def spectrogram(wavtemp, fs):
auditory_params = load_auditory_params()
new_fs = auditory_params['newFs']
durationCut = auditory_params['durationCut']
durationRCosDecay = auditory_params['durationRCosDecay']
sr_time = auditory_params['sr_time']
wavtemp = np.r_[wavtemp, np.zeros(new_fs)]
if wavtemp.shape[0] > math.floor(durationCut * fs):
wavtemp = wavtemp[:int(durationCut * fs)]
wavtemp[wavtemp.shape[0] - int(fs * durationRCosDecay):] = wavtemp[
wavtemp.shape[0] - int(
fs * durationRCosDecay):] * utils.raised_cosine(
np.arange(int(fs * durationRCosDecay)), 0,
int(fs * durationRCosDecay))
wavtemp = (wavtemp / 1.01) / (np.max(wavtemp) + np.finfo(float).eps)
wavtemp = signal.resample(wavtemp, int(wavtemp.shape[0] / fs * new_fs))
waveform2auditoryspectrogram_args = {
'frame_length':
1000 / sr_time, # sample rate 125 Hz in the NSL toolbox
'time_constant': 8,
'compression_factor': -2,
'octave_shift': math.log2(new_fs / new_fs),
'filt': 'p',
'VERB': 0
}
auditory_spec = features.waveform2auditoryspectrogram(
wavtemp, **waveform2auditoryspectrogram_args)
return auditory_spec
def mps(wavtemp, fs):
auditory_params = load_auditory_params()
new_fs = auditory_params['newFs']
durationCut = auditory_params['durationCut']
durationRCosDecay = auditory_params['durationRCosDecay']
sr_time = auditory_params['sr_time']
wavtemp = np.r_[wavtemp, np.zeros(new_fs)]
if wavtemp.shape[0] > math.floor(durationCut * fs):
wavtemp = wavtemp[:int(durationCut * fs)]
wavtemp[wavtemp.shape[0] - int(fs * durationRCosDecay):] = wavtemp[
wavtemp.shape[0] - int(
fs * durationRCosDecay):] * utils.raised_cosine(
np.arange(int(fs * durationRCosDecay)), 0,
int(fs * durationRCosDecay))
wavtemp = (wavtemp / 1.01) / (np.max(wavtemp) + np.finfo(float).eps)
wavtemp = signal.resample(wavtemp, int(wavtemp.shape[0] / fs * new_fs))
waveform2auditoryspectrogram_args = {
'frame_length':
1000 / sr_time, # sample rate 125 Hz in the NSL toolbox
'time_constant': 8,
'compression_factor': -2,
'octave_shift': math.log2(new_fs / new_fs),
'filt': 'p',
'VERB': 0
}
stft = features.waveform2auditoryspectrogram(
wavtemp, **waveform2auditoryspectrogram_args)
strf_args = {
'num_channels': 128,
'num_ch_oct': 24,
'sr_time': sr_time,
'nfft_rate': 2 * 2**utils.nextpow2(stft.shape[0]),
'nfft_scale': 2 * 2**utils.nextpow2(stft.shape[1]),
'KIND': 2
}
# Spectro-temporal modulation analysis
# Based on Hemery & Aucouturier (2015) Frontiers Comp Neurosciences
# nfft_fac = 2 # multiplicative factor for nfft_scale and nfft_rate
# nfft_scale = nfft_fac * 2**utils.nextpow2(stft.shape[1])
mod_scale, phase_scale, _, _ = features.spectrum2scaletime(
stft, **strf_args)
# plt.imshow(phase_scale)
# plt.colorbar()
# plt.show()
# Scales vs. Time => Scales vs. Rates
repres, phase_scale_rate, _, _ = features.scaletime2scalerate(
mod_scale * np.exp(1j * phase_scale), **strf_args)
# repres = repres[:, :int(repres.shape[1] / 2)]
return repres
def spectrogram(wavtemp,
audio_fs=44100,
duration=0.25,
duration_cut_decay=0.05,
resampling_fs=16000,
sr_time=250,
offset=0.0):
auditory_params = load_static_params()
# resampling_fs = auditory_params['newFs']
# duration = auditory_params['duration']
# duration_cut_decay = auditory_params['duration_cut_decay']
sr_time = auditory_params['sr_time']
wavtemp = np.r_[wavtemp, np.zeros(resampling_fs)]
# print(duration)
if duration == -1:
print('no duration cut')
elif wavtemp.shape[0] > math.floor(duration * audio_fs):
offset_n = int(offset * audio_fs)
duration_n = int(duration * audio_fs)
duration_decay_n = int(duration_cut_decay * audio_fs)
wavtemp = wavtemp[offset_n:offset_n + duration_n]
if offset_n == 0:
wavtemp[wavtemp.shape[0] - duration_decay_n:] = wavtemp[
wavtemp.shape[0] - duration_decay_n:] * utils.raised_cosine(
