def parse_image(im, model):
"""Parses graph image downloaded from innerfidelity.com"""
# Crop by left and right edges
box = (69, 31, 550, 290)
im = im.crop(box)
px_a_max = 0
px_a_min = im.size[1]
# im.show()
# X axis
f_min = 20
f_max = 20000
f_step = (f_max / f_min)**(1 / im.size[0])
f = [f_min]
for _ in range(1, im.size[0]):
f.append(f[-1] * f_step)
# Y axis
a_max = 150
a_min = 66
a_res = (a_max - a_min) / (px_a_min - px_a_max)
# Try blue curve
_im = im.copy()
inspection = _im.load()
amplitude, _im, _inspection = ReferenceAudioAnalyzerCrawler.find_curve(
_im, inspection, 203, 206, 0.8, 1.0, a_max, a_res)
if len([x for x in amplitude if x is None]) >= 0.5 * len(amplitude):
# More than half of the pixels were discarded, try green curve
_im = im.copy()
inspection = _im.load()
amplitude, _im, _inspection = ReferenceAudioAnalyzerCrawler.find_curve(
_im, inspection, 119, 121, 0.8, 1.0, a_max, a_res)
# Inspection image
draw = ImageDraw.Draw(_im)
x0 = np.log(30 / f_min) / np.log(f_step)
x1 = np.log(10000 / f_min) / np.log(f_step)
y_0 = px_a_max + 12 / a_res
y_1 = px_a_min - 12 / a_res
draw.rectangle(((x0, y_0), (x1, y_1)), outline='magenta')
draw.rectangle(((x0 + 1, y_0 + 1), (x1 - 1, y_1 - 1)),
outline='magenta')
# Create frequency response
fr = FrequencyResponse(model, f, amplitude)
fr.interpolate()
if len(fr.frequency) < 2:
im.show()
raise ValueError(f'Failed to parse image for {fr.name}')
fr.smoothen_fractional_octave(window_size=1 / 3,
treble_window_size=1 / 3)
fr.raw = fr.smoothed.copy()
fr.smoothed = np.array([])
fr.center()
return fr, _im
def main():
fig, ax = plt.subplots()
diffs = []
# Calculate differences for all models
for file in glob(os.path.join('compensation', 'compensated', '**',
'*.csv'),
recursive=True):
file = os.path.abspath(file)
comp = FrequencyResponse.read_from_csv(file)
comp.interpolate()
comp.center()
raw_data_path = file.replace('compensated', 'raw')
raw = FrequencyResponse.read_from_csv(raw_data_path)
raw.interpolate()
raw.center()
diff = FrequencyResponse(name=comp.name,
frequency=comp.frequency,
raw=raw.raw - comp.raw)
plt.plot(diff.frequency, diff.raw)
diffs.append(diff.raw)
# Average and smoothen difference
f = FrequencyResponse.generate_frequencies()
diffs = np.vstack(diffs)
diff = np.mean(diffs, axis=0)
diff = FrequencyResponse(name='Headphone.com Compensation',
frequency=f,
raw=diff)
diff.smoothen_fractional_octave(window_size=1 / 9, iterations=10)
diff.raw = diff.smoothed
diff.smoothed = np.array([])
plt.xlabel('Frequency (Hz)')
plt.semilogx()
plt.xlim([20, 20000])
plt.ylabel('Amplitude (dBr)')
plt.ylim([-15, 15])
plt.grid(which='major')
plt.grid(which='minor')
plt.title('Headphone.com Compensation Function')
ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))
plt.show()
diff.write_to_csv('headphonecom_compensation.csv')
diff.plot_graph(show=True,
f_min=10,
f_max=20000,
file_path='headphonecom_compensation.png')
def main():
harman_onear = FrequencyResponse.read_from_csv(
os.path.join(ROOT_DIR, 'compensation', 'harman_over-ear_2018.csv'))
harman_onear_wo_bass = FrequencyResponse.read_from_csv(
os.path.join(ROOT_DIR, 'compensation',
'harman_over-ear_2018_wo_bass.csv'))
harman_inear = FrequencyResponse.read_from_csv(
os.path.join(ROOT_DIR, 'compensation', 'harman_in-ear_2019v2.csv'))
