def get_bin_f0_B(bin_initial_estimate, fft_buffers,
noise_cutoff, bin_spread_below, bin_spread_above, fs):
### gradually discover/refine estimates of bin_f0 and B
bin_f0 = bin_initial_estimate
B = 1e-4
limit = defs.TOTAL_HARMONICS
for i in range(defs.B_INITIAL_PARTIALS, defs.TOTAL_HARMONICS):
#print fft_buffers.shape
bin_f0, B, rsquared, ok = align_with_B(i, bin_f0, B, fft_buffers,
noise_cutoff, bin_spread_below, bin_spread_above,
fs)
limit = stiff_ideal_conflict.find_limit(bin_f0, B,
bin_spread_below, bin_spread_above)
#print B, rsquared, limit
if i > limit:
#print "warning: exceeding lim?", i, limit
ok = False
if defs.B_PRINT_INDIVIDUAL_BS:
print "%i\t%.4f\t%.5e\t\t%i\t%i" % (
i, bin_f0, B, ok, 0)
if ok is False:
limit = i
break
rows, columns = fft_buffers.shape
partialss = []
idealss = []
for j in range(rows):
fft = fft_buffers[j]
#harmonics, ideal_harmonics = partials.get_freqs_mags(defs.TOTAL_HARMONICS, bin_f0, B,
harmonics, ideal_harmonics = partials.get_freqs_mags(limit, bin_f0, B,
fft, bin_spread_below, bin_spread_above,
only_peaks=True, Bsearch=True)
harmonics = [ h for h in harmonics if h.mag > noise_cutoff[h.fft_bin] ]
ideal_harmonics = [ h for h in ideal_harmonics if h.mag > noise_cutoff[h.fft_bin] ]
partialss.extend(harmonics)
idealss.extend(ideal_harmonics)
#if True:
if False:
pylab.figure()
for j in range(rows):
fft = fft_buffers[j]
pylab.plot(stft.amplitude2db(fft), color="orange")
xs = [ h.fft_bin for h in partialss]
#if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
ys = [ h.mag for h in partialss]
#if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
pylab.plot(xs, stft.amplitude2db(ys), 'p',
color="blue")
pylab.plot(stft.amplitude2db(noise_cutoff), color="black")
#ns = [ h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
#pylab.plot( ns,
# [ h.fft_bin - partials.mode_B2freq(bin_f0, h.n, B)
# for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ],
# '.')
pylab.show()
if defs.B_PLOT_FIT:
ns = [ h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
ys = [ h.fft_bin / h.n for h in partialss if h.n >
defs.B_MIN_HARMONIC_FIT_TO ]
omit_ns = [ h.n for h in partialss if h.n <= defs.B_MIN_HARMONIC_FIT_TO ]
omit_ys = [ h.fft_bin / h.n for h in partialss if h.n <=
defs.B_MIN_HARMONIC_FIT_TO ]
plots.plot_B_fit(ns, ys, omit_ns, omit_ys, bin_f0, B, fs, idealss)
pylab.show()
return bin_f0, B, rsquared, partialss, limit
def get_bin_f0_B(bin_initial_estimate, fft_buffers, noise_cutoff,
bin_spread_below, bin_spread_above, fs):
### gradually discover/refine estimates of bin_f0 and B
bin_f0 = bin_initial_estimate
B = 1e-4
limit = defs.TOTAL_HARMONICS
for i in range(defs.B_INITIAL_PARTIALS, defs.TOTAL_HARMONICS):
#print fft_buffers.shape
bin_f0, B, rsquared, ok = align_with_B(i, bin_f0, B, fft_buffers,
noise_cutoff, bin_spread_below,
bin_spread_above, fs)
limit = stiff_ideal_conflict.find_limit(bin_f0, B, bin_spread_below,
bin_spread_above)
#print B, rsquared, limit
if i > limit:
#print "warning: exceeding lim?", i, limit
ok = False
if defs.B_PRINT_INDIVIDUAL_BS:
print "%i\t%.4f\t%.5e\t\t%i\t%i" % (i, bin_f0, B, ok, 0)
if ok is False:
limit = i
break
rows, columns = fft_buffers.shape
partialss = []
idealss = []
for j in range(rows):
fft = fft_buffers[j]
#harmonics, ideal_harmonics = partials.get_freqs_mags(defs.TOTAL_HARMONICS, bin_f0, B,
harmonics, ideal_harmonics = partials.get_freqs_mags(limit,
bin_f0,
B,
fft,
bin_spread_below,
bin_spread_above,
only_peaks=True,
Bsearch=True)
harmonics = [h for h in harmonics if h.mag > noise_cutoff[h.fft_bin]]
ideal_harmonics = [
h for h in ideal_harmonics if h.mag > noise_cutoff[h.fft_bin]
]
partialss.extend(harmonics)
idealss.extend(ideal_harmonics)
#if True:
if False:
pylab.figure()
for j in range(rows):
fft = fft_buffers[j]
pylab.plot(stft.amplitude2db(fft), color="orange")
xs = [h.fft_bin for h in partialss]
#if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
ys = [h.mag for h in partialss]
#if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
pylab.plot(xs, stft.amplitude2db(ys), 'p', color="blue")
pylab.plot(stft.amplitude2db(noise_cutoff), color="black")
#ns = [ h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ]
