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'))
def main():
harman_onear = 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_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'))
dbs = [
('crinacle_inear', get_measurements(os.path.join(MEASUREMENTS, 'crinacle', 'data', 'inear')), None),
('crinacle_onear', get_measurements(os.path.join(MEASUREMENTS, 'crinacle', 'data', 'onear')), None),
(
'headphonecom_onear',
get_measurements(os.path.join(MEASUREMENTS, 'headphonecom', 'data', 'onear')),
FrequencyResponse.read_from_csv(os.path.join(MEASUREMENTS, 'headphonecom', 'resources', 'headphonecom_compensation_sbaf-serious.csv'))
),
(
'headphonecom_inear',
get_measurements(os.path.join(MEASUREMENTS, 'headphonecom', 'data', 'inear')),
FrequencyResponse.read_from_csv(os.path.join(MEASUREMENTS, 'headphonecom', 'resources', 'headphonecom_compensation_sbaf-serious.csv'))
),
(
'innerfidelity_onear',
get_measurements(os.path.join(MEASUREMENTS, 'innerfidelity', 'data', 'onear')),
FrequencyResponse.read_from_csv(os.path.join(MEASUREMENTS, 'innerfidelity', 'resources', 'innerfidelity_compensation_sbaf-serious.csv'))
),
(
'innerfidelity_inear',
get_measurements(os.path.join(MEASUREMENTS, 'innerfidelity', 'data', 'inear')),
FrequencyResponse.read_from_csv(os.path.join(MEASUREMENTS, 'innerfidelity', 'resources', 'innerfidelity_compensation_sbaf-serious.csv'))
),
(
'referenceaudioanalyzer_onear-hdm-x',
get_measurements(os.path.join(MEASUREMENTS, 'referenceaudioanalyzer', 'data', 'onear', 'HDM-X')),
None
),
(
'referenceaudioanalyzer_onear-hdm1',
get_measurements(os.path.join(MEASUREMENTS, 'referenceaudioanalyzer', 'data', 'onear', 'HDM1')),
None
),
(
'referenceaudioanalyzer_inear-siec',
get_measurements(os.path.join(MEASUREMENTS, 'referenceaudioanalyzer', 'data', 'inear', 'SIEC')),
None
),
(
'rtings_onear',
get_measurements(os.path.join(MEASUREMENTS, 'rtings', 'data', 'onear')),
FrequencyResponse.read_from_csv(os.path.join(MEASUREMENTS, 'rtings', 'resources', 'rtings_compensation_avg.csv'))
),
(
'rtings_inear',
get_measurements(os.path.join(MEASUREMENTS, 'rtings', 'data', 'inear')),
FrequencyResponse.read_from_csv(os.path.join(MEASUREMENTS, 'rtings', 'resources', 'rtings_compensation_avg.csv'))
),
]
stds = []
for name, measurements, original_target in dbs:
print(f'Calibrating {name}...')
# Find matching pairs
pairs = []
for fr in measurements:
ref = oratory1990_onear if 'onear' in name else oratory1990_inear
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 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, -0.05, 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)
stds.append(FrequencyResponse(name=name, raw=std))
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])
ref_target = harman_onear if 'onear' in name else harman_inear
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'target_{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 'onear' 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)
