CF-dependence of compressive nonlinearity in the Tan&Carney model. Reproduces Fig. 7 from:
import itertools
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
from brian import *
#set_global_preferences(useweave=True)
from brian.hears import *
from brian.hears.filtering.tan_carney import TanCarneySignal, MiddleEar
samplerate = 50*kHz
set_default_samplerate(samplerate)
duration = 50*ms
def gen_tone(freq, level):
'''
Little helper function to generate a pure tone at frequency `freq` with
the given `level`. The tone has a duration of 50ms and is ramped with
two ramps of 2.5ms.
'''
freq = float(freq) * Hz
level = float(level) * dB
return tone(freq, duration).ramp(when='both',
duration=2.5*ms,
inplace=False).atlevel(level)
freqs = [500, 1100, 2000, 4000]
levels = np.arange(-10, 100.1, 5)
cf_level = list(itertools.product(freqs, levels))
# steady-state
start = 10*ms*samplerate
end = 45*ms*samplerate
# For Figure 7 we have manually adjusts the gain for different CFs -- otherwise
# the RMS values wouldn't be identical for low CFs. Therefore, try to estimate
# suitable gain values first using the lowest CF as a reference
ref_tone = gen_tone(freqs[0], levels[0])
F_out_reference = TanCarneySignal(MiddleEar(ref_tone, gain=1), freqs[0],
update_interval=1).process().flatten()
ref_rms = np.sqrt(np.mean((F_out_reference[start:end] -
np.mean(F_out_reference[start:end]))**2))
gains = np.linspace(0.1, 1, 50) # for higher CFs we need lower gains
cf_gains = list(itertools.product(freqs[1:], gains))
tones = Sound([gen_tone(freq, levels[0]) for freq, _ in cf_gains])
F_out_test = TanCarneySignal(MiddleEar(tones, gain=np.array([g for _, g in cf_gains])),
[cf for cf,_ in cf_gains], update_interval=1).process()
reshaped_Fout = F_out_test.T.reshape((len(freqs[1:]), len(gains), -1))
rms = np.sqrt(np.mean((reshaped_Fout[:, :, start:end].T -
np.mean(reshaped_Fout[:, :, start:end], axis=2).T).T**2,
axis=2))
# get the best gain for each CF using simple linear interpolation
gain_dict = {freqs[0]: 1.} # reference gain
for idx, freq in enumerate(freqs[1:]):
gain_dict[freq] = interp1d(rms[idx, :], gains)(ref_rms)
# now do the real test: tones at different levels for different CFs
tones = Sound([gen_tone(freq, level) for freq, level in cf_level])
F_out = TanCarneySignal(MiddleEar(tones,
gain=np.array([gain_dict[cf] for cf, _ in cf_level])),
[cf for cf, _ in cf_level],
update_interval=1).process()
reshaped_Fout = F_out.T.reshape((len(freqs), len(levels), -1))
rms = np.sqrt(np.mean((reshaped_Fout[:, :, start:end].T -
np.mean(reshaped_Fout[:, :, start:end], axis=2).T).T**2,
axis=2))
# This should more or less reproduce Fig. 7
plt.plot(levels, rms.T)
plt.legend(['%.0f Hz' % cf for cf in freqs], 0)
plt.xlim(-20, 100)
plt.ylim(1e-6, 1)
plt.yscale('log')
plt.xlabel('input signal SPL (dB)')
plt.ylabel('rms of AC component of Fout')
plt.show()