def defaultTest(): str_time = time.time() (fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/oboe-A4.wav')) w = np.hamming(511) N = 512 H = 256 y = stft(x, fs, w, N, H) print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/mridangam.wav'))
w = np.blackman(901)
N = 1024
t = -70
y, ys, yr = sprModel(x, fs, w, N, t)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest(): str_time = time.time() (fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/ocean.wav')) w = np.hamming(512) N = 512 H = 256 stocf = 0.5 y = stochasticModel(x, w, N, H, stocf) print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/mridangam.wav'))
w = np.blackman(601)
N = 2048
t = -70
stocf = 0.2
y, ys, yst = spsModel(x, fs, w, N, t, stocf)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/oboe.wav'))
w = np.hamming(511)
N = 512
t = -60
fig = plt.figure()
y = sineModel(x, fs, w, N, t)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/oboe-A4.wav'))
w = np.hamming(801)
N = 1024
H = 200
t = -70
y = stftPeaksModel(x, fs, w, N, H, t)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/sax-phrase-short.wav'))
w = np.blackman(701)
N = 1024
t = -80
nH = 30
minf0 = 400
maxf0 = 700
f0et = 5
maxhd = 0.2
y = harmonicModel(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/speech-female.wav'))
w = np.hamming(601)
N = 1024
t = -80
nH = 30
minf0 = 200
maxf0 = 500
f0et = 2
maxhd = 0.2
y = harmonicModel(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/speech-female.wav'))
w = np.hamming(801)
N = 1024
t = -120
maxnS = 30
stocf = 0.5
y, yh, ys = sps(x, fs, w, N, t, maxnS, stocf)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
fs, x = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/soprano-E4.wav'))
fs, x2 = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/violin-B3.wav'))
w = np.hamming(1025)
N = 2048
t = -150
nH = 200
minf0 = 100
maxf0 = 400
f0et = 5
maxhd = 0.2
stocf = 0.1
dur = x.size/fs
f0intp = np.array([[ 0, dur], [0, 1]])
htintp = np.array([[ 0, dur], [0, 1]])
rintp = np.array([[ 0, dur], [0, 1]])
y, yh, ys = hpsMorph(x, x2, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf, f0intp, htintp, rintp)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/sax-phrase-short.wav'))
w = np.blackman(801)
N = 1024
t = -90
nH = 50
minf0 = 350
maxf0 = 700
f0et = 10
maxhd = 0.2
stocf = 0.2
y, yh, yst = hpsModel(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../sounds/speech-female.wav"))
w = np.blackman(801)
N = 1024
t = -90
nH = 50
minf0 = 350
maxf0 = 700
f0et = 10
maxhd = 0.2
stocf = 0.5
maxnpeaksTwm = 5
y, yh, ys = hps(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf, maxnpeaksTwm)
print "time taken for computation " + str(time.time() - str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/oboe-A4.wav'))
w = np.blackman(801)
N = 1024
t = -80
nH = 30
minf0 = 400
maxf0 = 600
f0et = 5
maxhd = 0.2
stocf = 0.5
y, yh, ys = hpsModel(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/speech-female.wav'))
w = np.hamming(1025)
N = 1024
t = -120
nH = 30
minf0 = 200
maxf0 = 500
f0et = 5
maxhd = 0.2
stocf = 0.5
y, yh, ys = hpsModel(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf)
print "time taken for computation " + str(time.time()-str_time)
def defaultTest():
str_time = time.time()
#(fs, x) = wp.wavread('../../sounds/oboe.wav')
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/oboe.wav'))
w = np.hamming(1025)
N = 1024
