def getCepsMatrixFromWavFile(filename):
'''
funkcja wyznacza z odczytanego pliku wav (ze sciezki filename) wspolczynniki
MFCC a potem je zwraca
'''
Fs = 44100.0; # sampling rate
t,y = PlotModule.readWav(filename, Fs)
##########
ceps_matrix = getCepsMatrixFromData(t,y)
return ceps_matrix
for i in range(giter):
'''''
if i < giter/4:
wr = W(map, QModel, 0.9, 1, 1, 0, True)
if i >= giter/4 and i <= giter * 3 / 4:
wr = W(map, QModel, 0.4, 1, 1, 0, True)
if i > giter * 3 / 4:
'''
wr = W(map, QModel, 0.1, 1, 1, True)
iter = wr.play()
plot_epoh.append(iter)
if i % 100 == 99:
print(i, iter)
#print( i * 0.8/(giter+1))
#mapgen.generateMap(map, 0.3 + i * 0.6/(giter+1))
randomnum = (random.random() - 0.5) * 0.5
#randomnum = 0
mapgen.generateMap(map, 0.7 + randomnum)
for i in QModel.state:
print(i, QModel.state[i])
plotBuilder = PlotModule.plotBilder()
plotBuilder.buildPlot(plot_epoh)
FileWR.FileWR.writeQ(QModel, 'Qmodel.txt')
animation = plot_animation.moveAnimation()
#mapgen.generateMap(map, 0.85)
wr = W(map, QModel, 0, 1, 11)
anim = wr.play(True)
animation.animate(map, anim, 'randMap')
# cooef = [1]*numCoefficients
len(signal) # Small data
filt = lazy_lpc.lpc.kautocor(signal, 2)
filt # The analysis filter
cooef = filt.numerator # List of coefficients
filt.error # Prediction error (squared!)
return cooef
if __name__ == '__main__':
i = 0
filename = "learn_set//wlacz//" + str(i + 1) + ".wav"
sampleRate, signal = PlotModule.readWav(filename, Fs)
rate = 22050
s, Hz = sHz(rate)
size = 512
table = signal #sin_table.harmonize({1: 1, 2: 5, 3: 3, 4: 2, 6: 9, 8: 1}).normalize()
data = table(str2freq("Bb3") * Hz).take(size)
filt = lpc(signal, order=14) # Analysis filter
gain = 1e-2 # Gain just for alignment with DFT
# Plots the synthesis filter
# - If blk is given, plots the block DFT together with the filter
# - If rate is given, shows the frequency range in Hz
(gain / filt).plot(blk=data, rate=rate, samples=1024, unwrap=False)
pylab.show()
vect_of_mccf[0] = 0
return vect_of_mccf
if __name__ == '__main__':
# ++++++++++++++++++++++++++++++++++++++++++++
for i in range(11):
print("please speak a word into the microphone")
filename = "learn_set//wlacz//"+str(i+1)+".wav"
# RecordModule.record_to_file(filename)
# print("done - result written to ", filename)
# filename = 'learn_set//wlacz//3.wav'
# ++++++++++++++++++++++++++++++++++++++++++++
t, extract = PlotModule.readWav(filename, FS)
extract = RecordModule.preemp(extract)
fr, wordspower, wordszeros, wordsdetect, ITL ,ITU, word_fr, word_y = RecordModule.detectSingleWord(t,extract)
ceps, mspec, spec = mfcc(word_y)
show_MFCC_spectrum(ceps)
# show_MFCC(ceps)
vect_of_mccf = np.zeros(len(ceps.T))
for i in range(len(ceps.T)):
vect_of_mccf[i] = max(ceps.T[i]) # sum(data.T[i]) #
# 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()
filt = lazy_lpc.lpc.kautocor(signal, 2)
filt # The analysis filter
cooef = filt.numerator # List of coefficients
filt.error # Prediction error (squared!)
return cooef
if __name__ == '__main__':
i = 0
filename = "learn_set//wlacz//"+str(i+1)+".wav"
sampleRate, signal = PlotModule.readWav(filename, Fs)
rate = 22050
s, Hz = sHz(rate)
size = 512
table = signal #sin_table.harmonize({1: 1, 2: 5, 3: 3, 4: 2, 6: 9, 8: 1}).normalize()
data = table(str2freq("Bb3") * Hz).take(size)
filt = lpc(signal, order=14) # Analysis filter
gain = 1e-2 # Gain just for alignment with DFT
# Plots the synthesis filter
# - If blk is given, plots the block DFT together with the filter
# - If rate is given, shows the frequency range in Hz
(gain / filt).plot(blk=data, rate=rate, samples=1024, unwrap=False)
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()
