def transformation_synthesis(inputFile1,
fs,
hfreq1,
hmag1,
stocEnv1,
inputFile2,
hfreq2,
hmag2,
stocEnv2,
hfreqIntp=np.array([0, 0, .1, 0, .9, 1, 1, 1]),
hmagIntp=np.array([0, 0, .1, 0, .9, 1, 1, 1]),
stocIntp=np.array([0, 0, .1, 0, .9, 1, 1, 1])):
"""
Transform the analysis values returned by the analysis function and synthesize the sound
inputFile1: name of input file 1
fs: sampling rate of input file 1
hfreq1, hmag1, stocEnv1: hps representation of sound 1
inputFile2: name of input file 2
hfreq2, hmag2, stocEnv2: hps representation of sound 2
hfreqIntp: interpolation factor between the harmonic frequencies of the two sounds, 0 is sound 1 and 1 is sound 2 (time,value pairs)
hmagIntp: interpolation factor between the harmonic magnitudes of the two sounds, 0 is sound 1 and 1 is sound 2 (time,value pairs)
stocIntp: interpolation factor between the stochastic representation of the two sounds, 0 is sound 1 and 1 is sound 2 (time,value pairs)
"""
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# morph the two sounds
yhfreq, yhmag, ystocEnv = HPST.hpsMorph(hfreq1, hmag1, stocEnv1, hfreq2,
hmag2, stocEnv2, hfreqIntp,
hmagIntp, stocIntp)
# synthesis
y, yh, yst = HPS.hpsModelSynth(yhfreq, yhmag, np.array([]), ystocEnv, Ns,
H, fs)
# write output sound
outputFile = 'output_sounds/' + os.path.basename(
inputFile1)[:-4] + '_hpsMorph.wav'
UF.wavwrite(y, fs, outputFile)
harmDevSlope2=0.01 Ns = 512 H = 128 (fs1, x1) = UF.wavread(inputFile1) (fs2, x2) = UF.wavread(inputFile2) w1 = get_window(window1, M1) w2 = get_window(window2, M2) hfreq1, hmag1, hphase1, stocEnv1 = HPS.hpsModelAnal(x1, fs1, w1, N1, H, t1, nH, minf01, maxf01, f0et1, harmDevSlope1, minSineDur1, Ns, stocf) hfreq2, hmag2, hphase2, stocEnv2 = HPS.hpsModelAnal(x2, fs2, w2, N2, H, t2, nH, minf02, maxf02, f0et2, harmDevSlope2, minSineDur2, Ns, stocf) hfreqIntp = np.array([0, 0, .1, 0, .9, 1, 1, 1]) hmagIntp = np.array([0, 0, .1, 0, .9, 1, 1, 1]) stocIntp = np.array([0, 0, .1, 0, .9, 1, 1, 1]) yhfreq, yhmag, ystocEnv = HPST.hpsMorph(hfreq1, hmag1, stocEnv1, hfreq2, hmag2, stocEnv2, hfreqIntp, hmagIntp, stocIntp) y, yh, yst = HPS.hpsModelSynth(yhfreq, yhmag, np.array([]), ystocEnv, Ns, H, fs1) UF.wavwrite(y,fs1, 'hps-morph-total.wav') plt.figure(figsize=(12, 9)) # frequency range to plot maxplotfreq = 15000.0 # plot spectrogram stochastic component of sound 1 plt.subplot(3,1,1) numFrames = int(stocEnv1[:,0].size) sizeEnv = int(stocEnv1[0,:].size) frmTime = H*np.arange(numFrames)/float(fs1)
def transformation_synthesis(inputFile1,
fs,
hfreq1,
hmag1,
stocEnv1,
inputFile2,
hfreq2,
hmag2,
stocEnv2,
hfreqIntp=np.array([0, 0, .1, 0, .9, 1, 1, 1]),
hmagIntp=np.array([0, 0, .1, 0, .9, 1, 1, 1]),
stocIntp=np.array([0, 0, .1, 0, .9, 1, 1, 1])):
"""
Transform the analysis values returned by the analysis function and synthesize the sound
inputFile1: name of input file 1
fs: sampling rate of input file 1
hfreq1, hmag1, stocEnv1: hps representation of sound 1
inputFile2: name of input file 2
hfreq2, hmag2, stocEnv2: hps representation of sound 2
hfreqIntp: interpolation factor between the harmonic frequencies of the two sounds, 0 is sound 1 and 1 is sound 2 (time,value pairs)
hmagIntp: interpolation factor between the harmonic magnitudes of the two sounds, 0 is sound 1 and 1 is sound 2 (time,value pairs)
stocIntp: interpolation factor between the stochastic representation of the two sounds, 0 is sound 1 and 1 is sound 2 (time,value pairs)
"""
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# morph the two sounds
yhfreq, yhmag, ystocEnv = HPST.hpsMorph(hfreq1, hmag1, stocEnv1, hfreq2,
hmag2, stocEnv2, hfreqIntp,
hmagIntp, stocIntp)
# synthesis
y, yh, yst = HPS.hpsModelSynth(yhfreq, yhmag, np.array([]), ystocEnv, Ns,
H, fs)
# write output sound
outputFile = 'output_sounds/' + os.path.basename(