#pylab.plot( ns,
# [ h.fft_bin - partials.mode_B2freq(bin_f0, h.n, B)
# for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO ],
# '.')
pylab.show()
if defs.B_PLOT_FIT:
ns = [h.n for h in partialss if h.n > defs.B_MIN_HARMONIC_FIT_TO]
ys = [
h.fft_bin / h.n for h in partialss
if h.n > defs.B_MIN_HARMONIC_FIT_TO
]
omit_ns = [h.n for h in partialss if h.n <= defs.B_MIN_HARMONIC_FIT_TO]
omit_ys = [
h.fft_bin / h.n for h in partialss
if h.n <= defs.B_MIN_HARMONIC_FIT_TO
]
plots.plot_B_fit(ns, ys, omit_ns, omit_ys, bin_f0, B, fs, idealss)
pylab.show()
return bin_f0, B, rsquared, partialss, limit
def estimate_f0_B(filenames):
### ASSUME: all filenames are of the same instrument-string
wav_filename = filenames[0]
basename='-'.join(os.path.basename(wav_filename).split('-')[0:3])
if basename.startswith("test-440f"):
return 440.0, 0, 1, 1, 1, 1
### get initial f0 estimate
base_frequency_estimate = expected_frequencies.get_freq_from_filename(
wav_filename)
### get noise
initial_noise_floor, initial_noise_freqs, _, _, _ = calc_noise.get_noise(wav_filename)
noise_cutoff = stft.db2amplitude(
stft.amplitude2db(initial_noise_floor)+defs.B_MINIMUM_HARMONIC_SNR)
#### get FFT frames from audio files
sample_rate = None
freqs = None
estimate_B_buffers_list = []
for wav_i, wav_filename in enumerate(filenames):
#print wav_filename
#window_buffer, sample_rate = stft.get_long_buffer_from_file(wav_filename,
window_buffers, sample_rate = stft.get_buffers_from_file(wav_filename,
(defs.B_NUM_BUFFERS_ESTIMATE))
if freqs is None:
freqs = [ stft.bin2hertz(i, sample_rate)
for i in range(stft.WINDOWSIZE/2+1) ]
estimate_B_buffers_this_list = []
#fft_amplitude = stft.fft_amplitude(window_buffer, sample_rate)
#estimate_B_buffers_this_list.append(fft_amplitude)
for window_number in range(defs.B_NUM_BUFFERS_ESTIMATE):
window_buffer = window_buffers[window_number]
fft_amplitude = stft.stft_amplitude(window_buffer)
estimate_B_buffers_this_list.append(fft_amplitude)
estimate_B_buffers_list.extend(estimate_B_buffers_this_list)
estimate_B_buffers = numpy.array(estimate_B_buffers_list)
### radius of search area for peaks
# used with STFT only
bin_initial_estimate = stft.hertz2bin(base_frequency_estimate,
sample_rate)
#bin_initial_estimate = (base_frequency_estimate
# * fft_amplitude.shape[0] / (sample_rate/2)
# )
#print bin_initial_estimate
bin_spread_below = int(math.ceil(abs(
stft.hertz2bin(
(1.0-defs.B_PEAK_SPREAD_BELOW_HERTZ)*base_frequency_estimate,
sample_rate) - bin_initial_estimate)))
bin_spread_above = int(math.ceil(
stft.hertz2bin(
(1.0+defs.B_PEAK_SPREAD_ABOVE_HERTZ)*base_frequency_estimate,
sample_rate) - bin_initial_estimate))
#bin_spread_below = int(round(bin_initial_estimate *
# defs.B_PEAK_SPREAD_BELOW_HERTZ))
#bin_spread_above = int(round(bin_initial_estimate *
# defs.B_PEAK_SPREAD_BELOW_HERTZ))
#bin_spread_below_main = int(
# stft.hertz2bin(defs.STFT_PEAK_SPREAD_BELOW_HERTZ*base_frequency_estimate,
# sample_rate))
#bin_spread_above_main = int(
# stft.hertz2bin(defs.STFT_PEAK_SPREAD_ABOVE_HERTZ*base_frequency_estimate,
# sample_rate))
### actual estimate
bin_f0, B, rsquared, harmonics, limit = get_bin_f0_B(
