# set patch parameters
    config['xInput'] = config['n_frames']
    config['yInput'] = config['audio_rep']['n_mels']

    # load audio representation paths
    file_index = config_file.DATA_FOLDER + config[
        'audio_representation_folder'] + 'index.tsv'
    [audio_repr_paths,
     id2audio_repr_path] = shared.load_id2audioReprPath(file_index)

    # load training ground truth
    file_ground_truth_train = config_file.DATA_FOLDER + config['gt_train']
    [all_ids_train, id2gt_train] = shared.load_id2gt(file_ground_truth_train)
    [_, id2label_train] = shared.load_id2label(file_ground_truth_train)
    label2ids_train = shared.load_label2ids(id2label_train)

    # load test ground truth
    file_ground_truth_test = config_file.DATA_FOLDER + config['gt_test']
    [all_ids_test, id2gt_test] = shared.load_id2gt(file_ground_truth_test)
    [_, id2label_test] = shared.load_id2label(file_ground_truth_test)
    label2ids_test = shared.load_label2ids(id2label_test)

    # set output according to the experimental setup
    classes_vector = list(range(config['num_classes_dataset']))

    # tensorflow: define the model
    with tf.name_scope('model'):

        # support for training [classes, support, time, freq, channel]
        x = tf.placeholder(tf.float32,
    # set patch parameters
    config['xInput'] = config['n_frames']
    config['yInput'] = config['audio_rep']['n_mels']

    # load audio representation paths
    file_index = config_file.DATA_FOLDER + config[
        'audio_representation_folder'] + 'index.tsv'
    [audio_repr_paths,
     id2audio_repr_path] = shared.load_id2audioReprPath(file_index)

    # load training ground truth
    file_ground_truth_train = config_file.DATA_FOLDER + config['gt_train']
    [all_ids_train, id2gt_train] = shared.load_id2gt(file_ground_truth_train)
    [_, id2label_train] = shared.load_id2label(file_ground_truth_train)
    label2ids_train = shared.load_label2ids(id2label_train)

    # load validation ground truth
    file_ground_truth_val = config_file.DATA_FOLDER + config['gt_val']
    [all_ids_val, id2gt_val] = shared.load_id2gt(file_ground_truth_val)
    [_, id2label_val] = shared.load_id2label(file_ground_truth_val)
    label2ids_val = shared.load_label2ids(id2label_val)

    # set output according to the experimental setup
    config['classes_vector'] = list(range(config['num_classes_dataset']))

    # save experimental settings
    experiment_id = 'fold_' + str(config_file.FOLD) + '_' + str(
        shared.get_epoch_time())
    experiment_folder = config_file.DATA_FOLDER + 'experiments/' + str(
        experiment_id) + '/'