1 Filter the vad

-   by running vad_processing/vad_processing.ipynb
-   fitered vad files should be put in the path preprocess/audio/filter_vad/

2 Using the filtered vad files to generate the samples, ground truth label and write them into csv files

-   by running preprocess/audio/generate_samples.py 

    #Train csv
    main(0, windowSize, positive_negative_ratio, vad_files)

    #Test csv
    main(experimentNumber, windowSize, positive_negative_ratio, vad_files, number_of_experiment_repeated, unrealized_sample_category(optional)) 

3 Making pkl files for training and corresponding experiments' samples

-   by running data_loading/make_examples.py

    -   generate training samples  
        -   make_all_examples(0, windowSize)

    -   generate samples for experiment 1
        -   make_all_examples(1, windowSize, numberOfExperiment)

    -   generate samples for experiment 2
        -   make_all_examples(2, windowSize, numberOfExperiment)

    -   generate samples for experiment 3
        -   make_all_examples(3, windowSize, numberOfExperiment, 'all_unsuccessful')
    
    -   generate samples for experiment 4
        -   make_all_examples(4, windowSize, numberOfExperiment, 'start')

    -   generate samples for experiment 5
        -   make_all_examples(5, windowSize, numberOfExperiment, 'continue')

4 Execute the training

-   running baseline/testTrain.py

    Train:
    -    main(True, 0, windowSize)

    Test for different experiments
    -    main(False, experiment_number, windowSize, number_of_experiments_repeated)