bin_initial_estimate,
estimate_B_buffers, noise_cutoff,
#estimate_B_buffers, numpy.zeros(defs.LONG_WINDOWSIZE+1),
bin_spread_below, bin_spread_above, sample_rate)
highest_harmonic = 0
for h in harmonics:
if highest_harmonic < h.n:
highest_harmonic = h.n
limit = min(limit, highest_harmonic)
# HACK: remove limit
#limit = defs.TOTAL_HARMONICS
#print "limit to:", limit
#harmonics_enable = [True]*defs.TOTAL_HARMONICS
harmonics_enable = [True]*limit
bins_estimate = [ partials.mode_B2freq(bin_f0, i, B) for
i in range(1,len(harmonics_enable)+1) ]
bins_naive = [ i*bin_f0 for
i in range(1,len(harmonics_enable)+1) ]
if defs.B_PLOT:
pylab.figure()
pylab.plot(initial_noise_freqs,
stft.amplitude2db(initial_noise_floor), color='black')
#pylab.plot(initial_noise_freqs,
# stft.amplitude2db(initial_noise_floor)+defs.B_MINIMUM_HARMONIC_SNR,
# color='black')
pylab.xlabel("Frequency (seconds)")
pylab.ylabel("Power (/ dB)")
for i in range(estimate_B_buffers.shape[0]):
#color = matplotlib.cm.spring(float(wav_i)/len(filenames))
#color = matplotlib.cm.RdYlGn(
#color = matplotlib.cm.spring(
# float(i)/len(estimate_B_buffers_this_list))
pylab.plot(freqs,
stft.amplitude2db(estimate_B_buffers[i,:]),
#color=color,
color="orange",
alpha=0.5,
label=basename,
)
for est in bins_estimate:
low = stft.bin2hertz(est - bin_spread_below, sample_rate)
high = stft.bin2hertz(est + bin_spread_above, sample_rate)
if True:
pylab.axvspan(low, high, color='c', alpha=0.3)
else:
pylab.axvline(stft.bin2hertz(est, sample_rate),
color='cyan', alpha=0.3,
#linewidth=2.0
)
for naive in bins_naive:
freq = stft.bin2hertz(naive, sample_rate)
pylab.axvline(freq, color='grey', alpha=0.2,
#linewidth=2.0
)
for j, harm in enumerate(harmonics):
if harm.mag == 0:
continue
fn = stft.bin2hertz(harm.fft_bin, sample_rate)
mag = stft.amplitude2db(harm.mag)
#pylab.plot(fn, mag, 'o',
# color='green'
# )
pylab.xlabel("Frequency")
pylab.ylabel("Decibels")
if defs.B_DUMP_HARMS:
t_fns = []
t_mags = []
for j, harm in enumerate(harmonics):
if harm.mag == 0:
continue
fn = stft.bin2hertz(harm.fft_bin, sample_rate)
mag = stft.amplitude2db(harm.mag)
t_fns.append(fn)
t_mags.append(mag)
data = numpy.vstack((t_fns, t_mags)).transpose()
numpy.savetxt("B-harms.txt", data)
if defs.B_PLOT:
pylab.show()
f0 = stft.bin2hertz(bin_f0, sample_rate)
stiff_ideal_limit = stiff_ideal_conflict.find_limit(bin_f0, B,
bin_spread_below, bin_spread_above)
lim = min(stiff_ideal_limit, limit)
detected_freqs = StringFreqsB(f0, B, lim)
stats = StringFreqsB_stats()
stats.num_files = len(filenames)
stats.rsquared = rsquared
stats.highest_mode_detected = limit
stats.highest_mode_stiff_ideal = stiff_ideal_limit
stats.basename = basename
adjusted_B, delta_fn = adjust_B.adjust(basename, limit, f0, B)
if adjusted_B is not None:
stiff_ideal_lim_adjusted = stiff_ideal_conflict.find_limit(
bin_f0, adjusted_B,
bin_spread_below, bin_spread_above)
lim = min(stiff_ideal_lim_adjusted, limit)
adjusted_freqs = StringFreqsB(f0, adjusted_B, lim)
adjusted_freqs.delta_fn = delta_fn
stats.highest_mode_stiff_ideal_adjusted = stiff_ideal_lim_adjusted
stats.delta_fn = delta_fn
final = StringFreqsB(f0, adjusted_B,
