def powerlawnoise(kind='diotic', **kwargs):
"""
Generate binaural power law noise. `kind`='diotic' produces the same noise samples in both channels,
`kind`='dichotic' produces uncorrelated noise. The rest is identical to `slab.Sound.powerlawnoise`.
"""
if kind == 'dichotic':
out = Binaural(Sound.powerlawnoise(n_channels=2, **kwargs))
out.left = Sound.powerlawnoise(n_channels=1, **kwargs)
elif kind == 'diotic':
out = Binaural(Sound.powerlawnoise(n_channels=2, **kwargs))
out.left = out.right
else:
raise ValueError("kind must be 'dichotic' or 'diotic'.")
return out
def _get_itd(self, max_lag):
max_lag = Sound.in_samples(max_lag, self.samplerate)
xcorr = numpy.correlate(self.data[:, 0], self.data[:, 1], 'full')
lags = numpy.arange(-max_lag, max_lag + 1)
xcorr = xcorr[self.n_samples - 1 - max_lag:self.n_samples + max_lag]
idx = numpy.argmax(xcorr)
return lags[idx]
def whitenoise(kind='diotic', **kwargs):
"""
Generate binaural white noise. `kind`='diotic' produces the same noise samples in both channels,
`kind`='dichotic' produces uncorrelated noise. The rest is identical to `slab.Sound.whitenoise`.
"""
out = Binaural(Sound.whitenoise(**kwargs))
if kind == 'diotic':
out.left = out.right
return out
def irn(kind='diotic', **kwargs):
"""
Generate iterated ripple noise (IRN). `kind`='diotic' produces the same noise samples in both channels,
`kind`='dichotic' produces uncorrelated noise. The rest is identical to `slab.Sound.irn`.
"""
out = Binaural(Sound.irn(n_channels=2, **kwargs))
if kind == 'diotic':
out.left = out.right
else:
raise ValueError("kind must be 'dichotic' or 'diotic'.")
return out
def whitenoise(duration=1.0,
kind='diotic',
samplerate=None,
normalise=True):
'''
Returns a white noise. If the samplerate is not specified, the global default value will be used. kind = 'diotic' produces the same noise samples in both channels, kind = 'dichotic' produces uncorrelated noise.
>>> noise = Binaural.whitenoise(kind='diotic')
'''
if kind == 'dichotic':
out = Binaural(
Sound.whitenoise(duration=duration,
nchannels=2,
samplerate=samplerate,
normalise=normalise))
elif kind == 'diotic':
out = Binaural(
Sound.whitenoise(duration=duration,
nchannels=2,
samplerate=samplerate,
normalise=normalise))
out.left = out.right
return out
def make_interaural_level_spectrum(hrtf=None):
"""
Compute the frequency band specific interaural intensity differences for all sound source azimuth's in
a head-related transfer function. For every azimuth in the hrtf, the respective transfer function is applied
to a sound. This sound is then divided into frequency sub-bands. The interaural level spectrum is the level
difference between right and left for each of these sub-bands for each azimuth.
When individual HRTFs are not avilable, the level spectrum of the KEMAR mannequin may be used (default).
Note that the computation may take a few minutes. Save the level spectrum to avoid re-computation, for instance
with pickle or numpy.save (see documentation on readthedocs for examples).
Arguments:
hrtf (None | slab.HRTF): The head-related transfer function used to compute the level spectrum. If None,
use the recordings from the KEMAR mannequin.
Returns:
(dict): A dictionary with keys `samplerate`, `frequencies` [n], `azimuths` [m], and `level_diffs` [n x m],
where `frequencies` lists the centres of sub-bands for which the level difference was computed, and
`azimuths` lists the sound source azimuth's in the hrft. `level_diffs` is a matrix of the interaural
level difference for each sub-band and azimuth.
