def dirsWavFeatureExtraction(dirNames, mtWin, mtStep, stWin, stStep, computeBEAT=False):
    '''
    Same as dirWavFeatureExtraction, but instead of a single dir it takes a list of paths as input and returns a list of feature matrices.
    EXAMPLE:
    [features, classNames] =
           a.dirsWavFeatureExtraction(['audioData/classSegmentsRec/noise','audioData/classSegmentsRec/speech',
                                       'audioData/classSegmentsRec/brush-teeth','audioData/classSegmentsRec/shower'], 1, 1, 0.02, 0.02);

    It can be used during the training process of a classification model ,
    in order to get feature matrices from various audio classes (each stored in a seperate path)
    '''

    # feature extraction for each class:
    features = []
    classNames = []
    fileNames = []
    for i, d in enumerate(dirNames):
        [f, fn] = dirWavFeatureExtraction(d, mtWin, mtStep, stWin, stStep, computeBEAT=computeBEAT)
        if f.shape[0] > 0:       # if at least one audio file has been found in the provided folder:
            features.append(f)
            fileNames.append(fn)
            if d[-1] == "/":
                classNames.append(d.split(os.sep)[-2])
            else:
                classNames.append(d.split(os.sep)[-1])
    return features, classNames, fileNames
dataset/ETHPy150Open tyiannak/pyAudioAnalysis/audioFeatureExtraction.py/dirsWavFeatureExtraction/original