inputFile1)[:-4] + '_hpsMorph.wav'
UF.wavwrite(y, fs, outputFile)
# create figure to plot
plt.figure(figsize=(12, 9))
# frequency range to plot
maxplotfreq = 15000.0
# plot spectrogram of transformed stochastic compoment
plt.subplot(2, 1, 1)
numFrames = int(ystocEnv[:, 0].size)
sizeEnv = int(ystocEnv[0, :].size)
frmTime = H * np.arange(numFrames) / float(fs)
binFreq = (.5 * fs) * np.arange(sizeEnv * maxplotfreq /
(.5 * fs)) / sizeEnv
plt.pcolormesh(
frmTime, binFreq,
np.transpose(ystocEnv[:, :int(sizeEnv * maxplotfreq / (.5 * fs)) + 1]))
plt.autoscale(tight=True)
# plot transformed harmonic on top of stochastic spectrogram
if (yhfreq.shape[1] > 0):
harms = np.copy(yhfreq)
harms = harms * np.less(harms, maxplotfreq)
harms[harms == 0] = np.nan
numFrames = int(harms[:, 0].size)
frmTime = H * np.arange(numFrames) / float(fs)
plt.plot(frmTime, harms, color='k', ms=3, alpha=1)
plt.xlabel('time (sec)')
plt.ylabel('frequency (Hz)')
plt.autoscale(tight=True)
plt.title('harmonics + stochastic spectrogram')
# plot the output sound
plt.subplot(2, 1, 2)
plt.plot(np.arange(y.size) / float(fs), y)
plt.axis([0, y.size / float(fs), min(y), max(y)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('output sound: y')
plt.tight_layout()
plt.show()
def transformation_synthesis(inputFile1, fs, hfreq1, hmag1, stocEnv1, inputFile2, hfreq2, hmag2, stocEnv2,
hfreqIntp = np.array([0, 0, .1, 0, .9, 1, 1, 1]), hmagIntp = np.array([0, 0, .1, 0, .9, 1, 1, 1]), stocIntp = np.array([0, 0, .1, 0, .9, 1, 1, 1])):
"""
Transform the analysis values returned by the analysis function and synthesize the sound
inputFile1: name of input file 1
fs: sampling rate of input file 1
hfreq1, hmag1, stocEnv1: hps representation of sound 1
inputFile2: name of input file 2
hfreq2, hmag2, stocEnv2: hps representation of sound 2
hfreqIntp: interpolation factor between the harmonic frequencies of the two sounds, 0 is sound 1 and 1 is sound 2 (time,value pairs)
hmagIntp: interpolation factor between the harmonic magnitudes of the two sounds, 0 is sound 1 and 1 is sound 2 (time,value pairs)
stocIntp: interpolation factor between the stochastic representation of the two sounds, 0 is sound 1 and 1 is sound 2 (time,value pairs)
"""
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# morph the two sounds
yhfreq, yhmag, ystocEnv = HPST.hpsMorph(hfreq1, hmag1, stocEnv1, hfreq2, hmag2, stocEnv2, hfreqIntp, hmagIntp, stocIntp)
# synthesis
y, yh, yst = HPS.hpsModelSynth(yhfreq, yhmag, np.array([]), ystocEnv, Ns, H, fs)
# write output sound
outputFile = 'output_sounds/' + os.path.basename(inputFile1)[:-4] + '_hpsMorph.wav'
UF.wavwrite(y, fs, outputFile)
# create figure to plot
plt.figure(figsize=(12, 9))
# frequency range to plot
maxplotfreq = 15000.0
# plot spectrogram of transformed stochastic compoment
plt.subplot(2,1,1)
numFrames = int(ystocEnv[:,0].size)
sizeEnv = int(ystocEnv[0,:].size)
frmTime = H*np.arange(numFrames)/float(fs)
binFreq = (.5*fs)*np.arange(sizeEnv*maxplotfreq/(.5*fs))/sizeEnv
plt.pcolormesh(frmTime, binFreq, np.transpose(ystocEnv[:,:sizeEnv*maxplotfreq/(.5*fs)+1]))
plt.autoscale(tight=True)
# plot transformed harmonic on top of stochastic spectrogram
if (yhfreq.shape[1] > 0):
harms = np.copy(yhfreq)
harms = harms*np.less(harms,maxplotfreq)
harms[harms==0] = np.nan
numFrames = int(harms[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
plt.plot(frmTime, harms, color='k', ms=3, alpha=1)
plt.xlabel('time (sec)')
plt.ylabel('frequency (Hz)')
plt.autoscale(tight=True)
plt.title('harmonics + stochastic spectrogram')
# plot the output sound
plt.subplot(2,1,2)
plt.plot(np.arange(y.size)/float(fs), y)
plt.axis([0, y.size/float(fs), min(y), max(y)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('output sound: y')
plt.tight_layout()
plt.show(block=False)
