def main(inputFile='../../sounds/ocean.wav', H=256, stocf=.1):

	# ------- analysis parameters -------------------

	# inputFile: input sound file (monophonic with sampling rate of 44100)
	# H: hop size
	# stocf: decimation factor used for the stochastic approximation

	# --------- computation -----------------  

	# read input sound
	(fs, x) = UF.wavread(inputFile)

	# compute stochastic model                                          
	mYst = STM.stochasticModelAnal(x, H, stocf)             

	# synthesize sound from stochastic model
	y = STM.stochasticModelSynth(mYst, H)    

	outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_stochasticModel.wav'

	# write output sound
	UF.wavwrite(y, fs, outputFile)               

	# --------- plotting --------------------

	# create figure to plot
	plt.figure(figsize=(12, 9))

	# plot the input sound
	plt.subplot(3,1,1)
	plt.plot(np.arange(x.size)/float(fs), x)
	plt.axis([0, x.size/float(fs), min(x), max(x)])
	plt.ylabel('amplitude')
	plt.xlabel('time (sec)')
	plt.title('input sound: x')

	# plot stochastic representation
	plt.subplot(3,1,2)
	numFrames = int(mYst[:,0].size)
	frmTime = H*np.arange(numFrames)/float(fs)                             
	binFreq = np.arange(stocf*H)*float(fs)/(stocf*2*H)                      
	plt.pcolormesh(frmTime, binFreq, np.transpose(mYst))
	plt.autoscale(tight=True)
	plt.xlabel('time (sec)')
	plt.ylabel('frequency (Hz)')
	plt.title('stochastic approximation')

	# plot the output sound
	plt.subplot(3,1,3)
	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.tight_layout()
	plt.show()
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def extractHarmSpec(inputFile='../../sounds/ocean.wav',
                    H=256,
                    N=512,
                    stocf=.1):
    """
	inputFile: input sound file (monophonic with sampling rate of 44100)
	H: hop size, N: fft size
	stocf: decimation factor used for the stochastic approximation (bigger than 0, maximum 1)
	"""

    # read input sound
    (fs, x) = UF.wavread(inputFile)

    # compute stochastic model
    stocEnv = STM.stochasticModelAnal(x, H, N, stocf)

    # synthesize sound from stochastic model
    y = STM.stochasticModelSynth(stocEnv, H, N)

    outputFile = 'output_sounds/' + os.path.basename(
        inputFile)[:-4] + '_stochasticModel.wav'

    # write output sound
    UF.wavwrite(y, fs, outputFile)

    # create figure to plot
    plt.figure(figsize=(12, 9))

    # plot the input sound
    plt.subplot(3, 1, 1)
    plt.plot(np.arange(x.size) / float(fs), x)
    plt.axis([0, x.size / float(fs), min(x), max(x)])
    plt.ylabel('amplitude')
    plt.xlabel('time (sec)')
    plt.title('input sound: x')

    # plot stochastic representation
    plt.subplot(3, 1, 2)
    numFrames = int(stocEnv[:, 0].size)
    frmTime = H * np.arange(numFrames) / float(fs)
    binFreq = np.arange(stocf * N / 2) * float(fs) / (stocf * N)
    plt.pcolormesh(frmTime, binFreq, np.transpose(stocEnv))
    plt.autoscale(tight=True)
    plt.xlabel('time (sec)')
    plt.ylabel('frequency (Hz)')
    plt.title('stochastic approximation')

    # plot the output sound
    plt.subplot(3, 1, 3)
    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.tight_layout()
    plt.show()
Exemple #3
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def hpsModelSynth(hfreq, hmag, hphase, stocEnv, N, H, fs):
	"""
	Synthesis of a sound using the harmonic plus stochastic model
	hfreq, hmag: harmonic frequencies and amplitudes; stocEnv: stochastic envelope
	Ns: synthesis FFT size; H: hop size, fs: sampling rate 
	returns y: output sound, yh: harmonic component, yst: stochastic component
	"""

	yh = SM.sineModelSynth(hfreq, hmag, hphase, N, H, fs)          # synthesize harmonics
	yst = STM.stochasticModelSynth(stocEnv, H, H*2)                # synthesize stochastic residual
	y = yh[:min(yh.size, yst.size)]+yst[:min(yh.size, yst.size)]   # sum harmonic and stochastic components
	return y, yh, yst
Exemple #4
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def hpsModelSynth(hfreq, hmag, hphase, stocEnv, N, H, fs):
	"""
	Synthesis of a sound using the harmonic plus stochastic model
	hfreq, hmag: harmonic frequencies and amplitudes; stocEnv: stochastic envelope
	Ns: synthesis FFT size; H: hop size, fs: sampling rate 
	returns y: output sound, yh: harmonic component, yst: stochastic component
	"""

