def main():
fs = 48e3
### Make sound
tmax = 0.03
t = np.arange(0, tmax, 1 / fs)
s = dsp.chirp(t, 80, t[-1], 16000)
sound = cochlea.set_dbspl(s, 50)
### Run model
anf = run_matlab_auditory_periphery(
sound,
fs,
anf_num=(100, 50, 20),
cf=(125, 16000, 80),
seed=0,
)
### Accumulate spike trains
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
### Plot auditory nerve response
fig, ax = plt.subplots(2, 1, sharex=True)
th.plot_signal(signal=sound, fs=fs, ax=ax[0])
th.plot_neurogram(anf_acc, fs, ax=ax[1])
plt.show()
def main():
fs = 100e3
### Make sound
t = np.arange(0, 0.1, 1 / fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
pad = np.zeros(10e-3 * fs)
sound = np.concatenate((s, pad))
### Run model
anf = cochlea.run_zilany2014(
sound, fs, anf_num=(100, 0, 0), cf=(125, 20000, 100), seed=0, powerlaw="approximate", species="human"
)
### Accumulate spike trains
anf_acc = th.accumulate(anf, keep=["cf", "duration"])
anf_acc.sort("cf", ascending=False, inplace=True)
### Plot auditory nerve response
fig, ax = plt.subplots(2, 1)
th.plot_signal(signal=sound, fs=fs, ax=ax[0])
th.plot_neurogram(anf_acc, fs, ax=ax[1])
plt.show()
def main():
fs = 48e3
### Make sound
t = np.arange(0, 0.1, 1 / fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate((s, np.zeros(int(10e-3 * fs))))
### Run model
anf_trains = cochlea.run_holmberg2007(
s,
fs,
anf_num=(100, 0, 0),
seed=0,
)
### Plot auditory nerve response
anf_acc = th.accumulate(anf_trains, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
fig, ax = plt.subplots(2, 1)
th.plot_signal(signal=s, fs=fs, ax=ax[0])
th.plot_neurogram(anf_acc, fs, ax=ax[1])
plt.show()
def test_accumulate():
trains = th.make_trains([[1], [2], [3], []], cfs=[2, 1, 2, 3])
accumulated = th.accumulate(trains)
expected = th.make_trains([[2], [1, 3], []], cfs=[1, 2, 3])
assert_frame_equal(accumulated, expected)
def main():
fs = 100e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate( (s, np.zeros(10e-3 * fs)) )
### Run model
anf = cochlea.run_zilany2009(
s,
fs,
anf_num=(100,0,0),
cf=(80, 20000, 100),
seed=0,
powerlaw='approximate'
)
### Plot auditory nerve response
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
cfs = anf.cf.unique()
fig, ax = plt.subplots(2,1, sharex=True)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[0]
)
th.plot_raster(
anf[anf.cf==cfs[30]],
ax=ax[1]
)
ax[1].set_title("CF = {}".format(cfs[30]))
plt.show()
def test_accumulate():
trains = th.make_trains(
[[1], [2], [3], []],
cfs=[2,1,2,3]
)
accumulated = th.accumulate(trains)
expected = th.make_trains(
[[2], [1,3], []],
cfs=[1,2,3]
)
assert_frame_equal(
accumulated,
expected,
check_like=True,
)
def main():
fs = 48e3
### Make sound
tmax = 0.03
t = np.arange(0, tmax, 1/fs)
s = dsp.chirp(t, 80, t[-1], 16000)
sound = cochlea.set_dbspl(s, 50)
### Run model
anf = run_matlab_auditory_periphery(
sound,
fs,
anf_num=(100,50,20),
cf=(125, 16000, 80),
seed=0,
)
### Accumulate spike trains
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
### Plot auditory nerve response
fig, ax = plt.subplots(2, 1, sharex=True)
th.plot_signal(
signal=sound,
fs=fs,
ax=ax[0]
)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[1]
)
plt.show()
def main():
fs = 100e3
# Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate((s, np.zeros(int(10e-3*fs))))
# Run model
anf = cochlea.run_zilany2009(
s,
fs,
anf_num=(100,0,0),
cf=(80, 20000, 100),
seed=0,
powerlaw='approximate'
)
# Plot auditory nerve response
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort_values('cf', ascending=False, inplace=True)
cfs = anf.cf.unique()
fig, ax = plt.subplots(2,1, sharex=True)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[0]
)
th.plot_raster(
anf[anf.cf==cfs[30]],
ax=ax[1]
)
ax[1].set_title("CF = {}".format(cfs[30]))
plt.show()
def main():
fs = 100e3
# Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
pad = np.zeros(int(10e-3 * fs))
sound = np.concatenate( (s, pad) )
# Run model
anf = cochlea.run_zilany2014(
sound,
fs,
anf_num=(100,0,0),
cf=(125, 20000, 100),
seed=0,
powerlaw='approximate',
species='human',
)
# Accumulate spike trains
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort_values('cf', ascending=False, inplace=True)
# Plot auditory nerve response
fig, ax = plt.subplots(2,1)
th.plot_signal(
signal=sound,
fs=fs,
ax=ax[0]
)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[1]
)
plt.show()
def main():
fs = 48e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate( (s, np.zeros(10e-3 * fs)) )
### Run model
anf_trains = cochlea.run_holmberg2007(
s,
fs,
anf_num=(100,0,0),
seed=0,
)
### Plot auditory nerve response
anf_acc = th.accumulate(anf_trains, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
fig, ax = plt.subplots(2,1)
th.plot_signal(
signal=s,
fs=fs,
ax=ax[0]
)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[1]
)
plt.show()
rate = f.getframerate()
duration = frames / float(rate)
spf = wave.open(fname, "r")
# Extract Raw Audio from Wav File
signal = spf.readframes(-1)
signal = np.frombuffer(signal, dtype=np.int16)
number_lv = np.ceil(np.log10(np.max(signal)))
spf.close()
signal = signal / 10.**number_lv
signal = dsp.resample(signal, int(fs * duration))
plt.figure(1)
plt.title("Signal Wave...")
wv.plot_signal(signal, int(fs))
plt.show()
anf_trains = cochlea.run_zilany2014(signal,
int(fs),
anf_num=(0, 0, 50),
cf=(125, 10e3, 100),
seed=0,
species='human')
th.plot_raster(th.accumulate(anf_trains))
plt.show()
