pyACA.PitchTimeAuditory Namespace Reference

Functions
	PitchTimeAuditory (x, iBlockLength, iHopLength, f_s)
	computes f0 via the "auditory" approach

Function Documentation

PitchTimeAuditory()

PitchTimeAuditory	(		x,
			iBlockLength,
			iHopLength,
			f_s )

computes f0 via the "auditory" approach

Parameters

x	array with floating point audio data (dimension samples x channels)
iBlockLength	internal block length
iHopLength	internal hop length
f_s	sample rate of audio data

Returns

f_0: fundamental frequency (in Hz)

t: time stamp

Definition at line 20 of file PitchTimeAuditory.py.

def PitchTimeAuditory(x, iBlockLength, iHopLength, f_s):
 
    # initialize
    iNumOfBlocks = math.ceil(x.size / iHopLength)
    f_0 = np.zeros(iNumOfBlocks)
    f_max = 2000
    iNumBands = 20
    fLengthLpInS = 0.001
 
    iLengthLp = math.ceil(fLengthLpInS * f_s)
 
    # compute time stamps
    t = (np.arange(0, iNumOfBlocks) * iHopLength + (iBlockLength / 2)) / f_s
 
    # apply filterbank
    X = ToolGammatoneFb(x, f_s, iNumBands)
 
    # half wave rectification
    X[X < 0] = 0
 
    # smooth the results with a moving average filter
    b = np.ones(iLengthLp) / iLengthLp
    X = filtfilt(b, 1, X)
 
    for n in range(0, iNumOfBlocks):
 
        eta_min = int(round(f_s / f_max))
        afSumCorr = np.zeros(iBlockLength - 1)
        x_tmp = np.zeros(iBlockLength)
 
        i_start = n * iHopLength
        i_stop = np.min([x.size - 1, i_start + iBlockLength - 1])
 
        # compute ACF per band and summarize
        for k in range(0, iNumBands):
            # get current block
            if X[k, np.arange(i_start, i_stop + 1)].sum() < 1e-20:
                continue
            else:
                x_tmp[np.arange(0, i_stop - i_start + 1)] = X[k, np.arange(i_start, i_stop + 1)]
 
            afCorr = np.correlate(x_tmp, x_tmp, "full") / np.dot(x_tmp, x_tmp)
 
            # aggregate bands with simple sum before peak picking
            afSumCorr += afCorr[np.arange(iBlockLength, afCorr.size)]
 
        if afSumCorr.sum() < 1e-20:
            continue
 
        # find the highest local maximum
        iPeaks = find_peaks(afSumCorr, height=0)
        if iPeaks[0].size:
            eta_min = np.max([eta_min, iPeaks[0][0] - 1])
        f_0[n] = np.argmax(afSumCorr[np.arange(eta_min + 1, afSumCorr.size)]) + 1
 
        # convert to Hz
        f_0[n] = f_s / (f_0[n] + eta_min + 1)
 
    return f_0, t