	yh = SM.sineModelSynth(hfreq, hmag, hphase, N, H, fs)          # synthesize harmonics
	yst = STM.stochasticModelSynth(stocEnv, H, H*2)                # synthesize stochastic residual
	y = yh[:min(yh.size, yst.size)]+yst[:min(yh.size, yst.size)]   # sum harmonic and stochastic components
	return y, yh, yst
Exemple #5
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def spsModelSynth(tfreq, tmag, tphase, stocEnv, N, H, fs):
	"""
	Synthesis of a sound using the sinusoidal plus stochastic model
	tfreq, tmag, tphase: sinusoidal frequencies, amplitudes and phases; stocEnv: stochastic envelope
	N: synthesis FFT size; H: hop size, fs: sampling rate 
	returns y: output sound, ys: sinusoidal component, yst: stochastic component
	"""

	ys = SM.sineModelSynth(tfreq, tmag, tphase, N, H, fs)          # synthesize sinusoids
	yst = STM.stochasticModelSynth(stocEnv, H, H*2)                # synthesize stochastic residual
	y = ys[:min(ys.size, yst.size)]+yst[:min(ys.size, yst.size)]   # sum sinusoids and stochastic components
	return y, ys, yst
def main(inputFile='../../sounds/ocean.wav', H=256, N=512, stocf=.1):
	"""
	inputFile: input sound file (monophonic with sampling rate of 44100)
	H: hop size, N: fft size
	stocf: decimation factor used for the stochastic approximation (bigger than 0, maximum 1)
	"""

	# read input sound
	(fs, x) = UF.wavread(inputFile)

	# compute stochastic model                                          
	stocEnv = STM.stochasticModelAnal(x, H, N, stocf)             

	# synthesize sound from stochastic model
	y = STM.stochasticModelSynth(stocEnv, H, N)    

	outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_stochasticModel.wav'

	# write output sound
	UF.wavwrite(y, fs, outputFile)               

	# create figure to plot
	plt.figure(figsize=(12, 9))

	# plot the input sound
	plt.subplot(3,1,1)
	plt.plot(np.arange(x.size)/float(fs), x)
	plt.axis([0, x.size/float(fs), min(x), max(x)])
	plt.ylabel('amplitude')
	plt.xlabel('time (sec)')
	plt.title('input sound: x')

	# plot stochastic representation
	plt.subplot(3,1,2)
	numFrames = int(stocEnv[:,0].size)
	frmTime = H*np.arange(numFrames)/float(fs)                             
	binFreq = np.arange(stocf*(N/2+1))*float(fs)/(stocf*N)                      
	plt.pcolormesh(frmTime, binFreq, np.transpose(stocEnv))
	plt.autoscale(tight=True)
	plt.xlabel('time (sec)')
	plt.ylabel('frequency (Hz)')
	plt.title('stochastic approximation')

	# plot the output sound
	plt.subplot(3,1,3)
	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.tight_layout()
	plt.show(block=False)
Exemple #7
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def spsModelSynth(tfreq, tmag, tphase, stocEnv, N, H, fs):
    """
	Synthesis of a sound using the sinusoidal plus stochastic model
	tfreq, tmag, tphase: sinusoidal frequencies, amplitudes and phases; stocEnv: stochastic envelope
	N: synthesis FFT size; H: hop size, fs: sampling rate 
	returns y: output sound, ys: sinusoidal component, yst: stochastic component
	"""

    ys = SM.sineModelSynth(tfreq, tmag, tphase, N, H,
                           fs)  # synthesize sinusoids
    #yst = STM.stochasticModelSynth(stocEnv, H, H*2)                # synthesize stochastic residual
    yst = STM.stochasticModelSynth(stocEnv, H, N)
    y = ys[:min(ys.size, yst.size)] + yst[:min(
        ys.size, yst.size)]  # sum sinusoids and stochastic components
    return y, ys, yst
Exemple #8
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def main(inputFile='../../sounds/ocean.wav', H=256, N=512, stocf=.1):
	"""
	inputFile: input sound file (monophonic with sampling rate of 44100)
	H: hop size, N: fft size
	stocf: decimation factor used for the stochastic approximation (bigger than 0, maximum 1)
	"""

