps

  1. Download the dataset from Zenodo. Put the folder data at the root of this repo.
  2. run python train.py -c config.json

The checkpoint model_best.pth will be saved at saved/gmvae-synth. After the training completes, play with ismir19-217-sup-material.ipynb to see the results.

Details missing from the paper

A pitch classifier which takes as input the pitch latent variable is added on top of the pitch space.

Note

  • In provided dataset, spec and spec-norm refer to the extracted mel-spectrograms and the normalized ones.
  • The configuration of config.json refers to the fully-supervised model in the paper, which is also the model used for controllable synthesis and timbre transfer. In config.json, change the label_portion under the trainer tag to train a semi-supervised model.

Citation

Please kindly cite the paper as follows if you find it useful.

@inproceedings{DBLP:conf/ismir/LuoAH19,
  author    = {Yin{-}Jyun Luo and
               Kat Agres and
               Dorien Herremans},
  editor    = {Arthur Flexer and
               Geoffroy Peeters and
               Juli{\'{a}}n Urbano and
               Anja Volk},
  title     = {Learning Disentangled Representations of Timbre and Pitch for Musical
               Instrument Sounds Using Gaussian Mixture Variational Autoencoders},
  booktitle = {Proceedings of the 20th International Society for Music Information
               Retrieval Conference, {ISMIR} 2019, Delft, The Netherlands, November
               4-8, 2019},
  pages     = {746--753},
  year      = {2019},
  url       = {http://archives.ismir.net/ismir2019/paper/000091.pdf},
  timestamp = {Thu, 12 Mar 2020 11:32:59 +0100},
  biburl    = {https://dblp.org/rec/conf/ismir/LuoAH19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

TODO

  • Clean up the code
  • Add comments
  • Confirm if the raw audio files can be released

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Reproducing code for Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders

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