Examples::
ils = slab.Binaural.make_interaural_level_spectrum() # get the ils from the KEMAR recordings
ils['samplerate'] # the sampling rate
ils['frequencies'] # the sub-band frequencies
ils['azimuths'] # the sound source azimuth's for which the level difference was calculated
ils['level_diffs'][5, :] # the level difference for each azimuth in the 5th sub-band
"""
if not hrtf:
hrtf = HRTF.kemar() # load KEMAR by default
# get the filters for the frontal horizontal arc
idx = numpy.where((hrtf.sources[:, 1] == 0) & (
(hrtf.sources[:, 0] <= 90) | (hrtf.sources[:, 0] >= 270)))[0]
# at this point, we could just get the transfer function of each filter with hrtf.data[idx[i]].tf(),
# but it may be better to get the spectral left/right differences with ERB-spaced frequency resolution:
azi = hrtf.sources[idx, 0]
# 270<azi<360 -> azi-360 to get negative angles on the left
azi[azi >= 270] = azi[azi >= 270]-360
sort = numpy.argsort(azi)
fbank = Filter.cos_filterbank(samplerate=hrtf.samplerate, pass_bands=True)
freqs = fbank.filter_bank_center_freqs()
noise = Sound.pinknoise(duration=5., samplerate=hrtf.samplerate)
ils = dict()
ils['samplerate'] = hrtf.samplerate
ils['frequencies'] = freqs
ils['azimuths'] = azi[sort]
ils['level_diffs'] = numpy.zeros((len(freqs), len(idx)))
for n, i in enumerate(idx[sort]): # put the level differences in order of increasing angle
noise_filt = Binaural(hrtf.data[i].apply(noise))
noise_bank_left = fbank.apply(noise_filt.left)
noise_bank_right = fbank.apply(noise_filt.right)
ils['level_diffs'][:, n] = noise_bank_right.level - noise_bank_left.level
return ils
def itd(self, duration=600e-6):
'''
Returns a binaural sound object with one channel delayed with respect to the other channel by duration (*600 microseconds*), which can be the number of samples or a length of time in seconds.
Negative dB values delay the right channel (virtual sound source moves to the left). itd requires a sound with two channels.
>>> sig = Binaural.whitenoise()
>>> _ = sig.itd(1)
>>> _ = sig.itd(-0.001)
'''
duration = Sound.in_samples(duration, self.samplerate)
new = copy.deepcopy(self) # so that we can return a new signal
if duration == 0:
return new # nothing needs to be shifted
if duration < 0: # negative itds by convention shift to the left (i.e. delay right channel)
channel = 1 # right
else:
channel = 0 # left
new.delay(duration=abs(duration), chan=channel)
return new
def _make_level_spectrum_filter(hrtf=None):
'''
Generate a level spectrum from the horizontal recordings in an HRTF file. The defaut Filter.cos_filterbank is used and the same filter bank has to be used when applying the level spectrum to a sound.
'''
from slab import DATAPATH
if not hrtf:
try:
ils = numpy.load(DATAPATH +
'KEMAR_interaural_level_spectrum.npy')
return ils
except FileNotFoundError:
hrtf = HRTF(
DATAPATH +
'mit_kemar_normal_pinna.sofa') # load the hrtf file
save_standard = True
# get the filters for the frontal horizontal arc
idx = numpy.where((hrtf.sources[:, 1] == 0) & (
(hrtf.sources[:, 0] <= 90) | (hrtf.sources[:, 0] >= 270)))[0]
# at this point, we could just get the transfer function of each filter with hrtf.data[idx[i]].tf(),
# but it may be better to get the spectral left/right differences with ERB-spaced frequency resolution:
azi = hrtf.sources[idx, 0]
# 270<azi<360 -> azi-360 to get negative angles the left
azi[azi >= 270] = azi[azi >= 270] - 360
sort = numpy.argsort(azi)
fbank = Filter.cos_filterbank(samplerate=hrtf.samplerate)
freqs = fbank.filter_bank_center_freqs()
noise = Sound.pinknoise(samplerate=hrtf.samplerate)
ils = numpy.zeros((len(freqs) + 1, len(idx) + 1))
ils[:, 0] = numpy.concatenate(
([0], freqs)) # first row are the frequencies
for n, i in enumerate(
idx[sort]
): # put the level differences in order of increasing angle
noise_filt = Binaural(hrtf.data[i].apply(noise))
noise_bank_left = fbank.apply(noise_filt.left)
noise_bank_right = fbank.apply(noise_filt.right)
ils[1:, n + 1] = noise_bank_right.level - noise_bank_left.level
ils[0, n + 1] = azi[sort[n]] # first entry is the angle
if save_standard:
numpy.save(DATAPATH + 'KEMAR_interaural_level_spectrum.npy', ils)
return ils
def click(**kwargs):
""" Identical to slab.Sound.click, but with two channels. """
return Binaural(Sound.click(n_channels=2, **kwargs))
def equally_masking_noise(**kwargs):
""" Identical to slab.Sound.erb_noise, but with two channels. """
return Binaural(Sound.equally_masking_noise(**kwargs))
class Binaural(Sound):
"""
Class for working with binaural sounds, including ITD and ILD manipulation. Binaural inherits all sound
generation functions from the Sound class, but returns binaural signals. Recasting an object of class sound or
sound with 1 or 3+ channels calls Sound.copychannel to return a binaural sound with two channels identical
to the first channel of the original sound.