harman_inear_wo_bass = FrequencyResponse.read_from_csv(
os.path.join(ROOT_DIR, 'compensation',
'harman_in-ear_2019v2_wo_bass.csv'))
oratory1990_onear = get_measurements(
os.path.join(MEASUREMENTS, 'oratory1990', 'data', 'onear'))
oratory1990_inear = get_measurements(
os.path.join(MEASUREMENTS, 'oratory1990', 'data', 'inear'))
crinacle_inear = get_measurements(
os.path.join(MEASUREMENTS, 'crinacle', 'data', 'inear'))
inear_ref = oratory1990_inear.copy()
inear_names = [fr.name for fr in inear_ref]
for fr in crinacle_inear:
if fr.name not in inear_names:
inear_ref.append(fr)
dbs = [
('crinacle_harman_in-ear_2019v2_wo_bass', crinacle_inear,
oratory1990_inear, None),
('crinacle_ears-711_harman_over-ear_2018_wo_bass',
get_measurements(
os.path.join(MEASUREMENTS, 'crinacle', 'data', 'onear',
'Ears-711')), oratory1990_onear, None),
('headphonecom_harman_over-ear_2018_wo_bass',
get_measurements(
os.path.join(MEASUREMENTS, 'headphonecom', 'data',
'onear')), oratory1990_onear,
FrequencyResponse.read_from_csv(
os.path.join(MEASUREMENTS, 'headphonecom', 'resources',
'headphonecom_compensation_sbaf-serious.csv'))),
('headphonecom_harman_in-ear_2019v2_wo_bass',
get_measurements(
os.path.join(MEASUREMENTS, 'headphonecom', 'data',
'inear')), inear_ref,
FrequencyResponse.read_from_csv(
os.path.join(MEASUREMENTS, 'headphonecom', 'resources',
'headphonecom_compensation_sbaf-serious.csv'))),
('innerfidelity_harman_over-ear_2018_wo_bass',
get_measurements(
os.path.join(MEASUREMENTS, 'innerfidelity', 'data',
'onear')), oratory1990_onear,
FrequencyResponse.read_from_csv(
os.path.join(MEASUREMENTS, 'innerfidelity', 'resources',
'innerfidelity_compensation_sbaf-serious.csv'))),
('innerfidelity_harman_in-ear_2019v2_wo_bass',
get_measurements(
os.path.join(MEASUREMENTS, 'innerfidelity', 'data',
'inear')), inear_ref,
FrequencyResponse.read_from_csv(
os.path.join(MEASUREMENTS, 'innerfidelity', 'resources',
'innerfidelity_compensation_sbaf-serious.csv'))),
('referenceaudioanalyzer_hdm-x_harman_over-ear_2018_wo_bass',
get_measurements(
os.path.join(MEASUREMENTS, 'referenceaudioanalyzer', 'data',
'onear', 'HDM-X')), oratory1990_onear, None),
('referenceaudioanalyzer_hdm1_harman_over-ear_2018_wo_bass',
get_measurements(
os.path.join(MEASUREMENTS, 'referenceaudioanalyzer', 'data',
'onear', 'HDM1')), oratory1990_onear, None),
('referenceaudioanalyzer_siec_harman_in-ear_2019v2_wo_bass',
get_measurements(
os.path.join(MEASUREMENTS, 'referenceaudioanalyzer', 'data',
'inear', 'SIEC')), inear_ref, None),
('rtings_harman_over-ear_2018_wo_bass',
get_measurements(os.path.join(MEASUREMENTS, 'rtings', 'data',
'onear')), oratory1990_onear,
FrequencyResponse.read_from_csv(
os.path.join(MEASUREMENTS, 'rtings', 'resources',
'rtings_compensation_avg.csv'))),
('rtings_harman_in-ear_2019v2_wo_bass',
get_measurements(os.path.join(MEASUREMENTS, 'rtings', 'data',
'inear')), inear_ref,
FrequencyResponse.read_from_csv(
os.path.join(MEASUREMENTS, 'rtings', 'resources',
'rtings_compensation_avg.csv'))),
('crinacle_gras_43ag-7_harman_over-ear_2018_wo_bass',
get_measurements(
os.path.join(MEASUREMENTS, 'crinacle', 'data', 'onear',
'GRAS 43AG-7')), oratory1990_onear, None)
]
stds = []
for name, measurements, ref, original_target in dbs:
print(f'Calibrating {name}...')
