def test_measure_rms(filename, regtest):
filename = os.path.dirname(os.path.abspath(__file__)) + "/data/" + filename
data = scipy.io.loadmat(filename, squeeze_me=True)
rms, key, rel_dB_thresh, active = MSBG.measure_rms(data["signal"],
data["fs"],
data["dB_rel_rms"])
npt.assert_allclose(rms, data["rms"], rtol=RTOL)
npt.assert_allclose(key, data["key"] - 1,
rtol=RTOL) # index so subtract 1 OK
npt.assert_allclose(rel_dB_thresh, data["rel_dB_thresh"], rtol=RTOL)
print(rms, file=regtest)
print(utils.hash_numpy(key), file=regtest)
print(rel_dB_thresh, file=regtest)
def process(
self,
chans,
add_calibration=False,
):
"""Run the hearing loss simulation.
Args:
chans (ndarray): signal to process, shape either N, Nx1, Nx2
add_calibration (bool): prepend calibration tone and speech-shaped noise
(default: False)
Returns:
ndarray: the processed signal
"""
fs = 44100 # This is the only sampling frequency that can be used
if fs != CONFIG.fs:
logging.error(
"Warning: only a sampling frequency of 44.1kHz can be used by MSBG."
)
logging.info(f"Processing {len(chans)} samples")
# Get single channel array and convert to list
chans = Ear.array_to_list(chans)
# Need to know file RMS, and then call that a certain level in SPL:
# needs some form of pre-measuring.
levelreFS = 10 * np.log10(np.mean(np.array(chans)**2))
equiv_0dB_SPL = CONFIG.equiv0dBSPL + CONFIG.ahr
leveldBSPL = equiv_0dB_SPL + levelreFS
CALIB_DB_SPL = leveldBSPL
TARGET_SPL = leveldBSPL
REF_RMS_DB = CALIB_DB_SPL - equiv_0dB_SPL
# Measure RMS where 3rd arg is dB_rel_rms (how far below)
calculated_rms, idx, rel_dB_thresh, active = MSBG.measure_rms(
chans[0], fs, -12)
# Rescale input data and check level after rescaling
# This is to ensure that the following processing steps are applied correctly
change_dB = TARGET_SPL - (equiv_0dB_SPL +
20 * np.log10(calculated_rms))
chans = [x * np.power(10, 0.05 * change_dB) for x in chans]
new_rms_db = equiv_0dB_SPL + 10 * np.log10(
np.mean(np.power(chans[0][idx], 2.0)))
logging.info(
f"Rescaling: leveldBSPL was {leveldBSPL:3.1f} dB SPL, now {new_rms_db:3.1f}dB SPL. Target SPL is {TARGET_SPL:3.1f} dB SPL."
)
# Add calibration signal at target SPL dB
if add_calibration == True:
if self.calibration_signal is None:
self.calibration_signal = Ear.make_calibration_signal(
REF_RMS_DB)
chans = [
np.concatenate((self.calibration_signal[0], x,
self.calibration_signal[1])) for x in chans
]
# Transform from src pos to cochlea, simulate cochlea, transform back to src pos
chans = [
Ear.src_to_cochlea_filt(x, self.src_correction, fs) for x in chans
]
chans = [self.cochlea.simulate(x, equiv_0dB_SPL) for x in chans]
chans = [
Ear.src_to_cochlea_filt(x, self.src_correction, fs, backward=True)
for x in chans
]
# Implement low-pass filter at top end of audio range: flat to Cutoff freq, tails
# below -80 dB. Suitable lpf for signals later converted to MP3, flat to 15 kHz.
# Small window to design low-pass FIR, to cut off high freq processing noise
# low-pass to something sensible, prevents exaggeration of > 15 kHz
winlen = 2 * math.floor(0.0015 * fs) + 1
lpf44d1 = firwin(winlen,
UPPER_CUTOFF_HZ / int(fs / 2),
window=("kaiser", 8))
chans = [lfilter(lpf44d1, 1, x) for x in chans]
return chans