Arguments:
data (slab.Signal | numpy.ndarray | list | str): see documentation of slab.Sound for details. the `data` must
have either one or two channels. If it has one, that channel is duplicated
samplerate (int): samplerate in Hz, must only be specified when creating an instance from an array.
Attributes:
.left: the first data channel, containing the sound for the left ear.
.right: the second data channel, containing the sound for the right ear
.data: the data-array of the Sound object which has the shape `n_samples` x `n_channels`.
.n_channels: the number of channels in `data`. Must be 2 for a binaural sound.
.n_samples: the number of samples in `data`. Equals `duration` * `samplerate`.
.duration: the duration of the sound in seconds. Equals `n_samples` / `samplerate`.
"""
# instance properties
def _set_left(self, other):
if hasattr(other, 'samplerate'): # probably an slab object
self.data[:, 0] = other.data[:, 0]
else:
self.data[:, 0] = numpy.array(other)
def _set_right(self, other):
if hasattr(other, 'samplerate'): # probably an slab object
self.data[:, 1] = other.data[:, 0]
else:
self.data[:, 1] = numpy.array(other)
left = property(fget=lambda self: Sound(self.channel(0)), fset=_set_left,
doc='The left channel for a stereo sound.')
right = property(fget=lambda self: Sound(self.channel(1)), fset=_set_right,
doc='The right channel for a stereo sound.')
def __init__(self, data, samplerate=None):
if isinstance(data, (Sound, Signal)):
if data.n_channels == 1: # if there is only one channel, duplicate it.
self.data = numpy.tile(data.data, 2)
elif data.n_channels == 2:
self.data = data.data
else:
raise ValueError("Data must have one or two channel!")
self.samplerate = data.samplerate
elif isinstance(data, (list, tuple)):
if isinstance(data[0], (Sound, Signal)):
if data[0].n_samples != data[1].n_samples:
raise ValueError('Sounds must have same number of samples!')
if data[0].samplerate != data[1].samplerate:
raise ValueError('Sounds must have same samplerate!')
super().__init__([data[0].data[:, 0], data[1].data[:, 0]], data[0].samplerate)
else:
super().__init__(data, samplerate)
elif isinstance(data, str):
super().__init__(data, samplerate)
if self.n_channels == 1:
self.data = numpy.tile(self.data, 2) # duplicate channel if monaural file
else:
super().__init__(data, samplerate)
if self.n_channels == 1:
self.data = numpy.tile(self.data, 2) # duplicate channel if monaural file
if self.n_channels != 2:
ValueError('Binaural sounds must have two channels!')
def itd(self, duration=None, max_lag=0.001):
"""
Either estimate the interaural time difference of the sound or generate a new sound with the specified
interaural time difference. The resolution for computing the ITD is 1/samplerate seconds. A negative
ITD value means that the right channel is delayed, meaning the sound source is to the left.