t = -120
nH = 30
minf0 = 200
maxf0 = 500
f0et = 2
maxhd = 0.2
y, yh, yr = hprModel(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd)
print "time taken for computation " + str(time.time()-str_time)
N = 1024
t = -80
nH = 30
minf0 = 400
maxf0 = 600
f0et = 5
maxhd = 0.2
stocf = 0.5
y, yh, ys = hpsModel(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf)
print "time taken for computation " + str(time.time()-str_time)
if __name__ == '__main__':
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/oboe-A4.wav'))
w = np.blackman(801)
N = 1024
t = -80
nH = 30
minf0 = 400
maxf0 = 600
f0et = 5
maxhd = 0.2
stocf = 0.5
y, yh, ys = hpsModel(x[0:44100], fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf)
wp.play(y, fs)
wp.play(yh, fs)
wp.play(ys, fs)
final_peaks = hloc[ind]
parray = np.zeros([final_peaks.size,2])
parray[:,0]=pin/float(fs)
parray[:,1]=final_peaks*float(fs)/N
specPeaks = np.append(specPeaks, parray,axis=0)
pin += H
frmNum += 1
frmTime = np.array(frmTime) # The time at the centre of the frames
plt.hold(True)
plt.pcolormesh(frmTime,binFreq,YSpec)
plt.scatter(specPeaks[:,0]+(0.5*H/float(fs)), specPeaks[:,1], s=10, marker='x')
plt.xlabel('Time(s)')
plt.ylabel('Frequency(Hz)')
plt.autoscale(tight=True)
plt.show()
return YSpec
# example call of sineModelPlot function
if __name__ == '__main__':
(fs, x) = wp.wavread('../../../../sounds/sax-phrase-short.wav')
w = np.blackman(901)
N = 2048
t = -70
nH = 10
minf0 = 300
maxf0 = 650
f0et = 4
maxhd = 0.2
maxFreq = fs/15.0
YSpec = harmonicModelPlot(x,fs,w,N,t,nH, minf0, maxf0, f0et, maxhd, maxFreq)
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackmanharris
from scipy.fftpack import fft, ifft, fftshift
import math
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../code/spectralModels/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../code/basicFunctions/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../code/spectralModelsPlotting/'))
import hprModel, hpsModel, hprModelSpectrogramPlot
import smsWavplayer as wp
if __name__ == '__main__':
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../sounds/speech-male.wav'))
w = np.blackman(1001)
N = 1024
t = -90
nH = 100
minf0 = 60
maxf0 = 180
f0et = 3
maxhd = 0.2
maxFreq = 1000
start = 0*fs
end = x.size
maxnpeaksTwm = 5
stocf = .2
#y, yh, yr = hprModel.hprModel(x[start:end], fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, maxnpeaksTwm)
hprModelSpectrogramPlot.hprModelPlot(x[start:end], fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, maxFreq, maxnpeaksTwm)
ys[ri:ri+Ns] += sw*ysw # overlap-add for sines
yst[ri:ri+Ns] += sw*ystw # overlap-add for residual
pin += H # advance sound pointer
y = ys+yst # sum of sinusoidal and residual components
return y, ys, yst
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/mridangam.wav'))
w = np.blackman(601)
N = 2048
t = -70
stocf = 0.2
y, ys, yst = spsModel(x, fs, w, N, t, stocf)
print "time taken for computation " + str(time.time()-str_time)
if __name__ == '__main__':
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/mridangam.wav'))
w = np.blackman(601)
N = 2048
t = -70
stocf = 0.2
y, ys, yst = spsModel(x, fs, w, N, t, stocf)
wp.play(y, fs)
wp.play(ys, fs)
wp.play(yst, fs)
iploc, ipmag, ipphase = PP.peakInterp(mX, pX, ploc) # refine peak values by interpolation
#-----synthesis-----
plocs = iploc*Ns/N; # adapt peak locations to size of synthesis FFT
