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): """ 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 # read input sound (fs, x) = UF.wavread(inputFile) # compute analysis window w = get_window(window, M) # perform sinusoidal+sotchastic analysis tfreq, tmag, tphase, stocEnv = SPS.spsModelAnal(x, fs, w, N, H, t, minSineDur, maxnSines, freqDevOffset, freqDevSlope, stocf) # synthesize sinusoidal+stochastic model y, ys, yst = SPS.spsModelSynth(tfreq, tmag, tphase, stocEnv, Ns, H, fs) # 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) return x, fs, stocEnv, tfreq, y
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):
"""
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
# read input sound
(fs, x) = UF.wavread(inputFile)
# compute analysis window
w = get_window(window, M)
# perform sinusoidal+sotchastic analysis
tfreq, tmag, tphase, stocEnv = SPS.spsModelAnal(x, fs, w, N, H, t,
minSineDur, maxnSines,
freqDevOffset,
freqDevSlope, stocf)
# synthesize sinusoidal+stochastic model
y, ys, yst = SPS.spsModelSynth(tfreq, tmag, tphase, stocEnv, Ns, H, fs)
# 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)
# create figure to plot
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(stocEnv[:, 0].size)
sizeEnv = int(stocEnv[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(stocEnv[:, :sizeEnv * maxplotfreq / (.5 * fs) + 1]))
plt.autoscale(tight=True)
# plot sinusoidal frequencies on top of stochastic component
if (tfreq.shape[1] > 0):
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()
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):
"""
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
# read input sound
(fs, x) = UF.wavread(inputFile)
# compute analysis window
w = get_window(window, M)
# perform sinusoidal+sotchastic analysis
tfreq, tmag, tphase, stocEnv = SPS.spsModelAnal(x, fs, w, N, H, t, minSineDur, maxnSines, freqDevOffset, freqDevSlope, stocf)
# synthesize sinusoidal+stochastic model
y, ys, yst = SPS.spsModelSynth(tfreq, tmag, tphase, stocEnv, Ns, H, fs)
# 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)
# create figure to plot
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(stocEnv[:,0].size)
sizeEnv = int(stocEnv[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(stocEnv[:,:sizeEnv*maxplotfreq/(.5*fs)+1]))
plt.autoscale(tight=True)
# plot sinusoidal frequencies on top of stochastic component
if (tfreq.shape[1] > 0):
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(block=False)