Arguments:
duration (None| int | float): Given None, the instance's ITD is computed. Given another value, a new sound
with the desired interaural time difference in samples (given an integer) or seconds (given a float)
is generated.
max_lag (float): Maximum possible value for ITD estimation. Defaults to 1 millisecond which is barely
outside the physiologically plausible range for humans. Is ignored if `duration` is specified.
Returns:
(int | slab.Binaural): The interaural time difference in samples or a copy of the instance with the
specified interaural time difference.
Examples::
sound = slab.Binaural.whitenoise()
lateral = sound.itd(duration=0.0005) # generate a sound with 0.5 ms ITD
lateral.itd() # estimate the ITD of the sound
"""
if duration is None:
return self._get_itd(max_lag)
return self._apply_itd(duration)
def _get_itd(self, max_lag):
max_lag = Sound.in_samples(max_lag, self.samplerate)
xcorr = numpy.correlate(self.data[:, 0], self.data[:, 1], 'full')
lags = numpy.arange(-max_lag, max_lag + 1)
xcorr = xcorr[self.n_samples - 1 - max_lag:self.n_samples + max_lag]
idx = numpy.argmax(xcorr)
return lags[idx]
def _apply_itd(self, duration):
if duration == 0:
return self # nothing needs to be shifted
if duration < 0: # negative itds by convention shift to the left (i.e. delay right channel)
channel = 1 # right
else:
channel = 0 # left
return self.delay(duration=abs(duration), channel=channel)
def ild(self, dB=None):
"""
Either estimate the interaural level difference of the sound or generate a new sound with the specified
interaural level difference. Negative ILD value means that the left channel is louder than the right
channel, meaning that the sound source is to the left. The level difference is achieved by adding half the ILD
to one channel and subtracting half from the other channel, so that the mean intensity remains constant.
Arguments:
dB (None | int | float): If None, estimate the sound's ITD. Given a value, a new sound is generated with
the desired interaural level difference in decibels.
Returns:
(float | slab.Binaural): The sound's interaural level difference, or a new sound with the specified ILD.
Examples::
sig = slab.Binaural.whitenoise()
lateral_right = sig.ild(3) # attenuate left channel by 1.5 dB and amplify right channel by the same amount
lateral_left = sig.ild(-3) # vice-versa
"""
if dB is None:
return self.right.level - self.left.level
new = copy.deepcopy(self) # so that we can return a new sound
level = numpy.mean(self.level)
new_levels = (level - dB/2, level + dB/2)
new.level = new_levels
return new
def itd_ramp(self, from_itd=-6e-4, to_itd=6e-4):
"""
Generate a sound with a linearly increasing or decreasing interaural time difference. This is achieved by sinc
interpolation of one channel with a dynamic delay. The resulting virtual sound source moves left or right.
Arguments:
from_itd (float): interaural time difference in seconds at the start of the sound.
Negative numbers correspond to sources to the left of the listener.
to_itd (float): interaural time difference in seconds at the end of the sound.
Returns:
(slab.Binaural): a copy of the instance wit the desired ITD ramp.
Examples::
sig = slab.Binaural.whitenoise()
moving = sig.itd_ramp(from_itd=-0.001, to_itd=0.01)
moving.play()
"""
new = copy.deepcopy(self)
# make the ITD ramps
left_ramp = numpy.linspace(from_itd / 2, to_itd / 2, self.n_samples)
right_ramp = numpy.linspace(-from_itd / 2, -to_itd / 2, self.n_samples)
if self.n_samples >= 8192:
filter_length = 1024
elif self.n_samples >= 512:
filter_length = self.n_samples // 16 * 2 # 1/8th of n_samples, always even
else:
raise ValueError('Signal too short! (min 512 samples)')
new = new.delay(duration=left_ramp, channel=0, filter_length=filter_length)
new = new.delay(duration=right_ramp, channel=1, filter_length=filter_length)
return new
def ild_ramp(self, from_ild=-50, to_ild=50):
"""
Generate a sound with a linearly increasing or decreasing interaural level difference. The resulting
virtual sound source moves to the left or right.
Arguments:
from_ild (int | float): interaural level difference in decibels at the start of the sound.
