pyACA.FeatureTimeAcfCoeff Namespace Reference

Functions
	FeatureTimeAcfCoeff (x, iBlockLength, iHopLength, f_s, eta=19)
	computes the ACF coefficients of a time domain signal

Function Documentation

FeatureTimeAcfCoeff()

FeatureTimeAcfCoeff	(		x,
			iBlockLength,
			iHopLength,
			f_s,
			eta = 19 )

computes the ACF coefficients of a time domain signal

Parameters

x	array with floating point audio data (dimension samples x channels)
iBlockLength	block length in samples
iHopLength	hop length in samples
f_s	sample rate of audio data
eta	index (or vector of indices) of coeff result

Returns

vacf: autocorrelation coefficient

t: time stamp

Definition at line 17 of file FeatureTimeAcfCoeff.py.

def FeatureTimeAcfCoeff(x, iBlockLength, iHopLength, f_s, eta=19):
 
    # create blocks
    xBlocks, t = pyACA.ToolBlockAudio(x, iBlockLength, iHopLength, f_s)
 
    # number of results
    iNumOfBlocks = xBlocks.shape[0]
    if np.isscalar(eta):
        iNumOfResultsPerBlock = 1
    else:
        iNumOfResultsPerBlock = eta.size
 
    # allocate memory
    vacf = np.zeros([iNumOfResultsPerBlock, iNumOfBlocks])
 
    for n, block in enumerate(xBlocks):
        # calculate the acf
        if not block.sum():
            vacf[np.arange(0, iNumOfResultsPerBlock), n] = np.zeros(iNumOfResultsPerBlock)
            continue
        else:
            afCorr = np.correlate(block, block, "full") / np.dot(block, block)
 
        # find the coefficients specified in eta
        vacf[np.arange(0, iNumOfResultsPerBlock), n] = afCorr[iBlockLength + eta]
 
    return vacf, t