Functions
	computeSpectrogram (x, f_s, afWindow=None, iBlockLength=4096, iHopLength=2048, bNormalize=True, bMagnitude=True)
	computes a spectrogram from the audio data

	computeSpectrogramCl (cPath)
	main

Variables
	parser = argparse.ArgumentParser(description='Compute key of wav file')

	metavar

	required

	help

	args = parser.parse_args()

	cPath = args.infile

Function Documentation

◆ computeSpectrogram()

computeSpectrogram	(	x,
		f_s,
		afWindow = None,
		iBlockLength = 4096,
		iHopLength = 2048,
		bNormalize = True,
		bMagnitude = True )

computes a spectrogram from the audio data

Parameters

x	array with floating point audio data (dimension samples x channels)
f_s	sample rate of audio data
afWindow	FFT window of length iBlockLength (default: hann), can be [] empty
iBlockLength	internal block length (default: 4096 samples)
iHopLength	internal hop length (default: 2048 samples)
bNormalize	normalize input audio file before fft computation (default: True)
bMagnitude	return magnitude instead of complex spectrum (default: True)

Returns: X: spectrum; f: frequencies of bins; t: time stamps

Definition at line 23 of file computeSpectrogram.py.

def computeSpectrogram(x, f_s, afWindow=None, iBlockLength=4096, iHopLength=2048, bNormalize=True, bMagnitude=True):
 
    iBlockLength = np.int_(iBlockLength)
    iHopLength = np.int_(iHopLength)
 
    # Pre-process: down-mix, normalize
    x = ToolPreprocAudio(x, bNormalize)
 
    if afWindow is None:
        # Compute window function for FFT
        afWindow = ToolComputeHann(iBlockLength)
 
    assert(afWindow.shape[0] == iBlockLength), "parameter error: invalid window dimension"
 
    # block audio data
    x_b, t = ToolBlockAudio(x, iBlockLength, iHopLength, f_s)
    
    # allocate memory
    iSpecDim = np.int_([(x_b.shape[1] / 2 + 1), x_b.shape[0]])
    X = np.zeros(iSpecDim)
    if not bMagnitude:
        X = X.astype(complex)
 
    norm = 2 / x_b.shape[1]
 
    for n in range(0, x_b.shape[0]):
        # windowed fft
        tmp = np.fft.fft(x_b[n, :] * afWindow) * norm
 
        # remove redundant spectrum parts
        if bMagnitude:
            X[:, n] = abs(tmp[range(iSpecDim[0])])
        else:
            X[:, n] = tmp[range(iSpecDim[0])]
 
    # let's be pedantic about normalization
    X[[0, iSpecDim[0]-1], :] = X[[0, iSpecDim[0]-1], :] / np.sqrt(2)
 
    f = np.arange(0, iSpecDim[0]) * f_s / iBlockLength
 
    return X, f, t
 

◆ computeSpectrogramCl()

computeSpectrogramCl ( cPath )

main

Definition at line 67 of file computeSpectrogram.py.

def computeSpectrogramCl(cPath):
    from pyACA.ToolReadAudio import ToolReadAudio
 
    # read audio file
    [f_s, x] = ToolReadAudio(cPath)
    
    # for debugging
    iBlockLength = 4096
    iHopLength = 2048
 
    # compute feature
    [X, f, t] = computeSpectrogram(x, f_s, None, iBlockLength, iHopLength)
 
    return X, f, t
 
 

Variable Documentation

◆ args

args = parser.parse_args()

Definition at line 92 of file computeSpectrogram.py.

◆ cPath

str cPath = args.infile

Definition at line 93 of file computeSpectrogram.py.

◆ help

help

Definition at line 89 of file computeSpectrogram.py.

◆ metavar

metavar

Definition at line 88 of file computeSpectrogram.py.

◆ parser

parser = argparse.ArgumentParser(description='Compute key of wav file')

Definition at line 87 of file computeSpectrogram.py.

◆ required

required