Negative numbers correspond to sources to the left of the listener.
to_ild (int | float): interaural level difference in decibels at the end of the sound.
Returns:
(slab.Binaural): a copy of the instance with the desired ILD ramp. Any previously existing level difference
is removed.
Examples::
sig = slab.Binaural.whitenoise()
moving = sig.ild_ramp(from_ild=-10, to_ild=10)
move.play()
"""
new = self.ild(0) # set ild to zero
# make ramps
left_ramp = numpy.linspace(-from_ild / 2, -to_ild / 2, self.n_samples)
right_ramp = numpy.linspace(from_ild / 2, to_ild / 2, self.n_samples)
left_ramp = 10**(left_ramp/20.)
right_ramp = 10**(right_ramp/20.)
# multiply channels with ramps
new.data[:, 0] *= left_ramp
new.data[:, 1] *= right_ramp
return new
@staticmethod
def azimuth_to_itd(azimuth, frequency=2000, head_radius=8.75):
"""
Compute the ITD for a sound source at a given azimuth. For frequencies >= 2 kHz the Woodworth (1962)
formula is used. For frequencies <= 500 Hz the low-frequency approximation mentioned in Aronson and Hartmann
(2014) is used. For frequencies in between, we interpolate linearly between the two formulas.
Arguments:
azimuth (int | float): The azimuth angle of the sound source, negative numbers refer to sources to the left.
frequency (int | float): Frequency in Hz for which the ITD is estimated.
Use the default for for sounds with a broadband spectrum.
head_radius (int | float): Radius of the head in centimeters. The bigger the head, the larger the ITD.
Returns:
(float): The interaural time difference for a sound source at a given azimuth.
Examples::
# compute the ITD for a sound source 90 degrees to the left for a large head
itd = slab.Binaural.azimuth_to_itd(-90, head_radius=10)
"""
head_radius = head_radius / 100
azimuth_radians = numpy.radians(azimuth)
speed_of_sound = 344 # m/s
itd_2000 = (head_radius / speed_of_sound) * \
(azimuth_radians + numpy.sin(azimuth_radians)) # Woodworth
itd_500 = (3 * head_radius / speed_of_sound) * numpy.sin(azimuth_radians)
itd = numpy.interp(frequency, [500, 2000], [itd_500, itd_2000],
left=itd_500, right=itd_2000)
return itd
@staticmethod
def azimuth_to_ild(azimuth, frequency=2000, ils=None):
"""
Get the interaural level difference corresponding to a sound source at a given azimuth and frequency.
Arguments:
azimuth (int | float): The azimuth angle of the sound source, negative numbers refer to sources to the left.
frequency (int | float): Frequency in Hz for which the ITD is estimated.
Use the default for for sounds with a broadband spectrum.
ils (dict | None): interaural level spectrum from which the ILD is taken. If None,
`make_interaural_level_spectrum()` is called. For repeated use, it is better to generate and keep the ils in
a variable to avoid re-computing it.
Returns:
(float): The interaural level difference for a sound source at a given azimuth in decibels.
Examples::
ils = slab.Binaural.make_interaural_level_spectrum() # using default KEMAR HRTF
ild = slab.Binaural.azimuth_to_ild(-90, ils=ils) # ILD equivalent to 90 deg leftward source for KEMAR
"""
if ils is None:
ils = Binaural.make_interaural_level_spectrum()
# interpolate levels at azimuth:
level_diffs = ils['level_diffs']
levels = [numpy.interp(azimuth, ils['azimuths'], level_diffs[i, :]) for i in range(level_diffs.shape[0])]
return numpy.interp(frequency, ils['frequencies'], levels)*-1 # interpolate level difference at frequency
def at_azimuth(self, azimuth=0, ils=None):
"""
Convenience function for adding ITD and ILD corresponding to the given `azimuth` to the sound source.
Values are obtained from azimuth_to_itd and azimuth_to_ild.
Frequency parameters for these functions are generated from the centroid frequency of the sound.