	# read input sound
	(fs, x) = UF.wavread(inputFile)

	# compute stochastic model                                          
	stocEnv = STM.stochasticModelAnal(x, H, N, stocf)             

	# synthesize sound from stochastic model
	y = STM.stochasticModelSynth(stocEnv, H, N)    

	outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_stochasticModel.wav'

	# write output sound
	UF.wavwrite(y, fs, outputFile)      
	return x, fs, stocEnv, y
def extractHarmSpec(inputFile='../../sounds/rain.wav',
                    stocf=0.1,
                    timeScaling=np.array([0, 0, 1, 2])):
    """
	function to perform a time scaling using the stochastic model
	inputFile: name of input sound file
	stocf: decimation factor used for the stochastic approximation
	timeScaling: time scaling factors, in time-value pairs
	"""

    # hop size
    H = 128

    # read input sound
    (fs, x) = UF.wavread(inputFile)

    # perform stochastic analysis
    mYst = STC.stochasticModelAnal(x, H, H * 2, stocf)

    # perform time scaling of stochastic representation
    ystocEnv = STCT.stochasticTimeScale(mYst, timeScaling)

    # synthesize output sound
    y = STC.stochasticModelSynth(ystocEnv, H, H * 2)

    # write output sound
    outputFile = 'output_sounds/' + os.path.basename(
        inputFile)[:-4] + '_stochasticModelTransformation.wav'
    UF.wavwrite(y, fs, outputFile)

    # create figure to plot
    plt.figure(figsize=(12, 9))

    # plot the input sound
    plt.subplot(4, 1, 1)
    plt.plot(np.arange(x.size) / float(fs), x)
    plt.axis([0, x.size / float(fs), min(x), max(x)])
    plt.ylabel('amplitude')
    plt.xlabel('time (sec)')
    plt.title('input sound: x')

    # plot stochastic representation
    plt.subplot(4, 1, 2)
    numFrames = int(mYst[:, 0].size)
    frmTime = H * np.arange(numFrames) / float(fs)
    binFreq = np.arange(stocf * H) * float(fs) / (stocf * 2 * H)
    plt.pcolormesh(frmTime, binFreq, np.transpose(mYst))
    plt.autoscale(tight=True)
    plt.xlabel('time (sec)')
    plt.ylabel('frequency (Hz)')
    plt.title('stochastic approximation')

    # plot modified stochastic representation
    plt.subplot(4, 1, 3)
    numFrames = int(ystocEnv[:, 0].size)
    frmTime = H * np.arange(numFrames) / float(fs)
    binFreq = np.arange(stocf * H) * float(fs) / (stocf * 2 * H)
    plt.pcolormesh(frmTime, binFreq, np.transpose(ystocEnv))
    plt.autoscale(tight=True)
    plt.xlabel('time (sec)')
    plt.ylabel('frequency (Hz)')
    plt.title('modified stochastic approximation')

    # plot the output sound
    plt.subplot(4, 1, 4)
    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.tight_layout()
    plt.show()
def main(
    inputFile="../../sounds/bendir.wav",
    window="hamming",
    M=2001,
    N=2048,
    t=-80,
    minSineDur=0.02,
    maxnSines=150,
    freqDevOffset=10,
    freqDevSlope=0.001,
    stocf=0.2,
):

    # ------- analysis parameters -------------------

    # inputFile: input sound file (monophonic with sampling rate of 44100)
    # window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)
    # M: analysis window size
    # N: fft size (power of two, bigger or equal than M)
    # t: magnitude threshold of spectral peaks
    # minSineDur: minimum duration of sinusoidal tracks
    # maxnSines: maximum number of parallel sinusoids
    # freqDevOffset: frequency deviation allowed in the sinusoids from frame to frame at frequency 0
    # freqDevSlope: slope of the frequency deviation, higher frequencies have bigger deviation
    # stocf: decimation factor used for the stochastic approximation

