def plot_resynth(harmonics, fft, sample_rate):
freqs = [ stft.bin2hertz(i, sample_rate) for i in xrange(len(fft)) ]
synth_min_cutoff = 0.5 * min(numpy.where(fft>0, fft, 1))
length = len(fft)
for i, sp in enumerate(harmonics):
synthesized_peak = partials.qifft_synthesize(sp.fft_bin,
sp.mag, sp.curvature_a, length)
synthesized_peak = numpy.where(
synthesized_peak > synth_min_cutoff,
synthesized_peak, 0)
if i == 0:
pylab.plot(
[ f for f, m in zip(freqs, synthesized_peak) if m > 0.0 ],
stft.amplitude2db(
numpy.array(
[ m for f, m in zip(freqs, synthesized_peak) if m > 0.0 ],
)),
'.-', color="yellow", alpha=0.8,
label="resynthesized peak")
else:
pylab.plot(
[ f for f, m in zip(freqs, synthesized_peak) if m > 0.0 ],
stft.amplitude2db(
numpy.array(
[ m for f, m in zip(freqs, synthesized_peak) if m > 0.0 ],
)),
'.-', color="yellow", alpha=0.8)
def plot_resynth(harmonics, fft, sample_rate):
freqs = [stft.bin2hertz(i, sample_rate) for i in xrange(len(fft))]
synth_min_cutoff = 0.5 * min(numpy.where(fft > 0, fft, 1))
length = len(fft)
for i, sp in enumerate(harmonics):
synthesized_peak = partials.qifft_synthesize(sp.fft_bin, sp.mag,
sp.curvature_a, length)
synthesized_peak = numpy.where(synthesized_peak > synth_min_cutoff,
synthesized_peak, 0)
if i == 0:
pylab.plot(
[f for f, m in zip(freqs, synthesized_peak) if m > 0.0],
stft.amplitude2db(
numpy.array([
m for f, m in zip(freqs, synthesized_peak) if m > 0.0
], )),
'.-',
color="yellow",
alpha=0.8,
label="resynthesized peak")
else:
pylab.plot(
[f for f, m in zip(freqs, synthesized_peak) if m > 0.0],
stft.amplitude2db(
numpy.array([
m for f, m in zip(freqs, synthesized_peak) if m > 0.0
], )),
'.-',
color="yellow",
alpha=0.8)
def f0_snr(buf, f0, fs):
window = scipy.signal.gaussian(len(buf), len(buf)/8)
fft = scipy.fftpack.fft(buf*window)
fft_abs = abs(fft[:len(fft)/2+1])
cutoff_low_bin = float(20.0) / (fs/2) * (len(fft_abs)-1)
cutoff_bin = 1.95*float(f0) / (fs/2) * (len(fft_abs)-1)
low_bin = float(f0*0.95) / (fs/2) * (len(fft_abs)-1)
high_bin = float(f0*1.05) / (fs/2) * (len(fft_abs)-1)
freqs = numpy.array([ float(i) / (fs/2) * (len(fft_abs)-1) for
i in range(len(fft_abs)) ])
cutoff_bin = int(cutoff_bin)
fft_abs = fft_abs[:cutoff_bin]
freqs = freqs[:cutoff_bin]
fft_power = fft_abs**2
examine = fft_abs[low_bin:high_bin]**2
# multiply the FREQUENCY domain with a window to ensure
# that the maximum will be close to 660.
examine *= tukeywin(len(examine))
peak_index_within = numpy.argmax(examine)
peak_index = low_bin + peak_index_within
while not (fft_power[peak_index-1] < fft_power[peak_index] > fft_power[peak_index+1]):
# find the next-highest peak, whatever it is
peak_index += 1
if False:
pylab.plot(examine)
pylab.semilogy(peak_index_within,
examine[peak_index_within], 'o')
pylab.figure()
pylab.plot(fft_power)
pylab.semilogy(peak_index,
fft_power[peak_index], 'o')
pylab.show()
try:
peak_bin, peak_mag, curvature_a = partials.quadratic_interpolation(
peak_index, fft_abs)
except:
print "PROBLEM"
pylab.plot(examine)
pylab.semilogy(peak_index_within,
examine[peak_index_within], 'o')
pylab.figure()
pylab.semilogy(fft_power)
pylab.semilogy(peak_index,
fft_power[peak_index], 'o')
pylab.show()
exit(1)
#print peak_bin, peak_mag, curvature_a
length = len(fft_power)
synth = partials.qifft_synthesize(peak_bin, peak_mag, curvature_a, length)
residual = fft_abs - synth
synth_min = min(fft_abs)
residual.clip(synth_min)
consider_orig = fft_abs[cutoff_low_bin:]
freqs_orig = freqs[cutoff_low_bin:]
#consider_residual = fft_abs[cutoff_low_bin:low_bin]
#freqs_residual = freqs[cutoff_low_bin:low_bin]
consider_residual = numpy.append(
# #residual[cutoff_low_bin:low_bin],
# #residual[high_bin:])
fft_abs[cutoff_low_bin:low_bin],
fft_abs[high_bin:])
freqs_residual = numpy.append(
freqs[cutoff_low_bin:low_bin],
freqs[high_bin:])
#consider_residual = residual[cutoff_low_bin:low_bin]
flatness_orig = (scipy.stats.gmean(consider_orig)
/ consider_orig.mean() )
flatness_residual= (scipy.stats.gmean(consider_residual)
/ consider_residual.mean() )
#mean = scipy.stats.gmean(consider)
#print mean
value = flatness_orig / flatness_residual
#print
#print flatness_orig, flatness_residual, peak_mag
def rms(sig):
return numpy.sqrt( numpy.mean( sig**2 ) )
energy_orig = rms( consider_orig )
energy_sig = rms( fft_abs[low_bin:high_bin] )
energy_residual = rms( consider_residual )
#print energy_sig, energy_orig, energy_residual
value = energy_residual / energy_sig
#print value
if SHOW_F0_SNR:
pylab.semilogy(freqs, fft_abs, '-')
pylab.semilogy(peak_bin, peak_mag, 'o')
#pylab.semilogy(synth)
pylab.semilogy(freqs_orig, consider_orig)
pylab.semilogy(freqs_residual, consider_residual)
#pylab.axhline(mean)
pylab.show()
return value