/undaw

Unsupervised Domain Adaptation for Acoustic Scene Classification with Wasserstein Distance

Primary LanguagePythonOtherNOASSERTION

UNDAW Repository

Welcome to the repository of UNDAW (Unsupervised Adversarial Domain Adaptation Based on the Wasserstein Distance)

This is the repository for the method presented in the paper: "Unsupervised Adversarial Domain Adaptation Based on the Wasserstein Distance," by K. Drossos, P. Magron, and T. Virtanen.

Our paper is accepted for publication at the 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), Mohonk Mountain House, New Paltz, NY.

You can find an online version of our paper at arXiv: https://arxiv.org/abs/1904.10678

If you use our method, please cite our paper.

Table of Contents

  1. Dependencies, pre-requisites, and setting up the code
  2. Reproduce the results of the paper
  3. Use the code with your own data
  4. Previous work
  5. Acknowledgement

Dependencies, pre-requisites, and setting up the code

In order to use our code, you have to firstly:

  • use Python 3.x and install all the required packages listed at the requirements file for PiP or at the requirements file for Anaconda
  • download the data (the file AUDASC_features_labels.zip) from DOI
  • download the pre-trained non-adapted model (the file AUDASC_pretrained_weights.zip) from DOI and the adapted model from DOI (this is optional and is required only in the case that you want to reproduce the results of the paper)

Then:

  • unzip the file AUDASC_features_labels.zip. This will produce the following files, which will have to be placed inside the directory dataset/data:

    • test_features.p
    • test_scene_labels.p
    • training_features.p
    • training_scene_labels.p
    • validation_features.p
    • validation_scene_labels.p

  • unzip the file AUDASC_pretrained_weights.zip. This will produce the following files, which will have to be place inside the directory pretrained_weights:

    • label_classifier.pytorch
    • LICENSE
    • non_adapted_cnn.pytorch
    • target_cnn.pytorch

  • unzip the file undaw.zip. This will produce the following files, which will have to be place inside the directory outputs/img/models:

    • adapted_cnn.pt

That's it!

You can either refer to the reproduce the results of the paper section for reproducing the results presented in our paper, or to the use the code with your own data section if you want to use our code for your own task and/or with your own data.

Enjoy!

Reproduce the results of the paper

To reproduce the results of the paper, you have to:

If you find any problem doing the above, please let us know through the issues section of this repository.

Use the code with your own data

To use your code with your own data, you will have to:

  • provide a set of features to be used
  • modify the data_handlers._domain_dataset.DomainDataset class
  • modify the modules used and are in the modules package
  • modify the settings to be used (i.e. the file that you will use and will be in the settings directory)
  • modify the settings reading for each of the modules, by modifying the functions in the helpers._models.py and helpers._modules_functions.py files

To use the code with your new settings, you will have to place the new settings file in the settings directory and specify the new settings file at the command line, when calling the main.py. For example, like:

python scripts/main.py --config-file new_settings_file

Notice that the file name is without extension, meaning that only YAML (i.e. *.yaml extension) files can be used.

The processes (i.e. pre-training, adaptation, and evaluation) should be run with any module/neural network.

If you have any question, please ask it using the issues section of this repository.

Previous work

Our work is based on the following previous work:

Acknowledgement

  • Part of the computations leading to these results were performed on a TITAN-X GPU donated by NVIDIA to K. Drossos.
  • The authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources.
  • The research leading to these results has received funding from the European Research Council under the European Union’s H2020 Framework Programme through ERC Grant Agreement 637422 EVERYSOUND.
  • P. Magron is supported by the Academy of Finland, project no. 290190.