Arguments:
azimuth (int | float): The azimuth angle of the sound source, negative numbers refer to sources to the left.
ils (dict | None): interaural level spectrum from which the ILD is taken. If None,
`make_interaural_level_spectrum()` is called. For repeated use, it is better to generate and keep the ils in
a variable to avoid re-computing it.
Returns:
(slab.Binaural): a sound with the appropriate ITD and ILD applied
"""
centroid = numpy.array(self.spectral_feature(feature='centroid')).mean()
itd = Binaural.azimuth_to_itd(azimuth, frequency=centroid)
ild = Binaural.azimuth_to_ild(azimuth, frequency=centroid, ils=ils)
out = self.itd(duration=itd)
return out.ild(dB=ild)
def externalize(self, hrtf=None):
"""
Convolve the sound with a smoothed HRTF to evoke the impression of an external sound source without adding
directional information, see Kulkarni & Colburn (1998) for why that works.
Arguments:
hrtf (None | slab.HRTF): The HRTF to use. If None use the one from the MIT KEMAR mannequin. The sound
source at zero azimuth and elevation is used for convolution so it has to be present in the HRTF.
Returns:
(slab.Binaural): externalized copy of the instance.
"""
if hrtf is None:
hrtf = HRTF.kemar() # load KEMAR as default
# get HRTF for [0,0] direction:
idx_frontal = numpy.where((hrtf.sources[:, 1] == 0) & (hrtf.sources[:, 0] == 0))[0][0]
if not idx_frontal.size: # idx_frontal is empty
raise ValueError('No frontal direction [0,0] found in HRTF.')
_, h = hrtf.data[idx_frontal].tf(channels=0, n_bins=12, show=False) # get low-res version of HRTF spectrum
h[0] = 1 # avoids low-freq attenuation in KEMAR HRTF (unproblematic for other HRTFs)
resampled_signal = copy.deepcopy(self)
# if sound and HRTF has different samplerates, resample the sound, apply the HRTF, and resample back:
resampled_signal = resampled_signal.resample(hrtf.data[0].samplerate) # resample to hrtf rate
filt = Filter(10**(h/20), fir=False, samplerate=hrtf.data[0].samplerate)
filtered_signal = filt.apply(resampled_signal)
filtered_signal = filtered_signal.resample(self.samplerate)
return filtered_signal
@staticmethod
def make_interaural_level_spectrum(hrtf=None):
"""
Compute the frequency band specific interaural intensity differences for all sound source azimuth's in
a head-related transfer function. For every azimuth in the hrtf, the respective transfer function is applied
to a sound. This sound is then divided into frequency sub-bands. The interaural level spectrum is the level
difference between right and left for each of these sub-bands for each azimuth.
When individual HRTFs are not avilable, the level spectrum of the KEMAR mannequin may be used (default).
Note that the computation may take a few minutes. Save the level spectrum to avoid re-computation, for instance
with pickle or numpy.save (see documentation on readthedocs for examples).
Arguments:
hrtf (None | slab.HRTF): The head-related transfer function used to compute the level spectrum. If None,
use the recordings from the KEMAR mannequin.
Returns:
(dict): A dictionary with keys `samplerate`, `frequencies` [n], `azimuths` [m], and `level_diffs` [n x m],
where `frequencies` lists the centres of sub-bands for which the level difference was computed, and
`azimuths` lists the sound source azimuth's in the hrft. `level_diffs` is a matrix of the interaural
level difference for each sub-band and azimuth.