min(stats.highest_mode_detected,
stats.highest_mode_stiff_ideal,
stiff_ideal_lim_adjusted))
else:
adjusted_freqs = None
final = StringFreqsB(f0, B,
min(stats.highest_mode_detected,
stats.highest_mode_stiff_ideal))
return detected_freqs, adjusted_freqs, stats, final
def estimate_f0_B(filenames):
### ASSUME: all filenames are of the same instrument-string
wav_filename = filenames[0]
basename = '-'.join(os.path.basename(wav_filename).split('-')[0:3])
if basename.startswith("test-440f"):
return 440.0, 0, 1, 1, 1, 1
### get initial f0 estimate
base_frequency_estimate = expected_frequencies.get_freq_from_filename(
wav_filename)
### get noise
initial_noise_floor, initial_noise_freqs, _, _, _ = calc_noise.get_noise(
wav_filename)
noise_cutoff = stft.db2amplitude(
stft.amplitude2db(initial_noise_floor) + defs.B_MINIMUM_HARMONIC_SNR)
#### get FFT frames from audio files
sample_rate = None
freqs = None
estimate_B_buffers_list = []
for wav_i, wav_filename in enumerate(filenames):
#print wav_filename
#window_buffer, sample_rate = stft.get_long_buffer_from_file(wav_filename,
window_buffers, sample_rate = stft.get_buffers_from_file(
wav_filename, (defs.B_NUM_BUFFERS_ESTIMATE))
if freqs is None:
freqs = [
stft.bin2hertz(i, sample_rate)
for i in range(stft.WINDOWSIZE / 2 + 1)
]
estimate_B_buffers_this_list = []
#fft_amplitude = stft.fft_amplitude(window_buffer, sample_rate)
#estimate_B_buffers_this_list.append(fft_amplitude)
for window_number in range(defs.B_NUM_BUFFERS_ESTIMATE):
window_buffer = window_buffers[window_number]
fft_amplitude = stft.stft_amplitude(window_buffer)
estimate_B_buffers_this_list.append(fft_amplitude)
estimate_B_buffers_list.extend(estimate_B_buffers_this_list)
estimate_B_buffers = numpy.array(estimate_B_buffers_list)
### radius of search area for peaks
# used with STFT only
bin_initial_estimate = stft.hertz2bin(base_frequency_estimate, sample_rate)
#bin_initial_estimate = (base_frequency_estimate
# * fft_amplitude.shape[0] / (sample_rate/2)
# )
#print bin_initial_estimate
bin_spread_below = int(
math.ceil(
abs(
stft.hertz2bin((1.0 - defs.B_PEAK_SPREAD_BELOW_HERTZ) *
base_frequency_estimate, sample_rate) -
bin_initial_estimate)))
bin_spread_above = int(
math.ceil(
stft.hertz2bin((1.0 + defs.B_PEAK_SPREAD_ABOVE_HERTZ) *
base_frequency_estimate, sample_rate) -
bin_initial_estimate))
#bin_spread_below = int(round(bin_initial_estimate *
# defs.B_PEAK_SPREAD_BELOW_HERTZ))
#bin_spread_above = int(round(bin_initial_estimate *
# defs.B_PEAK_SPREAD_BELOW_HERTZ))
#bin_spread_below_main = int(
# stft.hertz2bin(defs.STFT_PEAK_SPREAD_BELOW_HERTZ*base_frequency_estimate,
# sample_rate))
#bin_spread_above_main = int(
# stft.hertz2bin(defs.STFT_PEAK_SPREAD_ABOVE_HERTZ*base_frequency_estimate,
# sample_rate))
### actual estimate
bin_f0, B, rsquared, harmonics, limit = get_bin_f0_B(
bin_initial_estimate,
estimate_B_buffers,
noise_cutoff,
#estimate_B_buffers, numpy.zeros(defs.LONG_WINDOWSIZE+1),
bin_spread_below,
bin_spread_above,
sample_rate)
highest_harmonic = 0
for h in harmonics:
if highest_harmonic < h.n:
highest_harmonic = h.n
limit = min(limit, highest_harmonic)
