w = np.blackman(1501)
N = 2048
t = -90
minf0 = 100
maxf0 = 300
f0et = 1
maxnpeaksTwm = 4
H = 128
x1 = x[1.5*fs:1.8*fs]
plt.figure(1, figsize=(9, 7))
mX, pX = STFT.stftAnal(x, fs, w, N, H)
f0 = HM.f0Twm(x, fs, w, N, H, t, minf0, maxf0, f0et)
f0 = UF.cleaningTrack(f0, 5)
yf0 = SM.sinewaveSynth(f0, np.array([0.4]), H, fs)
UF.play(yf0,fs)
f0[f0==0] = np.nan
maxplotfreq = 800.0
numFrames = int(f0.size)
frmTime = H*np.arange(numFrames)/float(fs)
binFreq = fs*np.arange(N*maxplotfreq/fs)/N
plt.pcolormesh(frmTime, binFreq, np.transpose(mX[:,:N*maxplotfreq/fs+1]))
plt.autoscale(tight=True)
plt.plot(frmTime, f0, linewidth=2, color='k')
plt.autoscale(tight=True)
plt.title('mX + f0 (piano.wav), TWM')
plt.tight_layout()
plt.show()
pin2 += H2 # advance sound pointer
y = np.delete(y, range(hM1_2)) # delete half of first window which was added in stftAnal
y = np.delete(y, range(y.size-hM1_1, y.size)) # add zeros at the end to analyze last sample
return y
# example call of stftMorph function
if __name__ == '__main__':
(fs, x1) = UF.wavread('../../sounds/ocean.wav')
(fs, x2) = UF.wavread('../../sounds/speech-male.wav')
w1 = np.hamming(1024)
N1 = 1024
H1 = 256
w2 = np.hamming(1024)
N2 = 1024
smoothf = .5
balancef = 0.2
y = stftMorph(x1, x2, fs, w1, N1, w2, N2, H1, smoothf, balancef)
w = np.hamming(1024)
N = 1024
H = 256
# design a band stop filter using a hanning window
startBin = int(N*2000.0/fs)
nBins = int(N*3000.0/fs)
bandstop = -np.hanning(nBins) * 40.0
filt = np.zeros(N/2)
filt[startBin:startBin+nBins] = bandstop
y = stftFiltering(y, fs, w, N, H, filt)
UF.play(y, fs)
maxnSines = 200
freqDevOffset = 10
freqDevSlope = 0.001
tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope)
xr = UF.sineSubtraction(x, N, H, tfreq, tmag, tphase, fs)
mXr, pXr = STFT.stftAnal(xr, fs, hamming(H*2), H*2, H)
Ns = 512
ys = SM.sineModelSynth(tfreq, tmag, tphase, Ns, H, fs)
plt.figure(1, figsize=(9.5, 7))
numFrames = int(mXr[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
binFreq = np.arange(H)*float(fs)/(H*2)
plt.pcolormesh(frmTime, binFreq, np.transpose(mXr))
plt.autoscale(tight=True)
tfreq[tfreq==0] = np.nan
numFrames = int(tfreq[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
plt.plot(frmTime, tfreq, color='k', ms=3, alpha=1)
plt.xlabel('Time(s)')
plt.ylabel('Frequency(Hz)')
plt.autoscale(tight=True)
plt.title('sinusoidal + residual components')
UF.play(ys, fs)
UF.play(xr, fs)
plt.tight_layout()
plt.show()
N = 1024 t = -100 nH = 100 minf0 = 350 maxf0 = 700 f0et = 5 maxnpeaksTwm = 5 minSineDur = .1 harmDevSlope = 0.01 Ns = 512 H = Ns/4 stocf = .2 hfreq, hmag, hphase, mYst = hpsModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur, Ns, stocf) y, yh, yst = hpsModelSynth(hfreq, hmag, hphase, mYst, Ns, H, fs) UF.play(y, fs) UF.play(yh, fs) UF.play(yst, fs) plt.figure(1, figsize=(9.5, 7)) maxplotfreq = 20000.0 numFrames = int(mYst[:,0].size) sizeEnv = int(mYst[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(mYst[:,:sizeEnv*maxplotfreq/(.5*fs)+1])) plt.autoscale(tight=True) harms = hfreq*np.less(hfreq,maxplotfreq) harms[harms==0] = np.nan numFrames = int(harms[:,0].size)
yhfreq[l,ind_valid] = yhfreq[l,ind_valid] * freqScaling[l] return yhfreq, yhmag if __name__ == '__main__': (fs, x) = UF.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/soprano-E4.wav')) w = np.blackman(801) N = 1024 t = -90 nH = 100 minf0 = 250 maxf0 = 400 f0et = 8 minSineDur = .1 harmDevSlope = 0.01 Ns = 512 H = Ns/4 hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur) freqScaling = np.array([0, 3, 1, .5]) freqStretching = np.array([]) timbrePreservation = 1 hfreqt, hmagt = harmonicFreqScaling(hfreq, hmag, freqScaling, freqStretching, timbrePreservation, fs) timeScaling = np.array([0, 0, 1, .5, 2, 4]) hfreqt, hmagt = ST.sineTimeScaling(hfreq, hmag, timeScaling) yh = SM.sineModelSynth(hfreqt, hmagt, np.array([]), Ns, H, fs) UF.play(yh, fs)
import math import sys, os, time sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../software/models/')) import stft as STFT import utilFunctions as UF import harmonicModel as HM (fs, x) = UF.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../sounds/cello-double.wav')) w = np.blackman(3501) N = 2048*2 t = -100 nH = 100 minf0 = 140 maxf0 = 150 f0et = 10 minSineDur = .2 harmDevSlope = 0.001 Ns = 512 H = Ns/4 hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur) y = HM.harmonicModelSynth(hfreq, hmag, hphase, Ns, H, fs) xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs) UF.play(x, fs) UF.play(y, fs) UF.play(xr, fs)
t = -100 nH = 100 minf0 = 350 maxf0 = 700 f0et = 5 maxnpeaksTwm = 5 minSineDur = .1 harmDevSlope = 0.01 Ns = 512 H = Ns/4 hfreq, hmag, hphase = HM.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur) xr = UF.sineSubtraction(x, Ns, H, hfreq, hmag, hphase, fs) mXr, pXr = STFT.stftAnal(xr, fs, hamming(Ns), Ns, H) yh = HM.harmonicModelSynth(hfreq, hmag, hphase, Ns, H, fs) UF.play(yh, fs) UF.play(xr, fs) plt.figure(1, figsize=(9.5, 7)) maxplotfreq = 20000.0 numFrames = int(mXr[:,0].size) frmTime = H*np.arange(numFrames)/float(fs) binFreq = fs*np.arange(Ns*maxplotfreq/fs)/Ns plt.pcolormesh(frmTime, binFreq, np.transpose(mXr[:,:Ns*maxplotfreq/fs+1])) plt.autoscale(tight=True) harms = hfreq*np.less(hfreq,maxplotfreq) harms[harms==0] = np.nan numFrames = int(harms[:,0].size) frmTime = H*np.arange(numFrames)/float(fs)
