import utilFunctions as UF
import stftTransformations as STFTT
import stochasticModel as STOC
import math
import stft as STFT
(fs, x1) = UF.wavread('../../../sounds/orchestra.wav')
(fs, x2) = UF.wavread('../../../sounds/speech-male.wav')
w1 = np.hamming(1024)
N1 = 1024
H1 = 256
w2 = np.hamming(1024)
N2 = 1024
smoothf = .2
balancef = 0.5
y = STFTT.stftMorph(x1, x2, fs, w1, N1, w2, N2, H1, smoothf, balancef)
mX2 = STOC.stochasticModelAnal(x2,H1,H1*2, smoothf)
mX,pX = STFT.stftAnal(x1, w1, N1, H1)
mY,pY = STFT.stftAnal(y, w1, N1, H1)
maxplotfreq = 10000.0
plt.figure(1, figsize=(12, 9))
plt.subplot(311)
numFrames = int(mX[:,0].size)
frmTime = H1*np.arange(numFrames)/float(fs)
binFreq = fs*np.arange(N1*maxplotfreq/fs)/N1
plt.pcolormesh(frmTime, binFreq, np.transpose(mX[:,:int(N1*maxplotfreq/fs+1)]))
plt.title('mX (orchestra.wav)')
plt.autoscale(tight=True)
plt.subplot(312)
import utilFunctions as UF
import stftTransformations as STFTT
import stochasticModel as STOC
import math
import stft as STFT
(fs, x1) = UF.wavread('../../../sounds/orchestra.wav')
(fs, x2) = UF.wavread('../../../sounds/speech-male.wav')
w1 = np.hamming(1024)
N1 = 1024
H1 = 256
w2 = np.hamming(1024)
N2 = 1024
smoothf = .2
balancef = 0.5
y = STFTT.stftMorph(x1, x2, fs, w1, N1, w2, N2, H1, smoothf, balancef)
mX2 = STOC.stochasticModelAnal(x2, H1, H1 * 2, smoothf)
mX, pX = STFT.stftAnal(x1, fs, w1, N1, H1)
mY, pY = STFT.stftAnal(y, fs, w1, N1, H1)
maxplotfreq = 10000.0
plt.figure(1, figsize=(12, 9))
plt.subplot(311)
numFrames = int(mX[:, 0].size)
frmTime = H1 * np.arange(numFrames) / float(fs)
binFreq = fs * np.arange(N1 * maxplotfreq / fs) / N1
plt.pcolormesh(frmTime, binFreq,
np.transpose(mX[:, :N1 * maxplotfreq / fs + 1]))
plt.title('mX (orchestra.wav)')
plt.autoscale(tight=True)
def main(inputFile1='../../sounds/ocean.wav',
inputFile2='../../sounds/speech-male.wav',
window1='hamming',
window2='hamming',
M1=1024,
M2=1024,
N1=1024,
N2=1024,
H1=256,
smoothf=.5,
balancef=0.2):
"""
Function to perform a morph between two sounds
inputFile1: name of input sound file to be used as source
inputFile2: name of input sound file to be used as filter
window1 and window2: windows for both files
M1 and M2: window sizes for both files
N1 and N2: fft sizes for both sounds
H1: hop size for sound 1 (the one for sound 2 is computed automatically)
smoothf: smoothing factor to be applyed to magnitude spectrum of sound 2 before morphing
balancef: balance factor between booth sounds, 0 is sound 1 and 1 is sound 2
"""
# read input sounds
(fs, x1) = UF.wavread(inputFile1)
(fs, x2) = UF.wavread(inputFile2)
# compute analysis windows
w1 = get_window(window1, M1)
w2 = get_window(window2, M2)
# perform morphing
y = STFTT.stftMorph(x1, x2, fs, w1, N1, w2, N2, H1, smoothf, balancef)
# compute the magnitude and phase spectrogram of input sound (for plotting)
mX1, pX1 = STFT.stftAnal(x1, w1, N1, H1)
# compute the magnitude and phase spectrogram of output sound (for plotting)
mY, pY = STFT.stftAnal(y, w1, N1, H1)
# write output sound
outputFile = 'output_sounds/' + os.path.basename(
inputFile1)[:-4] + '_stftMorph.wav'
UF.wavwrite(y, fs, outputFile)
# create figure to plot
plt.figure(figsize=(12, 9))
# frequency range to plot
maxplotfreq = 10000.0
# plot sound 1
plt.subplot(4, 1, 1)
plt.plot(np.arange(x1.size) / float(fs), x1)
plt.axis([0, x1.size / float(fs), min(x1), max(x1)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('input sound: x')
# plot magnitude spectrogram of sound 1
plt.subplot(4, 1, 2)
