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E2E_ASD_DETECTION

ASD Detection based on feature extractors(Auto-encoder or wav2vec2.0) and BLSTM classifier

Information

The model training and evaluation scripts for BLSTM, AE-BLSTM-FT, AE-BLSTM-JT, W2V-BLSTM-FT, and W2V-BLSTM-JT.

Arguments

For AE-BLSTM-FT, it consists of two stages. You should train the model rgrs and clsf in sequence.

python main.py [--train] [--eval] [--target_model rgrs, clsf] [--exp exp]

All the other models only uses following command:

python main.py [--train] [--eval] [--exp exp]

Usage

  1. Set wave files and corresponding paths in data/*.csv.
  2. Train model with --train command. You can select experiment name with --exp argument.
python main.py --train --exp ft_test
  1. After the training is done, you can evaluate your model with --eval argument --exp given in 2.
python main.py --eval --exp ft_test

Acknowledgement

This work was supported by the Institute of Information & communications Technology Planning & evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00330, Development of AI Technology for Early Screening of Child/Child Autism Spectrum Disorders based on Cognition of the Psychological Behavior and Response).