Examples::
ils = slab.Binaural.make_interaural_level_spectrum() # get the ils from the KEMAR recordings
ils['samplerate'] # the sampling rate
ils['frequencies'] # the sub-band frequencies
ils['azimuths'] # the sound source azimuth's for which the level difference was calculated
ils['level_diffs'][5, :] # the level difference for each azimuth in the 5th sub-band
"""
if not hrtf:
hrtf = HRTF.kemar() # load KEMAR by default
# get the filters for the frontal horizontal arc
idx = numpy.where((hrtf.sources[:, 1] == 0) & (
(hrtf.sources[:, 0] <= 90) | (hrtf.sources[:, 0] >= 270)))[0]
# at this point, we could just get the transfer function of each filter with hrtf.data[idx[i]].tf(),
# but it may be better to get the spectral left/right differences with ERB-spaced frequency resolution:
azi = hrtf.sources[idx, 0]
# 270<azi<360 -> azi-360 to get negative angles on the left
azi[azi >= 270] = azi[azi >= 270]-360
sort = numpy.argsort(azi)
fbank = Filter.cos_filterbank(samplerate=hrtf.samplerate, pass_bands=True)
freqs = fbank.filter_bank_center_freqs()
noise = Sound.pinknoise(duration=5., samplerate=hrtf.samplerate)
ils = dict()
ils['samplerate'] = hrtf.samplerate
ils['frequencies'] = freqs
ils['azimuths'] = azi[sort]
ils['level_diffs'] = numpy.zeros((len(freqs), len(idx)))
for n, i in enumerate(idx[sort]): # put the level differences in order of increasing angle
noise_filt = Binaural(hrtf.data[i].apply(noise))
noise_bank_left = fbank.apply(noise_filt.left)
noise_bank_right = fbank.apply(noise_filt.right)
ils['level_diffs'][:, n] = noise_bank_right.level - noise_bank_left.level
return ils
def interaural_level_spectrum(self, azimuth, ils=None):
"""
Apply an interaural level spectrum, corresponding to a sound sources azimuth, to a
binaural sound. The interaural level spectrum consists of frequency specific interaural level differences
which are computed from a head related transfer function (see the `make_interaural_level_spectrum()` method).
The binaural sound is divided into frequency sub-bands and the levels of each sub-band are set according to
the respective level in the interaural level spectrum. Then, the sub-bands are summed up again into one
binaural sound.
Arguments:
azimuth (int | float): azimuth for which the interaural level spectrum is calculated.
ils (dict): interaural level spectrum to apply. If None, `make_interaural_level_spectrum()` is called.
For repeated use, it is better to generate and keep the ils in a variable to avoid re-computing it.
Returns:
(slab.Binaural): A binaural sound with the interaural level spectrum corresponding to the given azimuth.
Examples::
noise = slab.Binaural.pinknoise(kind='diotic')
ils = slab.Binaural.make_interaural_level_spectrum() # using default KEMAR HRTF
noise.interaural_level_spectrum(azimuth=-45, ils=ils).play()
"""
if ils is None:
ils = Binaural.make_interaural_level_spectrum()
ils_samplerate = ils['samplerate']
original_samplerate = self.samplerate
azis = ils['azimuths']
level_diffs = ils['level_diffs']
levels = numpy.array([numpy.interp(azimuth, azis, level_diffs[i, :]) for i in range(level_diffs.shape[0])])
# resample the signal to the rate of the HRTF from which the filter was computed:
resampled = self.resample(samplerate=ils_samplerate)
fbank = Filter.cos_filterbank(length=resampled.n_samples, samplerate=ils_samplerate, pass_bands=True)
subbands_left = fbank.apply(resampled.left)
subbands_right = fbank.apply(resampled.right)
# change subband levels:
subbands_left.level = subbands_left.level + levels / 2
subbands_right.level = subbands_right.level - levels / 2
out_left = Filter.collapse_subbands(subbands_left, filter_bank=fbank)
out_right = Filter.collapse_subbands(subbands_right, filter_bank=fbank)
out = Binaural([out_left, out_right])
return out.resample(samplerate=original_samplerate)
@staticmethod
def whitenoise(kind='diotic', **kwargs):
"""
Generate binaural white noise. `kind`='diotic' produces the same noise samples in both channels,
`kind`='dichotic' produces uncorrelated noise. The rest is identical to `slab.Sound.whitenoise`.
"""
out = Binaural(Sound.whitenoise(**kwargs))
if kind == 'diotic':
out.left = out.right
return out
@staticmethod
def pinknoise(kind='diotic', **kwargs):
"""
Generate binaural pink noise. `kind`='diotic' produces the same noise samples in both channels,
`kind`='dichotic' produces uncorrelated noise. The rest is identical to `slab.Sound.pinknoise`.