    # size of fft used in synthesis
    Ns = 512

    # hop size (has to be 1/4 of Ns)
    H = 128

    # --------- computation -----------------

    # read input sound
    (fs, x) = UF.wavread(inputFile)

    # compute analysis window
    w = get_window(window, M)

    # perform sinusoidal analysis
    tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope)

    # subtract sinusoids from original sound
    Ns = 512
    xr = UF.sineSubtraction(x, Ns, H, tfreq, tmag, tphase, fs)

    # compute stochastic model of residual
    mYst = STM.stochasticModelAnal(xr, H, stocf)

    # synthesize sinusoids
    ys = SM.sineModelSynth(tfreq, tmag, tphase, Ns, H, fs)

    # synthesize stochastic component
    yst = STM.stochasticModelSynth(mYst, H)

    # sum sinusoids and stochastic
    y = yst[: min(yst.size, ys.size)] + ys[: min(yst.size, ys.size)]

    # output sound file (monophonic with sampling rate of 44100)
    outputFileSines = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_spsModel_sines.wav"
    outputFileStochastic = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_spsModel_stochastic.wav"
    outputFile = "output_sounds/" + os.path.basename(inputFile)[:-4] + "_spsModel.wav"

    # write sounds files for sinusoidal, residual, and the sum
    UF.wavwrite(ys, fs, outputFileSines)
    UF.wavwrite(yst, fs, outputFileStochastic)
    UF.wavwrite(y, fs, outputFile)

    # --------- plotting --------------------

    # plot stochastic component
    plt.figure(figsize=(12, 9))

    # frequency range to plot
    maxplotfreq = 10000.0

    # plot the input sound
    plt.subplot(3, 1, 1)
    plt.plot(np.arange(x.size) / float(fs), x)
    plt.axis([0, x.size / float(fs), min(x), max(x)])
    plt.ylabel("amplitude")
    plt.xlabel("time (sec)")
    plt.title("input sound: x")

    plt.subplot(3, 1, 2)
    numFrames = int(mYst[:, 0].size)
    sizeEnv = int(mYst[0, :].size)
    frmTime = H * np.arange(numFrames) / float(fs)
    binFreq = (0.5 * fs) * np.arange(sizeEnv * maxplotfreq / (0.5 * fs)) / sizeEnv
    plt.pcolormesh(frmTime, binFreq, np.transpose(mYst[:, : sizeEnv * maxplotfreq / (0.5 * fs) + 1]))
    plt.autoscale(tight=True)

    # plot sinusoidal frequencies on top of stochastic component
    sines = tfreq * np.less(tfreq, maxplotfreq)
    sines[sines == 0] = np.nan
    numFrames = int(sines[:, 0].size)
    frmTime = H * np.arange(numFrames) / float(fs)
    plt.plot(frmTime, sines, color="k", ms=3, alpha=1)
    plt.xlabel("time(s)")
    plt.ylabel("Frequency(Hz)")
    plt.autoscale(tight=True)
    plt.title("sinusoidal + stochastic spectrogram")

    # plot the output sound
    plt.subplot(3, 1, 3)
    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()
sys.path.append(
    os.path.join(os.path.dirname(os.path.realpath(__file__)),
                 '../../../software/models/'))

import utilFunctions as UF
import stochasticModel as STM
import stft as STFT

(fs, x) = UF.wavread('../../../sounds/ocean.wav')
w = np.hamming(512)
N = 512
H = 256
stocf = .1
mYst = STM.stochasticModelAnal(x, H, N, stocf)
y = STM.stochasticModelSynth(mYst, H, N)
mX, pX = STFT.stftAnal(x, w, N, H)

plt.figure(1, figsize=(9, 7))
plt.subplot(411)
plt.plot(np.arange(x.size) / float(fs), x, 'b')
plt.title('x (ocean.wav)')
plt.axis([0, x.size / float(fs), min(x), max(x)])

plt.subplot(412)
numFrames = int(mX[:, 0].size)
frmTime = H * np.arange(numFrames) / float(fs)
binFreq = np.arange(mX[0, :].size) * float(fs) / N
plt.pcolormesh(frmTime, binFreq, np.transpose(mX))
plt.title('mX; M=512, N=512, H=256')
plt.autoscale(tight=True)
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../models/"))

import stochasticModel as STC
import utilFunctions as UF


def stochasticTimeScale(stocEnv, timeScaling):
    # time scaling of the stochastic representation of a sound
    # stocEnv: stochastic envelope
    # timeScaling: scaling factors, in time-value pairs
    # returns ystocEnv: stochastic envelope
    L = stocEnv[:, 0].size  # number of input frames
    outL = int(L * timeScaling[-1] / timeScaling[-2])  # number of synthesis frames
    timeScalingEnv = interp1d(timeScaling[::2] / timeScaling[-2], timeScaling[1::2] / timeScaling[-1])
    indexes = (L - 1) * timeScalingEnv(np.arange(outL) / float(outL))
    ystocEnv = stocEnv[0, :]  # first output frame is same than input
    for l in indexes[1:]:
        ystocEnv = np.vstack((ystocEnv, stocEnv[round(l), :]))
    return ystocEnv


