def getCepsVect(y):
ceps, mspec, spec = mfcc(y)
vect_of_mccf = np.zeros(COEFS)
for j in range(COEFS):
vect_of_mccf[j] = RecordModule.arithmeticMean(ceps.T[j])
vect_of_mccf[0] = 0
return vect_of_mccf
# Compute the spectrum magnitude
spec = np.abs(fft(framed, nfft, axis=-1))
# Filter the spectrum through the triangle filterbank
mspec = np.log10(np.dot(spec, fbank.T))
# Use the DCT to 'compress' the coefficients (spectrum -> cepstrum domain)
ceps = dct(mspec, type=2, norm='ortho', axis=-1)[:, :nceps]
return ceps, mspec, spec
def preemp(input, p):
"""Pre-emphasis filter."""
return lfilter([1., -p], 1, input)
if __name__ == '__main__':
for i in range(10):
filename = "learn_set//wlacz//"+str(i+1)+".wav"
RATE = 44100.0
t,y = PlotModule.readWav(filename, RATE)
ceps, mspec, spec = mfcc(y)
print(ceps.shape)
vect_of_mccf = np.zeros(len(ceps.T))
for i in range(len(ceps.T)):
vect_of_mccf[i] = RecordModule.arithmeticMean(ceps.T[i]) # sum(data.T[i]) #
pylab.title("ceps : ")
pylab.plot(range(len(vect_of_mccf)), vect_of_mccf, 'g')
pylab.show()
mspec = np.log10(np.dot(spec, fbank.T))
# Use the DCT to 'compress' the coefficients (spectrum -> cepstrum domain)
ceps = dct(mspec, type=2, norm='ortho', axis=-1)[:, :nceps]
return ceps, mspec, spec
def preemp(input, p):
"""Pre-emphasis filter."""
return lfilter([1., -p], 1, input)
if __name__ == '__main__':
for i in range(10):
filename = "learn_set//wlacz//" + str(i + 1) + ".wav"
RATE = 44100.0
t, y = PlotModule.readWav(filename, RATE)
ceps, mspec, spec = mfcc(y)
print(ceps.shape)
vect_of_mccf = np.zeros(len(ceps.T))
for i in range(len(ceps.T)):
vect_of_mccf[i] = RecordModule.arithmeticMean(
ceps.T[i]) # sum(data.T[i]) #
pylab.title("ceps : ")
pylab.plot(range(len(vect_of_mccf)), vect_of_mccf, 'g')
pylab.show()