"""
return Binaural.powerlawnoise(alpha=1, **kwargs)
@staticmethod
def powerlawnoise(kind='diotic', **kwargs):
"""
Generate binaural power law noise. `kind`='diotic' produces the same noise samples in both channels,
`kind`='dichotic' produces uncorrelated noise. The rest is identical to `slab.Sound.powerlawnoise`.
"""
if kind == 'dichotic':
out = Binaural(Sound.powerlawnoise(n_channels=2, **kwargs))
out.left = Sound.powerlawnoise(n_channels=1, **kwargs)
elif kind == 'diotic':
out = Binaural(Sound.powerlawnoise(n_channels=2, **kwargs))
out.left = out.right
else:
raise ValueError("kind must be 'dichotic' or 'diotic'.")
return out
@staticmethod
def irn(kind='diotic', **kwargs):
"""
Generate iterated ripple noise (IRN). `kind`='diotic' produces the same noise samples in both channels,
`kind`='dichotic' produces uncorrelated noise. The rest is identical to `slab.Sound.irn`.
"""
out = Binaural(Sound.irn(n_channels=2, **kwargs))
if kind == 'diotic':
out.left = out.right
else:
raise ValueError("kind must be 'dichotic' or 'diotic'.")
return out
@staticmethod
def tone(**kwargs):
""" Identical to slab.Sound.tone, but with two channels. """
return Binaural(Sound.tone(n_channels=2, **kwargs))
@staticmethod
def harmoniccomplex(**kwargs):
""" Identical to slab.Sound.harmoniccomplex, but with two channels. """
return Binaural(Sound.harmoniccomplex(n_channels=2, **kwargs))
@staticmethod
def click(**kwargs):
""" Identical to slab.Sound.click, but with two channels. """
return Binaural(Sound.click(n_channels=2, **kwargs))
@staticmethod
def clicktrain(**kwargs):
""" Identical to slab.Sound.clicktrain, but with two channels. """
return Binaural(Sound.clicktrain(**kwargs))
@staticmethod
def chirp(**kwargs):
""" Identical to slab.Sound.chirp, but with two channels. """
return Binaural(Sound.chirp(**kwargs))
@staticmethod
def silence(**kwargs):
""" Identical to slab.Sound.silence, but with two channels. """
return Binaural(Sound.silence(**kwargs))
@staticmethod
def vowel(**kwargs):
""" Identical to slab.Sound.vowel, but with two channels. """
return Binaural(Sound.vowel(**kwargs))
@staticmethod
def multitone_masker(**kwargs):
""" Identical to slab.Sound.multitone_masker, but with two channels. """
return Binaural(Sound.multitone_masker(**kwargs))
@staticmethod
def equally_masking_noise(**kwargs):
""" Identical to slab.Sound.erb_noise, but with two channels. """
return Binaural(Sound.equally_masking_noise(**kwargs))
def vowel(**kwargs):
""" Identical to slab.Sound.vowel, but with two channels. """
return Binaural(Sound.vowel(**kwargs))
def multitone_masker(**kwargs):
""" Identical to slab.Sound.multitone_masker, but with two channels. """
return Binaural(Sound.multitone_masker(**kwargs))
def silence(**kwargs):
""" Identical to slab.Sound.silence, but with two channels. """
return Binaural(Sound.silence(**kwargs))
def chirp(**kwargs):
""" Identical to slab.Sound.chirp, but with two channels. """
return Binaural(Sound.chirp(**kwargs))
def clicktrain(**kwargs):
""" Identical to slab.Sound.clicktrain, but with two channels. """
return Binaural(Sound.clicktrain(**kwargs))
def tone(**kwargs):
""" Identical to slab.Sound.tone, but with two channels. """
return Binaural(Sound.tone(n_channels=2, **kwargs))
def harmoniccomplex(**kwargs):
""" Identical to slab.Sound.harmoniccomplex, but with two channels. """
return Binaural(Sound.harmoniccomplex(n_channels=2, **kwargs))