numFrames = int(mX1[:, 0].size)
frmTime = H1 * np.arange(numFrames) / float(fs)
binFreq = fs * np.arange(N1 * maxplotfreq / fs) / N1
plt.pcolormesh(frmTime, binFreq,
np.transpose(mX1[:, :int(N1 * maxplotfreq / fs) + 1]))
plt.xlabel('time (sec)')
plt.ylabel('frequency (Hz)')
plt.title('magnitude spectrogram of x')
plt.autoscale(tight=True)
# plot magnitude spectrogram of morphed sound
plt.subplot(4, 1, 3)
numFrames = int(mY[:, 0].size)
frmTime = H1 * np.arange(numFrames) / float(fs)
binFreq = fs * np.arange(N1 * maxplotfreq / fs) / N1
plt.pcolormesh(frmTime, binFreq,
np.transpose(mY[:, :int(N1 * maxplotfreq / fs) + 1]))
plt.xlabel('time (sec)')
plt.ylabel('frequency (Hz)')
plt.title('magnitude spectrogram of y')
plt.autoscale(tight=True)
# plot the morphed 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.title('output sound: y')
plt.tight_layout()
plt.show()
def main(
inputFile1="../../sounds/ocean.wav",
inputFile2="../../sounds/speech-male.wav",
window1="hamming",
window2="hamming",
M1=1024,
M2=1024,
N1=1024,
N2=1024,
H1=256,
smoothf=0.5,
balancef=0.2,
):
# function to perform a morph between two sounds
# inputFile1: name of input sound file to be used as source
# inputFile2: name of input sound file to be used as filter
# window1 and window2: windows for both files
# M1 and M2: window sizes for both files
# N1 and N2: fft sizes for both sounds
# H1: hop size for sound 1 (the one for sound 2 is computed automatically)
# smoothf: smoothing factor to be applyed to magnitude spectrum of sound 2 before morphing
# balancef: balance factor between booth sounds, 0 is sound 1 and 1 is sound 2
# read input sounds
(fs, x1) = UF.wavread(inputFile1)
(fs, x2) = UF.wavread(inputFile2)
# compute analysis windows
w1 = get_window(window1, M1)
w2 = get_window(window2, M2)
# compute the magnitude and phase spectrogram of output sound
mX1, pX1 = STFT.stftAnal(x1, fs, w1, N1, H1)
# perform morphing
y = STFTT.stftMorph(x1, x2, fs, w1, N1, w2, N2, H1, smoothf, balancef)
# compute the magnitude and phase spectrogram of output sound
mY, pY = STFT.stftAnal(y, fs, w1, N1, H1)
# write output sound
outputFile = "output_sounds/" + os.path.basename(inputFile1)[:-4] + "_stftMorph.wav"
UF.wavwrite(y, fs, outputFile)
# --------- plotting --------------------
# create figure to plot
plt.figure(figsize=(12, 9))
# frequency range to plot
maxplotfreq = 10000.0
# plot sound 1
plt.subplot(4, 1, 1)
plt.plot(np.arange(x1.size) / float(fs), x1)
plt.axis([0, x1.size / float(fs), min(x1), max(x1)])
plt.ylabel("amplitude")
plt.xlabel("time (sec)")
plt.title("input sound: x")
# plot magnitude spectrogram of sound 1
plt.subplot(4, 1, 2)
numFrames = int(mX1[:, 0].size)
frmTime = H1 * np.arange(numFrames) / float(fs)
binFreq = fs * np.arange(N1 * maxplotfreq / fs) / N1
plt.pcolormesh(frmTime, binFreq, np.transpose(mX1[:, : N1 * maxplotfreq / fs + 1]))
plt.xlabel("time (sec)")
plt.ylabel("frequency (Hz)")
plt.title("magnitude spectrogram of x")
plt.autoscale(tight=True)
# plot magnitude spectrogram of morphed sound
plt.subplot(4, 1, 3)
numFrames = int(mY[:, 0].size)
frmTime = H1 * np.arange(numFrames) / float(fs)
binFreq = fs * np.arange(N1 * maxplotfreq / fs) / N1
plt.pcolormesh(frmTime, binFreq, np.transpose(mY[:, : N1 * maxplotfreq / fs + 1]))
plt.xlabel("time (sec)")
plt.ylabel("frequency (Hz)")
plt.title("magnitude spectrogram of y")
plt.autoscale(tight=True)
# plot the morphed 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.title("output sound: y")
plt.tight_layout()
plt.show()