# HACK: remove limit
#limit = defs.TOTAL_HARMONICS
#print "limit to:", limit
#harmonics_enable = [True]*defs.TOTAL_HARMONICS
harmonics_enable = [True] * limit
bins_estimate = [
partials.mode_B2freq(bin_f0, i, B)
for i in range(1,
len(harmonics_enable) + 1)
]
bins_naive = [i * bin_f0 for i in range(1, len(harmonics_enable) + 1)]
if defs.B_PLOT:
pylab.figure()
pylab.plot(initial_noise_freqs,
stft.amplitude2db(initial_noise_floor),
color='black')
#pylab.plot(initial_noise_freqs,
# stft.amplitude2db(initial_noise_floor)+defs.B_MINIMUM_HARMONIC_SNR,
# color='black')
pylab.xlabel("Frequency (seconds)")
pylab.ylabel("Power (/ dB)")
for i in range(estimate_B_buffers.shape[0]):
#color = matplotlib.cm.spring(float(wav_i)/len(filenames))
#color = matplotlib.cm.RdYlGn(
#color = matplotlib.cm.spring(
# float(i)/len(estimate_B_buffers_this_list))
pylab.plot(
freqs,
stft.amplitude2db(estimate_B_buffers[i, :]),
#color=color,
color="orange",
alpha=0.5,
label=basename,
)
for est in bins_estimate:
low = stft.bin2hertz(est - bin_spread_below, sample_rate)
high = stft.bin2hertz(est + bin_spread_above, sample_rate)
if True:
pylab.axvspan(low, high, color='c', alpha=0.3)
else:
pylab.axvline(
stft.bin2hertz(est, sample_rate),
color='cyan',
alpha=0.3,
#linewidth=2.0
)
for naive in bins_naive:
freq = stft.bin2hertz(naive, sample_rate)
pylab.axvline(
freq,
color='grey',
alpha=0.2,
#linewidth=2.0
)
for j, harm in enumerate(harmonics):
if harm.mag == 0:
continue
fn = stft.bin2hertz(harm.fft_bin, sample_rate)
mag = stft.amplitude2db(harm.mag)
#pylab.plot(fn, mag, 'o',
# color='green'
# )
pylab.xlabel("Frequency")
pylab.ylabel("Decibels")
if defs.B_DUMP_HARMS:
t_fns = []
t_mags = []
for j, harm in enumerate(harmonics):
if harm.mag == 0:
continue
fn = stft.bin2hertz(harm.fft_bin, sample_rate)
mag = stft.amplitude2db(harm.mag)
t_fns.append(fn)
t_mags.append(mag)
data = numpy.vstack((t_fns, t_mags)).transpose()
numpy.savetxt("B-harms.txt", data)
if defs.B_PLOT:
pylab.show()
f0 = stft.bin2hertz(bin_f0, sample_rate)
stiff_ideal_limit = stiff_ideal_conflict.find_limit(
bin_f0, B, bin_spread_below, bin_spread_above)
lim = min(stiff_ideal_limit, limit)
detected_freqs = StringFreqsB(f0, B, lim)
stats = StringFreqsB_stats()
stats.num_files = len(filenames)
stats.rsquared = rsquared
stats.highest_mode_detected = limit
stats.highest_mode_stiff_ideal = stiff_ideal_limit
stats.basename = basename
adjusted_B, delta_fn = adjust_B.adjust(basename, limit, f0, B)
if adjusted_B is not None:
stiff_ideal_lim_adjusted = stiff_ideal_conflict.find_limit(
bin_f0, adjusted_B, bin_spread_below, bin_spread_above)
lim = min(stiff_ideal_lim_adjusted, limit)
adjusted_freqs = StringFreqsB(f0, adjusted_B, lim)
adjusted_freqs.delta_fn = delta_fn
stats.highest_mode_stiff_ideal_adjusted = stiff_ideal_lim_adjusted
stats.delta_fn = delta_fn
final = StringFreqsB(
f0, adjusted_B,
min(stats.highest_mode_detected, stats.highest_mode_stiff_ideal,
stiff_ideal_lim_adjusted))
else:
adjusted_freqs = None
final = StringFreqsB(
f0, B,
min(stats.highest_mode_detected, stats.highest_mode_stiff_ideal))
return detected_freqs, adjusted_freqs, stats, final
