Pytorch code of the ICASSP 2020 paper "Generating and Protecting Against Adversarial Attacks for Deep Speech-based Emotion Recognition Models", by Zhao Ren, Alice Baird, Jing Han, Zixing Zhang, Björn Schuller.
Database: the Database of Elicited Mood in Speech (DEMoS)
Task: seven-class classification
channels:
- pytorch dependencies:
- matplotlib=2.2.2
- numpy=1.14.5
- h5py=2.8.0
- pytorch=0.4.0
- pip:
- audioread==2.1.6
- librosa==0.6.1
- scikit-learn==0.19.1
- soundfile==0.10.2
sh runme.sh
In runme.sh, please run the following files for different tasks:
-
feature extraction: utils/features.py
-
training a model, and evaluation: main_pytorch.py
-
the folder 'pytorch' is corresponding to vanilla adversarial Training
-
the folder 'pytorch-similarity' is corresponding to Similarity-based Adversarial Training
-
Please revise the '$BACKEND' to the folder name 'pytorch' or 'pytorch-similarity' in runme.sh, regarding the method which is achieved
If the user referred the code, please cite our paper,
@inproceedings{ren2020generating,
title = {{Generating and protecting against adversarial attacks for deep speech-based emotion recognition models}},
author = {Ren, Zhao and Baird, Alice and Han, Jing and Zhang, Zixing and Schuller, Bj{"o}rn},
address = {Barcelona, Spain},
Booktitle = {Proc.\ ICASSP},
Year = {2020},
pages = {7184--7188}
}
Zhao Ren
Chair of Embedded Intelligence for Health Care and Wellbeing
University of Augsburg
06.07.2020