Y = GS.genSpecSines(plocs, ipmag, ipphase, Ns) # generate sines in the spectrum
fftbuffer = np.real( ifft(Y) ) # compute inverse FFT
yw[:hNs-1] = fftbuffer[hNs+1:] # undo zero-phase window
yw[hNs-1:] = fftbuffer[:hNs+1]
y[pin-hNs:pin+hNs] += sw*yw # overlap-add and apply a synthesis window
pin += H # advance sound pointer
return y
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/oboe-A4.wav'))
w = np.hamming(511)
N = 1024
t = -60
y = sineModel(x, fs, w, N, t)
print "time taken for computation " + str(time.time()-str_time)
# example call of sineModel function
if __name__ == '__main__':
(fs, x) = wp.wavread('../../sounds/oboe-A4.wav')
w = np.hamming(511)
N = 1024
t = -60
y = sineModel(x, fs, w, N, t)
wp.play(y, fs)
fftbuffer = np.real( ifft(Y) ) # inverse FFT
y[pin-hM:pin+hM] += H*ws*fftbuffer # overlap-add
pin += H
frmNum += 1
frmTime = np.array(frmTime) # The time at the centre of the frames
plt.figure(1)
plt.subplot(2,1,1)
plt.pcolormesh(frmTime,binFreq,XSpec)
plt.autoscale(tight=True)
plt.title('X spectrogram')
plt.subplot(2,1,2)
plt.pcolormesh(frmTime,binFreq,YSpec)
plt.autoscale(tight=True)
plt.title('X stochastic approx. spectrogram')
plt.show()
return y
# example call of stochasticModel function
if __name__ == '__main__':
(fs, x) = wp.wavread('../../sounds/ocean.wav')
w = np.hamming(1024)
N = 1024
H = 512
stocf = 0.3
maxFreq = fs
y = stochasticModelSpectrogramPlot(x, w, N, H, stocf, maxFreq)
wp.play(y, fs)
# -----synthesis-----
mY = resample(mXenv, hN) # interpolate to original size
pY = 2 * np.pi * np.random.rand(hN) # generate phase random values
Y = np.zeros(N, dtype=complex)
Y[:hN] = 10 ** (mY / 20) * np.exp(1j * pY) # generate positive freq.
Y[hN + 1 :] = 10 ** (mY[:0:-1] / 20) * np.exp(-1j * pY[:0:-1]) # generate negative freq.
fftbuffer = np.real(ifft(Y)) # inverse FFT
y = ws * fftbuffer * N / 2 # overlap-add
return mX, pX, mY, pY, y
# example call of stochasticModel function
if __name__ == "__main__":
(fs, x) = wp.wavread("../../../../sounds/ocean.wav")
w = np.hanning(1024)
N = 1024
stocf = 0.2
maxFreq = 10000.0
lastbin = N * maxFreq / fs
first = 1000
last = first + w.size
mX, pX, mY, pY, y = stochasticModel(x[first:last], w, N, stocf)
plt.figure(1)
plt.subplot(4, 1, 1)
plt.plot(np.arange(first, last) / float(fs), x[first:last] * w)
plt.axis([first / float(fs), last / float(fs), min(x[first:last] * w), max(x[first:last] * w)])
plt.title("xw")
plt.subplot(4, 1, 2)
minf0 = 100
maxf0 = 400
f0et = 5
maxhd = 0.2
stocf = 0.1
dur = x.size/fs
f0intp = np.array([[ 0, dur], [0, 1]])
htintp = np.array([[ 0, dur], [0, 1]])
rintp = np.array([[ 0, dur], [0, 1]])
y, yh, ys = hpsMorph(x, x2, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf, f0intp, htintp, rintp)
print "time taken for computation " + str(time.time()-str_time)
if __name__ == '__main__':
fs, x = wp.wavread('../../sounds/soprano-E4.wav')
fs, x2 = wp.wavread('../../sounds/violin-B3.wav')
w = np.hamming(1025)
N = 2048
t = -150
nH = 200
minf0 = 100
maxf0 = 400
f0et = 5
maxhd = 0.2
stocf = 0.1
dur = x.size/fs
f0intp = np.array([[ 0, dur], [0, 1]])
htintp = np.array([[ 0, dur], [0, 1]])
rintp = np.array([[ 0, dur], [0, 1]])
yhw[hNs-1:] = fftbuffer[:hNs+1]
fftbuffer = np.zeros(Ns)
fftbuffer = np.real(ifft(Yr)) # inverse FFT of residual spectrum
yrw[:hNs-1] = fftbuffer[hNs+1:] # undo zero-phase window
yrw[hNs-1:] = fftbuffer[:hNs+1]
yh[ri:ri+Ns] += sw*yhw # overlap-add for sines
