def defaultTest():
str_time = time.time()
(fs, x) = WIO.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)
import matplotlib.pyplot as plt
import numpy as np
import sys
import math
sys.path.append('../../../software/models/')
import waveIO as WIO
import dftAnal as DF
(fs, x) = WIO.wavread('../../../sounds/violin-B3.wav')
w = np.hamming(1024)
N = 1024
pin = 5000
hM1 = int(math.floor((w.size+1)/2))
hM2 = int(math.floor(w.size/2))
x1 = x[pin-hM1:pin+hM2]
mX, pX = DF.dftAnal(x1, w, N)
plt.figure(1, figsize=(9.5, 5))
plt.subplot(311)
plt.plot(np.arange(-hM1, hM2), x1, lw=1.5)
plt.axis([-hM1, hM2, min(x1), max(x1)])
plt.ylabel('amplitude')
plt.title('x (violin-B3.wav)')
plt.subplot(3,1,2)
plt.plot(np.arange(N/2), mX, 'r', lw=1.5)
plt.axis([0,N/2,-90,max(mX)])
plt.title ('magnitude spectrum: mX = 20*log10(abs(X))')
plt.ylabel('amplitude (dB)')
import matplotlib.pyplot as plt
import numpy as np
import sys
import math
sys.path.append('../../../software/models/')
import waveIO as WIO
import dftAnal as DF
(fs, x) = WIO.wavread('../../../sounds/violin-B3.wav')
w = np.hamming(1024)
N = 1024
pin = 5000
hM1 = int(math.floor((w.size + 1) / 2))
hM2 = int(math.floor(w.size / 2))
x1 = x[pin - hM1:pin + hM2]
mX, pX = DF.dftAnal(x1, w, N)
plt.figure(1, figsize=(9.5, 5))
plt.subplot(311)
plt.plot(np.arange(-hM1, hM2), x1, lw=1.5)
plt.axis([-hM1, hM2, min(x1), max(x1)])
plt.ylabel('amplitude')
plt.title('x (violin-B3.wav)')
plt.subplot(3, 1, 2)
plt.plot(np.arange(N / 2), mX, 'r', lw=1.5)
plt.axis([0, N / 2, -90, max(mX)])
plt.title('magnitude spectrum: mX = 20*log10(abs(X))')
plt.ylabel('amplitude (dB)')
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) = WIO.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) = WIO.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)
WIO.play(y, fs)
WIO.play(ys, fs)
WIO.play(yst, fs)
from scipy.signal import hamming, triang, blackmanharris
from scipy.fftpack import ifft, fftshift
import math
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/utilFunctions/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import sineModelAnal as SA
import sineModelSynth as SS
import waveIO as WIO
import sineSubtraction as SSub
(fs, x) = WIO.wavread('../../../sounds/carnatic.wav')
w = np.blackman(2001)
N = 2048
t = -90
minSineDur = .2
maxnSines = 200
freqDevOffset = 20
freqDevSlope = 0.02
Ns = 512
H = Ns/4
tfreq, tmag, tphase = SA.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope)
y = SS.sineModelSynth(tfreq, tmag, tphase, Ns, H, fs)
xr = SSub.sineSubtraction(x, Ns, H, tfreq, tmag, tphase, fs)
#mXr, pXr = ST.stftAnal(xr, fs, hamming(H*2), H*2, H)
import matplotlib.pyplot as plt
import numpy as np
import time, os, sys
import math
from scipy.signal import blackman
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/utilFunctions/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/utilFunctions_C/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import waveIO as WIO
import dftAnal as DF
(fs, x) = WIO.wavread('../../../sounds/piano.wav')
pin = .3*fs
w = np.blackman(1001)
N = 1024
hM1 = int(math.floor((w.size+1)/2))
hM2 = int(math.floor(w.size/2))
x1 = x[pin-hM1:pin+hM2]
mX, pX = DF.dftAnal(x1, w, N)
plt.figure(1)
plt.subplot(311)
plt.plot(np.arange(-hM1, hM2)/float(fs), x1, lw=1.5)
plt.axis([-hM1/float(fs), hM2/float(fs), min(x1), max(x1)])
plt.ylabel('amplitude')
plt.title('x (piano.wav)')
plt.subplot(3,1,2)
plt.plot(fs*np.arange(N/2)/float(N), mX, 'r', lw=1.5)
mXenv = resample(np.maximum(-200, mX), mX.size*stocf) # decimate the mag spectrum
pX = np.angle(X[:hN])
#-----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 = fftbuffer*N/2
return mX, pX, mY, pY, y
# example call of stochasticModel function
if __name__ == '__main__':
(fs, x) = WIO.wavread('../../../sounds/ocean.wav')
w = np.hanning(1024)
N = 1024
stocf = 0.1
maxFreq = 10000.0
lastbin = N*maxFreq/fs
first = 1000
last = first+w.size
mX, pX, mY, pY, y = stochasticModelFrame(x[first:last], w, N, stocf)
plt.figure(1)
plt.subplot(3,1,1)
plt.plot(np.arange(0, fs/2.0, fs/float(N)), mY, 'r', label="mY")
plt.axis([0, maxFreq, -80, max(mX)+3])
plt.title('magnitude spectrum (approx of mX)')
plt.subplot(3,1,2)