if __name__ == "__main__":
    (fs, x) = UF.wavread(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../sounds/rain.wav"))
    H = 256
    stocf = 0.2
    mYst = STC.stochasticModelAnal(x, H, stocf)
    timeScaling = np.array([0, 0, 1, 2])
    ystocEnv = stochasticTimeScale(mYst, timeScaling)
    y = STC.stochasticModelSynth(ystocEnv, H)
    UF.play(y, fs)
def main (inputFile='../../sounds/rain.wav', stocf=0.1, timeScaling = np.array([0, 0, 1, 2])):
	"""
	function to perform a time scaling using the stochastic model
	inputFile: name of input sound file
	stocf: decimation factor used for the stochastic approximation
	timeScaling: time scaling factors, in time-value pairs
	"""

	# hop size 
	H = 128

	# read input sound
	(fs, x) = UF.wavread(inputFile)

	# perform stochastic analysis
	mYst = STC.stochasticModelAnal(x, H, H*2, stocf)
	        
	# perform time scaling of stochastic representation
	ystocEnv = STCT.stochasticTimeScale(mYst, timeScaling)
	
	# synthesize output sound
	y = STC.stochasticModelSynth(ystocEnv, H, H*2)
	
	# write output sound
	outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_stochasticModelTransformation.wav'
	UF.wavwrite(y, fs, outputFile)

	# create figure to plot
	plt.figure(figsize=(12, 9))

	# plot the input sound
	plt.subplot(4,1,1)
	plt.plot(np.arange(x.size)/float(fs), x)
	plt.axis([0, x.size/float(fs), min(x), max(x)])
	plt.ylabel('amplitude')
	plt.xlabel('time (sec)')
	plt.title('input sound: x')

	# plot stochastic representation
	plt.subplot(4,1,2)
	numFrames = int(mYst[:,0].size)
	frmTime = H*np.arange(numFrames)/float(fs)                             
	binFreq = np.arange(stocf*H)*float(fs)/(stocf*2*H)                      
	plt.pcolormesh(frmTime, binFreq, np.transpose(mYst))
	plt.autoscale(tight=True)
	plt.xlabel('time (sec)')
	plt.ylabel('frequency (Hz)')
	plt.title('stochastic approximation')

	# plot modified stochastic representation
	plt.subplot(4,1,3)
	numFrames = int(ystocEnv[:,0].size)
	frmTime = H*np.arange(numFrames)/float(fs)                             
	binFreq = np.arange(stocf*H)*float(fs)/(stocf*2*H)                      
	plt.pcolormesh(frmTime, binFreq, np.transpose(ystocEnv))
	plt.autoscale(tight=True)
	plt.xlabel('time (sec)')
	plt.ylabel('frequency (Hz)')
	plt.title('modified stochastic approximation')

	# plot the output sound
	plt.subplot(4,1,4)
	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.tight_layout()
	plt.show()
import time
import sys, os

sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))

import utilFunctions as UF
import stochasticModel as STM
import stft as STFT
  
(fs, x) = UF.wavread('../../../sounds/ocean.wav')
w = np.hamming(512)
N = 512
H = 256
stocf = .1
mYst = STM.stochasticModelAnal(x, H, stocf)
y = STM.stochasticModelSynth(mYst, H)
mX, pX = STFT.stftAnal(x, fs, w, N, H)


plt.figure(1, figsize=(9, 7))
plt.subplot(411)
plt.plot(np.arange(x.size)/float(fs), x,'b')
plt.title('x (ocean.wav)')
plt.axis([0,x.size/float(fs),min(x),max(x)])

plt.subplot(412)
numFrames = int(mX[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)                             
binFreq = np.arange(N/2)*float(fs)/N                         
plt.pcolormesh(frmTime, binFreq, np.transpose(mX))
plt.title('mX; M=512, N=512, H=256')
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../software/models/'))
import utilFunctions as UF
import stochasticModel as STM

# Read input sound
(fs, x) = UF.wavread('../sounds/ocean.wav')

# Compute stochastic model
H = 128
stocf = .2
stocEnv = STM.stochasticModelAnal(x, H, H*2, stocf)

# Synthesize sound from stochastic model
y = STM.stochasticModelSynth(stocEnv, H, N)