# Find matching pairs
pairs = []
for fr in measurements:
for candidate in ref:
if fr.name.lower() == candidate.name.lower():
pairs.append((fr, candidate))
fig, axs = plt.subplots(1, 3)
fig.set_size_inches(30, 8)
fig.suptitle(name)
description = 'Calibrated against reference measurements with headphones: '
line_len = len(description)
for fr, _ in pairs:
if line_len > 240:
description += '\n'
line_len = 0
description += f'{fr.name}, '
line_len += len(fr.name) + 2
description = description[:-2]
fig.text(0.5, -0.05, description, ha='center')
# Individual errors
errors = []
i = 0
for fr, target in pairs:
fr.compensate(target, min_mean_error=True)
errors.append(fr.error)
fr.raw = fr.error.copy()
fr.error = []
fr.target = []
fr.plot_graph(fig=fig,
ax=axs[0],
show=False,
raw_plot_kwargs={
'color': 'C0',
'alpha': 0.3
})
i += 1
axs[0].set_ylim([-15, 15])
axs[0].set_title('Individual Errors')
axs[0].legend(['Error'])
# Mean and standard deviation
errors = np.vstack(errors)
mean = np.mean(errors, axis=0)
std = np.std(errors, axis=0)
stds.append(FrequencyResponse(name=name, raw=std))
fr = FrequencyResponse(name='Mean and Standard Deviation')
fr.raw = mean
fr.smoothen_fractional_octave(window_size=1 / 3,
treble_window_size=1 / 3)
fr.raw = fr.smoothed.copy()
fr.smoothed = []
fr.plot_graph(fig=fig, ax=axs[1], color='C0', show=False)
axs[1].fill_between(fr.frequency,
mean - std,
mean + std,
facecolor='#c1dff5')
axs[1].set_ylim([-15, 15])
axs[1].legend(['Mean', 'STD'])
# Target curves
ref_target = harman_onear_wo_bass if 'over-ear' in name else harman_inear_wo_bass
ref_target.plot_graph(fig=fig, ax=axs[2], show=False, color='C0')
target = ref_target.copy()
target.name = name
target.raw += fr.raw
target.plot_graph(fig=fig, ax=axs[2], show=False, color='C1')
if original_target is not None:
original_target.plot_graph(fig=fig,
ax=axs[2],
show=False,
color='C2')
axs[2].legend([ref_target.name, target.name, original_target.name])
else:
axs[2].legend([ref_target.name, target.name])
axs[2].set_title(f'{name} target')
axs[2].set_ylim([-15, 15])
fig.savefig(os.path.join(DIR_PATH, f'calibration_{name}.png'),
bbox_inches='tight')
target.plot_graph(show=False,
file_path=os.path.join(DIR_PATH, f'{name}.png'),
color='C0')
target.write_to_csv(file_path=os.path.join(DIR_PATH, f'{name}.csv'))
plt.close(fig)
fig, axs = plt.subplots(1, 2)
fig.set_size_inches(20, 8)
onear_labels = []
inear_labels = []
for fr in stds:
if 'over-ear' in fr.name:
fr.plot_graph(fig=fig,
ax=axs[0],
color=f'C{len(onear_labels)}',
show=False)
onear_labels.append(fr.name)
else:
fr.plot_graph(fig=fig,
ax=axs[1],
color=f'C{len(inear_labels)}',
show=False)
inear_labels.append(fr.name)
axs[0].legend(onear_labels)
axs[1].legend(inear_labels)
axs[0].set_title('On-ear')
axs[1].set_title('In-ear')
axs[0].set_ylim([0, 8])
axs[1].set_ylim([0, 8])
fig.savefig(os.path.join(DIR_PATH, 'STDs.png'))
plt.close(fig)
def main():
# Filenames
if_files = list(
glob(os.path.join('innerfidelity', 'data', '**', '*.csv'),
recursive=True))
