def METHOD_NAME(self):
    # Test the BFCC extractor compared to Rastamat specifications
    audio = MonoLoader(filename = join(testdata.audio_dir, 'recorded/vignesh.wav'),
                                        sampleRate = 44100)()*2**15
    #Expected values generated in Rastamat/MATLAB
    expected = [ 20.28919141, 23.80362425, 26.69797305, 27.10461133, 26.64508125,
                 26.7758322, 27.1787682, 27.10699792, 26.29040982, 25.04243486, 
                 24.24791966, 24.17377063, 24.61976518, 25.29554584, 24.87617598, 
                 23.79018513, 23.04026225, 23.20707811, 23.09716777, 23.33050168,
                 22.8201923, 21.49477903, 21.63639095, 22.12937291, 22.01981441,
                 21.70728156]
    frameSize = 1102
    hopSize = 441
    fftsize = 2048
    paddingSize = fftsize - frameSize
    spectrumSize = int(fftsize/2) + 1
    w = Windowing(type = 'hann', 
                  size = frameSize, 
                  zeroPadding = paddingSize,
                  normalized = False,
                  zeroPhase = False)
    spectrum = Spectrum(size = fftsize)
    mbands = TriangularBarkBands(inputSize= spectrumSize,
                      type = 'power',
                      highFrequencyBound = 8000,
                      lowFrequencyBound = 0,
                      numberBands = 26,
                      weighting = 'linear', 
                      normalize = 'unit_max')
    pool = Pool()
    for frame in FrameGenerator(audio, frameSize = frameSize, hopSize = hopSize, startFromZero = True, validFrameThresholdRatio = 1):
        pool.add('TriangularBarkBands', mbands(spectrum(w(frame))))
    # Save results in a csv file. Use only for debugging purposes.
    # np.savetxt("out.csv", np.mean(np.log(pool['TriangularBarkBands']),0), delimiter=',')
    self.assertAlmostEqualVector( np.mean(np.log(pool['TriangularBarkBands']),0), expected,1e-2)