def transformation_synthesis(inputFile, fs, tfreq, tmag, freqScaling = np.array([0, 2.0, 1, .3]),
timeScaling = np.array([0, .0, .671, .671, 1.978, 1.978+1.0])):
"""
Transform the analysis values returned by the analysis function and synthesize the sound
inputFile: name of input file; fs: sampling rate of input file
tfreq, tmag: sinusoidal frequencies and magnitudes
freqScaling: frequency scaling factors, in time-value pairs
timeScaling: time scaling factors, in time-value pairs
"""
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# frequency scaling of the sinusoidal tracks
ytfreq = ST.sineFreqScaling(tfreq, freqScaling)
# time scale the sinusoidal tracks
ytfreq, ytmag = ST.sineTimeScaling(ytfreq, tmag, timeScaling)
# synthesis
y = SM.sineModelSynth(ytfreq, ytmag, np.array([]), Ns, H, fs)
# write output sound
outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_sineModelTransformation.wav'
UF.wavwrite(y,fs, outputFile)
# create figure to plot
plt.figure(figsize=(12, 6))
# frequency range to plot
maxplotfreq = 15000.0
# plot the transformed sinusoidal frequencies
if (ytfreq.shape[1] > 0):
plt.subplot(2,1,1)
tracks = np.copy(ytfreq)
tracks = tracks*np.less(tracks, maxplotfreq)
tracks[tracks<=0] = np.nan
numFrames = int(tracks[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
plt.plot(frmTime, tracks)
plt.title('transformed sinusoidal tracks')
plt.autoscale(tight=True)
# plot the output sound
plt.subplot(2,1,2)
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 transformation_synthesis(inputFile,
fs,
tfreq,
tmag,
freqScaling=np.array([0, 2.0, 1, .3]),
timeScaling=np.array(
[0, .0, .671, .671, 1.978, 1.978 + 1.0])):
"""
Transform the analysis values returned by the analysis function and synthesize the sound
inputFile: name of input file; fs: sampling rate of input file
tfreq, tmag: sinusoidal frequencies and magnitudes
freqScaling: frequency scaling factors, in time-value pairs
timeScaling: time scaling factors, in time-value pairs
"""
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# frequency scaling of the sinusoidal tracks
ytfreq = ST.sineFreqScaling(tfreq, freqScaling)
# time scale the sinusoidal tracks
ytfreq, ytmag = ST.sineTimeScaling(ytfreq, tmag, timeScaling)
# synthesis
y = SM.sineModelSynth(ytfreq, ytmag, np.array([]), Ns, H, fs)
# write output sound
outputFile = 'output_sounds/' + os.path.basename(
inputFile)[:-4] + '_sineModelTransformation.wav'
UF.wavwrite(y, fs, outputFile)
# create figure to plot
plt.figure(figsize=(12, 6))
# frequency range to plot
maxplotfreq = 15000.0
# plot the transformed sinusoidal frequencies
if (ytfreq.shape[1] > 0):
plt.subplot(2, 1, 1)
tracks = np.copy(ytfreq)
tracks = tracks * np.less(tracks, maxplotfreq)
tracks[tracks <= 0] = np.nan
numFrames = int(tracks[:, 0].size)
frmTime = H * np.arange(numFrames) / float(fs)
plt.plot(frmTime, tracks)
plt.title('transformed sinusoidal tracks')
plt.autoscale(tight=True)
# plot the output sound
plt.subplot(2, 1, 2)
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)
import sineTransformations as SMT
(fs, x) = UF.wavread('../../../sounds/orchestra.wav')
w = np.hamming(801)
N = 2048
t = -90
minSineDur = .005
maxnSines = 150
freqDevOffset = 20
freqDevSlope = 0.02
Ns = 512
H = Ns/4
mX, pX = STFT.stftAnal(x, fs, w, N, H)
tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope)
freqScaling = np.array([0, .8, 1, 1.2])
ytfreq = SMT.sineFreqScaling(tfreq, freqScaling)
y = SM.sineModelSynth(ytfreq, tmag, np.array([]), Ns, H, fs)
mY, pY = STFT.stftAnal(y, fs, w, N, H)
UF.wavwrite(y,fs, 'sineModelFreqScale-orchestra.wav')
maxplotfreq = 4000.0
plt.figure(1, figsize=(9.5, 7))
plt.subplot(4,1,1)
plt.plot(np.arange(x.size)/float(fs), x, 'b')
plt.axis([0,x.size/float(fs),min(x),max(x)])
plt.title('x (orchestra.wav)')
plt.subplot(4,1,2)
numFrames = int(tfreq[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
import sineTransformations as SMT
(fs, x) = UF.wavread('../../../sounds/orchestra.wav')
w = np.hamming(801)
N = 2048
t = -90
minSineDur = .005
maxnSines = 150
freqDevOffset = 20
freqDevSlope = 0.02
Ns = 512
H = Ns//4
mX, pX = STFT.stftAnal(x, w, N, H)
tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope)
freqScaling = np.array([0, .8, 1, 1.2])
ytfreq = SMT.sineFreqScaling(tfreq, freqScaling)
y = SM.sineModelSynth(ytfreq, tmag, np.array([]), Ns, H, fs)
mY, pY = STFT.stftAnal(y, w, N, H)
UF.wavwrite(y,fs, 'sineModelFreqScale-orchestra.wav')
maxplotfreq = 4000.0
plt.figure(1, figsize=(9.5, 7))
plt.subplot(4,1,1)
plt.plot(np.arange(x.size)/float(fs), x, 'b')
plt.axis([0,x.size/float(fs),min(x),max(x)])
plt.title('x (orchestra.wav)')
plt.subplot(4,1,2)
numFrames = int(tfreq[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