if_file_names = [os.path.split(os.path.abspath(f))[-1] for f in if_files]
normalized_if_files = [normalize(s) for s in if_file_names]
hp_files = list(
glob(os.path.join('rtings', 'data', '**', '*.csv'), recursive=True))
# Find matching files
matching_if_files = []
matching_hp_files = []
for hp_file in hp_files:
file_name = os.path.split(os.path.abspath(hp_file))[-1]
for i in range(len(normalized_if_files)):
if normalized_if_files[i] == normalize(file_name):
matching_hp_files.append(hp_file)
matching_if_files.append(if_files[i])
# Write mathces to file for manual inspection
df = pd.DataFrame(
np.array([matching_hp_files, matching_if_files]).transpose())
df.to_csv('matches.csv', index=False, header=False)
fig, ax = plt.subplots()
diffs = []
# Calculate differences for all models
if_compensation = FrequencyResponse.read_from_csv(
os.path.join('innerfidelity', 'resources',
'innerfidelity_compensation_2017.csv'))
if_compensation.interpolate()
hp_compensation = FrequencyResponse.read_from_csv(
os.path.join('rtings', 'resources', 'rtings_compensation.csv'))
hp_compensation.interpolate()
for i in range(len(matching_if_files)):
if_fr = FrequencyResponse.read_from_csv(matching_if_files[i])
if_fr.interpolate()
if_fr.center()
#if_fr.compensate(if_compensation)
hp_fr = FrequencyResponse.read_from_csv(matching_hp_files[i])
hp_fr.interpolate()
hp_fr.center()
#hp_fr.compensate(hp_compensation)
#diff = FrequencyResponse(name=if_fr.name, frequency=if_fr.frequency, raw=hp_fr.error - if_fr.error)
diff = FrequencyResponse(name=if_fr.name,
frequency=if_fr.frequency,
raw=hp_fr.raw - if_fr.raw)
plt.plot(diff.frequency, diff.raw)
diffs.append(diff.raw)
# Average and smoothen difference
f = FrequencyResponse.generate_frequencies()
diffs = np.vstack(diffs)
diff = np.mean(diffs, axis=0)
std = np.std(diffs, axis=0)
diff = FrequencyResponse(name='Rtings Raw to Innerfidelity Raw',
frequency=f,
raw=diff)
#diff.smoothen(window_size=1/7, iterations=10)
diff.smoothen_fractional_octave(window_size=1 / 5, iterations=100)
diff.raw = diff.smoothed
diff.smoothed = np.array([])
plt.xlabel('Frequency (Hz)')
plt.semilogx()
plt.xlim([20, 20000])
plt.ylabel('Amplitude (dBr)')
plt.ylim([-15, 15])
plt.grid(which='major')
plt.grid(which='minor')
plt.title('Rtings Raw to Innerfidelity Raw')
ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))
plt.show()
fig, ax = diff.plot_graph(f_min=20, f_max=20000, show=False, color=None)
ax.fill_between(diff.frequency,
diff.raw + std,
diff.raw - std,
facecolor='lightblue')
plt.legend(['Rtings Raw to Innerfidelity Raw', 'Standard Deviation'])
plt.ylim([-10, 10])
fig.savefig(os.path.join('calibration', 'rtings_to_innerfidelity.png'),
dpi=240)
plt.show()
diff.write_to_csv(
os.path.join('calibration', 'rtings_to_innerfidelity.csv'))
diff.raw *= -1
diff.name = 'Innerfidelity Raw to Rtings Raw'
fig, ax = diff.plot_graph(f_min=20, f_max=20000, show=False, color=None)
ax.fill_between(diff.frequency,
diff.raw + std,
diff.raw - std,
facecolor='lightblue')
plt.legend(['Innerfidelity Raw to Rtings Raw', 'Standard Deviation'])
