attention_cnn_lstm_covid_mel_spectrogram

Attention-based Hybrid CNN-LSTM and Spectral Data Augmentation for COVID-19 Diagnosis from Cough Sound This repository contains the used source code of the experiments which led to the results presented in the paper Attention-based Hybrid CNN-LSTM and Spectral Data Augmentation for COVID-19 Diagnosis from Cough Sound We used COUGHVID dataset. See in Zenodo . The augmented version is now available publicly on the Kaggle plateform here . The silence removed version of the dataset is now available here . utils.py : Provides some helpful functions (progressbar, plotROCCurve, plotCurves, plotConfusionMatrix,...) attentionlayer.py : Attention layer class pitchshift.py : Run this script to create the signal-augmented version of the dataset specaugment.py : Run this script to create the spectral-augmented version of the dataset (apply SpecAugment technique) cnnbaseline.py : Run this script to start CNN model training lstmbaseline.py : Run this script to start LSTM model training cnnlstmbaseline.py : Run this script to start hybrid CNN-LSTM model training attentioncnnlstm.py : Run this script to start Attention-based hybrid CNN-LSTM model training