def METHOD_NAME(self):
    # Test the BFCC extractor compared to Rastamat specifications
    from numpy import mean
    frameSize = 1103
    hopSize = 441
    fftSize= 2048
    zeroPadding = fftSize - frameSize
    spectrumSize = int(fftSize/2) + 1
    #Expected values generated in Rastamat/MATLAB
    expected = array([ 174.2926614, 17.63766925, -12.01485808, -13.28523247,
                      -21.98320439, -27.65860448, -20.36286442, -11.31062965,
                      -3.406240974, -27.84575779, -27.37699052, -6.010315266, 
                      -14.08500904])
    #Scale into integer a la Rastamat/HTK
    audio = MonoLoader(filename = join(testdata.audio_dir, 'recorded/vignesh.wav'),
                       sampleRate = 44100)()*2**15     
    w = Windowing(type = 'hann', 
                  size = frameSize, 
                  zeroPadding = zeroPadding,
                  normalized = False,
                  zeroPhase = False)
    spectrum = Spectrum(size = fftSize)
    bfccEssentia = BFCC(inputSize = spectrumSize,
                        type = 'power', 
                        weighting = 'linear',
                        lowFrequencyBound = 0,
                        highFrequencyBound = 8000,
                        numberBands = 26,
                        numberCoefficients = 13,
                        normalize = 'unit_max',
                        dctType = 3,
                        logType = 'log',
                        liftering = 22)
    pool = Pool()
    for frame in FrameGenerator(audio, frameSize = frameSize, hopSize = hopSize , startFromZero = True, validFrameThresholdRatio = 1):
        pool.add("bfcc",bfccEssentia(spectrum(w(frame)))[1])
    self.assertAlmostEqualVector( mean(pool['bfcc'], 0), expected ,1.0e-2)    