import numpy as np import matplotlib.pyplot as plt from scipy.signal import hamming, hanning, triang, blackmanharris, resample import math import sys, os, time sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/utilFunctions/')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/')) import stftAnal as STFT import waveIO as WIO import harmonicModelAnal as HA import sineSubtraction as SS (fs, x) = WIO.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../sounds/flute-A4.wav')) w = np.blackman(551) N = 1024 t = -100 nH = 40 minf0 = 420 maxf0 = 460 f0et = 5 maxnpeaksTwm = 5 minSineDur = .1 harmDevSlope = 0.01 Ns = 512 H = Ns/4 mX, pX = STFT.stftAnal(x, fs, w, N, H) hfreq, hmag, hphase = HA.harmonicModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, maxnpeaksTwm, minSineDur)
import matplotlib.pyplot as plt
import numpy as np
import sys
import math
sys.path.append('../../../software/basicFunctions/')
sys.path.append('../../../software/models/')
import waveIO as WIO
import dftAnal as DF
(fs, x) = WIO.wavread('../../../sounds/oboe-A4.wav')
w = np.hamming(511)
N = 512
pin = 5000
hM1 = int(math.floor((w.size+1)/2))
hM2 = int(math.floor(w.size/2))
x1 = x[pin-hM1:pin+hM2]
mX, pX = DF.dftAnal(x1, w, N)
plt.figure(1, figsize=(9.5, 7))
plt.subplot(311)
plt.plot(np.arange(-hM1, hM2), x1, lw=1.5)
plt.axis([-hM1, hM2, min(x1), max(x1)])
plt.ylabel('amplitude')
plt.title('x (oboe-A4.wav)')
plt.subplot(3,1,2)
plt.plot(np.arange(N/2), mX, 'r', lw=1.5)
plt.axis([0,N/2,min(mX),max(mX)])
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackmanharris
import math
from scipy.fftpack import fft, ifft, fftshift
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/basicFunctions/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftAnal as DFT
import waveIO as WIO
import peakProcessing as PP
import harmonicDetection as HD
import genSpecSines as GS
import twm as TWM
(fs, x) = WIO.wavread('../../../sounds/flute-A4.wav')
pos = .8*fs
M = 601
hM1 = int(math.floor((M+1)/2))
hM2 = int(math.floor(M/2))
w = np.hamming(M)
N = 1024
t = -100
nH = 40
minf0 = 420
maxf0 = 460
f0et = 5
maxnpeaksTwm = 5
minSineDur = .1
harmDevSlope = 0.01
Ns = 512
import matplotlib.pyplot as plt
import numpy as np
import sys
import math
sys.path.append('../../../software/basicFunctions/')
sys.path.append('../../../software/models/')
import waveIO as WIO
import dftAnal as DF
(fs, x) = WIO.wavread('../../../sounds/oboe-A4.wav')
w = np.hamming(511)
N = 512
pin = 5000
hM1 = int(math.floor((w.size + 1) / 2))
hM2 = int(math.floor(w.size / 2))
x1 = x[pin - hM1:pin + hM2]
mX, pX = DF.dftAnal(x1, w, N)
plt.figure(1, figsize=(9.5, 7))
plt.subplot(311)
plt.plot(np.arange(-hM1, hM2), x1, lw=1.5)
plt.axis([-hM1, hM2, min(x1), max(x1)])
plt.ylabel('amplitude')
plt.title('x (oboe-A4.wav)')
plt.subplot(3, 1, 2)
plt.plot(np.arange(N / 2), mX, 'r', lw=1.5)
plt.axis([0, N / 2, min(mX), max(mX)])
import numpy as np import matplotlib.pyplot as plt from scipy.signal import hamming, hanning, triang, blackmanharris, resample import math import sys, os, time sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/utilFunctions/')) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/')) import stftAnal as STFT import waveIO as WIO import hpsModelAnal as HA import hpsModelSynth as HS (fs, x) = WIO.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../sounds/sax-phrase-short.wav')) w = np.blackman(601) 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 = HA.hpsModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, maxnpeaksTwm, minSineDur, Ns, stocf)
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
import math
import sys, os, time
from scipy.fftpack import fft, ifft
import essentia.standard as ess
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/utilFunctions/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import waveIO as WIO
lpc = ess.LPC(order=14)
N= 512
(fs, x) = WIO.wavread('../../../sounds/soprano-E4.wav')
first = 20000
last = first+N
x1 = x[first:last]
X = fft(hamming(N)*x1)
mX = 20 * np.log10(abs(X[:N/2]))
coeff = lpc(x1)
Y = fft(coeff[0], N)
mY = 20 * np.log10(abs(Y[:N/2]))
plt.figure(1)
plt.subplot(2,1,1)
plt.plot(np.arange(first, last)/float(fs), x[first:last], 'b')
plt.axis([first/float(fs), last/float(fs), min(x[first:last]), max(x[first:last])])
