def stFeatureSpeed(signal, Fs, Win, Step):

    signal = numpy.double(signal)
    signal = (2.0 ** 15) / signal
    DC = signal.mean()
    MAX = (numpy.abs(signal)).max()
    signal = (signal - DC) / MAX
    # print (numpy.abs(signal)).max()

    N = len(signal)        # total number of signals
    curPos = 0
    countFrames = 0

    lowfreq = 133.33
    linsc = 200/3.
    logsc = 1.0711703
    nlinfil = 13
    nlogfil = 27
    nceps = 13
    nfil = nlinfil + nlogfil
    nfft = Win / 2
    if Fs < 8000:
        nlogfil = 5
        nfil = nlinfil + nlogfil
        nfft = Win / 2

    # compute filter banks for mfcc:
    [fbank, freqs] = mfccInitFilterBanks(Fs, nfft, lowfreq, linsc, logsc, nlinfil, nlogfil)

    numOfTimeSpectralFeatures = 8
    numOfHarmonicFeatures = 1
    totalNumOfFeatures = numOfTimeSpectralFeatures + nceps + numOfHarmonicFeatures
    #stFeatures = numpy.array([], dtype=numpy.float64)
    stFeatures = []

    while (curPos + Win - 1 < N):
        countFrames += 1
        x = signal[curPos:curPos + Win]
        curPos = curPos + Step
        X = abs(fft(x))
        X = X[0:nfft]
        X = X / len(X)
        Ex = 0.0
        El = 0.0
        X[0:4] = 0
#        M = numpy.round(0.016 * fs) - 1
#        R = numpy.correlate(frame, frame, mode='full')
        stFeatures.append(stHarmonic(x, Fs))
#        for i in range(len(X)):
            #if (i < (len(X) / 8)) and (i > (len(X)/40)):
            #    Ex += X[i]*X[i]
            #El += X[i]*X[i]
#        stFeatures.append(Ex / El)
#        stFeatures.append(numpy.argmax(X))
#        if curFV[numOfTimeSpectralFeatures+nceps+1]>0:
#            print curFV[numOfTimeSpectralFeatures+nceps], curFV[numOfTimeSpectralFeatures+nceps+1]
    return numpy.array(stFeatures)
dataset/ETHPy150Open tyiannak/pyAudioAnalysis/audioFeatureExtraction.py/stFeatureSpeed/swapped operands of `/`