This depression detection work is run on Jupyter notebook.
- DAIC: https://dcapswoz.ict.usc.edu/
- AVID: http://avec2013-db.sspnet.eu/
- To obtain both DAIC and AVID datasets, agreement forms should be signed and return to corresponding email address
Classification model training code and regression model training code are provided in classfication folder and regression folder. To run the code, prefix in BiLSTM.ipynb, cnn_audio.py and fusion_net.ipynb should be set to the path where DAIC dataset placed. To run fusion_net.ipynb, path of trained lstm_model and cnn_model should be set corresponding path.
Preprocessing code of audio recordings in AVID dataset is offered in AudioPreprocess.ipynb. You should change the paths to your AVID dataset path before runing. Regression model training code is provided in cnn_audio_reg_avid.py. The input required by the training code is preprocessed audio clips, which is saved in avid_info.pkl. Therefore, prefix varaible should be set to the path where avid_info.pkl stored during the preprocessing step.
The code is the implementation of the paper Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model. If you find the paper or the code is useful, please cite the paper below.
@Article{app10238701,
AUTHOR = {Lin, Lin and Chen, Xuri and Shen, Ying and Zhang, Lin},
TITLE = {Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model},
JOURNAL = {Applied Sciences},
VOLUME = {10},
YEAR = {2020},
NUMBER = {23},
ARTICLE-NUMBER = {8701},
URL = {https://www.mdpi.com/2076-3417/10/23/8701},
ISSN = {2076-3417},
}