Scattering Transforms for Audio in Python

Please cite:

@article{py3fst,
	title={Cortical Features for Defense Against Adversarial Audio Attacks},
	author={Ilya Kavalerov and Frank Zheng and Wojciech Czaja and Rama Chellappa},
	journal={arXiv preprint},
	year={2021}
}

Usage

See scripts directory.

Creating data

scripts/wake/data.sh

You will see data refered to in the scripts and ipython notebooks. v7.18 is the version of the data used in reported results (its the gpu version of v7.17), ignore earlier versions.

Training

scripts/wake_final/vulcan_baseline_train.sh
scripts/wake_final/vulcan_cortical_train.sh

Eval to create DET curves

scripts/wake_final/vulcan_baseline_eval.sh

Create universal adversarial noise attacks

scripts/wake_final/attack_univ_all.sh

Create adversarial music attacks

To do this attack you will need to export a model of a different length first using:

scripts/wake/vulcan_export.124.sh

Then attack with:

scripts/wake_final/attack_univ_music.sh

Eval adversarial music attacks

scripts/wake_final/attack_univ_music_eval.sh

Installing

conda env create -f venvtf1p15nb.yml

Versioning

Running on: Python 3.6.9, tensorflow 1.15, cuda/10.0.130, cudnn/v7.6.5.

For creating datasets, works with: ffmpeg/4.2.1, rubberband 1.8.2. Also using vamp-plugin-sdk 2.9 2019-11-13, libsndfile Version 1.0.28, libsndfile Version 0.1.9.

License

MIT