yr[ri:ri+Ns] += sw*yrw # overlap-add for residual
y = yh+yr # sum of harmonic and residual components
return mX, hloc, hmag, mXr, mYh, mYr, mYs
if __name__ == '__main__':
(fs, x) = wp.wavread('../../../../sounds/flute-A4.wav')
w = np.blackman(601)
N = 2048
Ns = 512
t = -90
nH = 30
minf0 = 350
maxf0 = 600
f0et = 10
maxhd = 0.2
stocf = .2
maxFreq = 10000.0
lastbin = N*maxFreq/fs
first = 40000
last = first+w.size
mX, hloc, hmag, mXr, mYh, mYr, mYs = hprModelFrame(x[first:last], fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf)
import matplotlib.pyplot as plt
import numpy as np
from scipy.fftpack import fft
import smsWavplayer as wp
N = 1024
hN = N/2
M = 601
hM = (M+1)/2
w = np.blackman(M)
(fs, x) = wp.wavread('oboe.wav')
xw = x[40000:40000+M] * w
plt.figure(1)
fftbuffer = np.zeros(N)
fftbuffer[:hM] = xw[hM-1:]
fftbuffer[N-hM+1:] = xw[:hM-1]
plt.subplot(3,1,1)
plt.plot(np.arange(N), fftbuffer, 'b')
plt.axis([0, N, min(xw), max(xw)])
plt.title('fftbuffer')
X = fft(fftbuffer)
mX = 20*np.log10(abs(X[:hN]))
plt.subplot(3,1,2)
plt.plot(fs*np.arange(hN)/float(N),mX, 'r')
plt.axis([250,3300,-20,max(mX)])
plt.title('Magintude spectrum: abs(X)')
pX = np.unwrap(np.angle(X[0:hN]))
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackmanharris
from scipy.fftpack import fft, ifft, fftshift
import math
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../code/spectralModels/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../code/basicFunctions/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../code/spectralModelsPlotting/'))
import hprModel, hpsModel, hprModelSpectrogramPlot
import smsWavplayer as wp
if __name__ == '__main__':
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../sounds/sax-phrase.wav'))
w = np.blackman(801)
N = 1024
t = -90
nH = 50
minf0 = 340
maxf0 = 700
f0et = 7
maxhd = 0.2
maxFreq = 2000
start = 0*fs
end = x.size
maxnpeaksTwm = 5
stocf = .2
y, yh, yr = hprModel.hprModel(x[start:end], fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, maxnpeaksTwm)
y, yh, yst = hpsModel.hpsModel(x[start:end], fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf, maxnpeaksTwm)
w = np.blackman(801)
N = 1024
t = -90
nH = 50
minf0 = 350
maxf0 = 700
f0et = 10
maxhd = 0.2
stocf = 0.5
maxnpeaksTwm = 5
y, yh, ys = hps(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf, maxnpeaksTwm)
print "time taken for computation " + str(time.time() - str_time)
if __name__ == "__main__":
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../sounds/speech-female.wav"))
w = np.blackman(901)
N = 1024
t = -90
nH = 40
minf0 = 100
maxf0 = 400
f0et = 3
maxhd = 0.1
stocf = 0.5
maxnpeaksTwm = 5
y, yh, ys = hps(x, fs, w, N, t, nH, minf0, maxf0, f0et, maxhd, stocf, maxnpeaksTwm)
wp.play(y, fs)
wp.play(yh, fs)
wp.play(ys, fs)
def defaultTest():
str_time = time.time()
(fs, x) = wp.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../sounds/speech-female.wav'))
w = np.hamming(801)
N = 1024
t = -120
maxnS = 30
stocf = 0.5
y, yh, ys = spsTimescale(x, fs, w, N, t, maxnS, stocf)
print "time taken for computation " + str(time.time()-str_time)
if __name__ == '__main__':
(fs, x) = wp.wavread('../../sounds/speech-female.wav')
# wp.play(x, fs)
w = np.hamming(801)
N = 1024
t = -120
maxnS = 30
stocf = 0.5
y, yh, ys = spsTimescale(x, fs, w, N, t, maxnS, stocf)
wp.play(x, fs)
wp.play(y, fs)
# wp.play(yh, fs)
# wp.play(ys, fs)