plt.ylim([-10, 10])
fig.savefig(os.path.join('calibration', 'innerfidelity_to_rtings.png'),
dpi=240)
plt.show()
diff.write_to_csv(
os.path.join('calibration', 'innerfidelity_to_rtings.csv'))
def main():
harman_nobass = FrequencyResponse.read_from_csv(
os.path.join(DIR_PATH, os.pardir, os.pardir, 'compensation',
'harman_over-ear_2018_wo_bass.csv'))
harman = FrequencyResponse.read_from_csv(
os.path.join(DIR_PATH, os.pardir, os.pardir, 'compensation',
'harman_over-ear_2018.csv'))
oratory1990 = measurements(
os.path.join(DIR_PATH, os.pardir, 'oratory1990', 'data', 'onear'))
crinacle = measurements(os.path.join(DIR_PATH, 'data', 'onear'))
pairs = []
for fr in crinacle:
for candidate in oratory1990:
if fr.name.lower() == candidate.name.lower():
pairs.append((fr, candidate))
fig, axs = plt.subplots(1, 3)
fig.set_size_inches(25, 10)
fig.suptitle('Crinacle Over-ear Calibration')
description = 'Calibrated against oratory1990 measurements with headphones: '
line_len = len(description)
for fr, _ in pairs:
if line_len > 240:
description += '\n'
line_len = 0
description += f'{fr.name}, '
line_len += len(fr.name) + 2
description = description[:-2]
fig.text(0.5, 0.93, description, ha='center')
errors = []
i = 0
for fr, target in pairs:
fr.compensate(target, min_mean_error=True)
errors.append(fr.error)
fr.raw = fr.error.copy()
fr.error = []
fr.target = []
fr.plot_graph(fig=fig, ax=axs[0], show=False, color=f'C{i}')
i += 1
axs[0].set_ylim([-15, 15])
axs[0].set_title('Individual Errors')
errors = np.vstack(errors)
mean = np.mean(errors, axis=0)
std = np.std(errors, axis=0)
fr = FrequencyResponse(name='Mean and Standard Deviation')
fr.raw = mean
fr.smoothen_heavy_light()
fr.raw = fr.smoothed.copy()
fr.smoothed = []
fr.plot_graph(fig=fig, ax=axs[1], color='C0', show=False)
axs[1].fill_between(fr.frequency,
mean - std,
mean + std,
facecolor='#c1dff5')
axs[1].set_ylim([-15, 15])
harman_nobass.plot_graph(fig=fig, ax=axs[2], show=False, color='C0')
crinacle_bass = harman.copy()
crinacle_bass.raw += fr.raw
crinacle_nobass = harman_nobass.copy()
crinacle_nobass.raw += fr.raw
crinacle_nobass.plot_graph(fig=fig, ax=axs[2], show=False, color='C1')
axs[2].legend(
['Harman over-ear 2018 w/o bass', 'Crinacle over-ear w/o bass'])
axs[2].set_title('Crinacle Over-ear Target')
axs[2].set_ylim([-15, 15])
resources_dir = os.path.join(DIR_PATH, 'resources')
os.makedirs(resources_dir, exist_ok=True)
fig.savefig(os.path.join(DIR_PATH, 'resources', 'calibration.png'),
bbox_inches='tight')
crinacle_bass.name = 'Crinacle Over-ear Target with Harman Bass Boost'
crinacle_bass.plot_graph(show=False,
file_path=os.path.join(
resources_dir, 'crinacle_over-ear_bass.png'),
color='C0')
crinacle_bass.write_to_csv(
file_path=os.path.join(resources_dir, 'crinacle_over-ear_bass.csv'))
crinacle_nobass.name = 'Crinacle Over-ear Target'
crinacle_nobass.plot_graph(show=False,
file_path=os.path.join(resources_dir,
'crinacle_over-ear.png'),
color='C0')
crinacle_nobass.write_to_csv(
file_path=os.path.join(resources_dir, 'crinacle_over-ear.csv'))
