pyACA.PitchTimeAcf Namespace Reference

Functions
	PitchTimeAcf (x, iBlockLength, iHopLength, f_s)
	computes f0 via the lag of the autocorrelation function

Function Documentation

PitchTimeAcf()

PitchTimeAcf	(		x,
			iBlockLength,
			iHopLength,
			f_s )

computes f0 via the lag of the autocorrelation function

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 17 of file PitchTimeAcf.py.

def PitchTimeAcf(x, iBlockLength, iHopLength, f_s):
 
    # initialize
    f_max = 2000
    fMinThresh = .35
 
    # block audio data
    x_b, t = ToolBlockAudio(x, iBlockLength, iHopLength, f_s)
    iNumOfBlocks = x_b.shape[0]
 
    # allocate memory
    f_0 = np.zeros(iNumOfBlocks)
 
    for n, block in enumerate(x_b):
        eta_min = np.floor(f_s / f_max).astype(int)
 
        # calculate the acf if non zero
        if not block.sum():
            continue
        else:
            afCorr = np.correlate(block, block, "full") / np.dot(block, block)
 
        afCorr = afCorr[np.arange(iBlockLength, afCorr.size)]
 
        # update eta_min to avoid main lobe
        eta_tmp = np.argmax(afCorr < fMinThresh)
        eta_min = np.max([eta_min, eta_tmp])
 
        afDeltaCorr = np.diff(afCorr)
        eta_tmp = np.argmax(afDeltaCorr > 0)
        eta_min = np.max([eta_min, eta_tmp])
 
        # find the coefficients specified in eta
        f_0[n] = np.argmax(afCorr[np.arange(eta_min + 1, afCorr.size)]) + 1
 
        # convert to Hz
        f_0[n] = f_s / (f_0[n] + eta_min + 1)
 
    return f_0, t