np.arange(duration_decay_n), 0, duration_decay_n)
else:
wavtemp[wavtemp.shape[0] - duration_decay_n:] = wavtemp[
wavtemp.shape[0] - duration_decay_n:] * utils.raised_cosine(
np.arange(duration_decay_n), 0, duration_decay_n)
wavtemp[:
duration_decay_n] = wavtemp[:duration_decay_n] * utils.raised_cosine(
np.arange(duration_decay_n), duration_decay_n,
duration_decay_n)
wavtemp = (wavtemp / 1.01) / (np.max(wavtemp) + np.finfo(float).eps)
wavtemp = signal.resample(wavtemp,
int(wavtemp.shape[0] / audio_fs * resampling_fs))
waveform2auditoryspectrogram_args = {
'frame_length':
1000 / sr_time, # sample rate 125 Hz in the NSL toolbox
'time_constant': 8,
'compression_factor': -2,
'octave_shift': math.log2(resampling_fs / resampling_fs),
'filt': 'p',
'VERB': 0
}
auditory_spectrogram_ = features.waveform2auditoryspectrogram(
wavtemp, **waveform2auditoryspectrogram_args)
return auditory_spectrogram_
def strf(wavtemp, fs):
auditory_params = load_auditory_params()
scales = auditory_params['scales']
rates = auditory_params['rates']
durationCut = auditory_params['durationCut']
durationRCosDecay = auditory_params['durationRCosDecay']
new_fs = auditory_params['newFs']
sr_time = auditory_params['sr_time']
wavtemp = np.r_[wavtemp, np.zeros(new_fs)]
if wavtemp.shape[0] > math.floor(durationCut * fs):
wavtemp = wavtemp[:int(durationCut * fs)]
wavtemp[wavtemp.shape[0] - int(fs * durationRCosDecay):] = wavtemp[
wavtemp.shape[0] - int(
fs * durationRCosDecay):] * utils.raised_cosine(
np.arange(int(fs * durationRCosDecay)), 0,
int(fs * durationRCosDecay))
else:
wavtemp = np.r_[wavtemp,
np.zeros(
np.abs(len(wavtemp) - int(durationCut * fs)) + 10)]
wavtemp = (wavtemp / 1.01) / (np.max(wavtemp) + np.finfo(float).eps)
wavtemp = signal.resample(wavtemp, int(wavtemp.shape[0] / fs * new_fs))
# Peripheral auditory model (from NSL toolbox)
# # compute spectrogram with waveform2auditoryspectrogram (from NSL toolbox), first f0 = 180 Hz
# num_channels = 128 # nb channels (128 ch. in the NSL toolbox)
# num_ch_oct = 24 # nb channels per octaves (24 ch/oct in the NSL toolbox)
# sr_time = 125 # sample rate (125 Hz in the NSL toolbox)
waveform2auditoryspectrogram_args = {
'frame_length':
1000 / sr_time, # sample rate 125 Hz in the NSL toolbox
'time_constant': 8,
'compression_factor': -2,
'octave_shift': math.log2(new_fs / new_fs),
'filt': 'p',
'VERB': 0
}
# frame_length = 1000 / sr_time # frame length (in ms)
# time_constant = 8 # time constant (lateral inhibitory network)
# compression_factor = -2
# # fac = 0, y = (x > 0), full compression, booleaner.
# # fac = -1, y = max(x, 0), half-wave rectifier
# # fac = -2, y = x, linear function
# octave_shift = math.log2(new_fs / new_fs) # octave shift
stft = features.waveform2auditoryspectrogram(
wavtemp, **waveform2auditoryspectrogram_args)
strf_args = {
'num_channels': 128,
'num_ch_oct': 24,
'sr_time': sr_time,
'nfft_rate': 2 * 2**utils.nextpow2(stft.shape[0]),
'nfft_scale': 2 * 2**utils.nextpow2(stft.shape[1]),
'KIND': 2
}
# Spectro-temporal modulation analysis
# Based on Hemery & Aucouturier (2015) Frontiers Comp Neurosciences
# nfft_fac = 2 # multiplicative factor for nfft_scale and nfft_rate
# nfft_scale = nfft_fac * 2**utils.nextpow2(stft.shape[1])
mod_scale, phase_scale, _, _ = features.spectrum2scaletime(
stft, **strf_args)
# Scales vs. Time => Scales vs. Rates
# nfft_rate = nfft_fac * 2**utils.nextpow2(stft.shape[0])
scale_rate, phase_scale_rate, _, _ = features.scaletime2scalerate(
mod_scale * np.exp(1j * phase_scale), **strf_args)
# print(scale_rate.shape)
# print(phase_scale_rate.shape)
#num_channels, num_ch_oct, sr_time, nfft_rate, nfft_scale)
cortical_rep = features.scalerate2cortical(
stft, scale_rate, phase_scale_rate, scales, rates, **strf_args)
# print(cortical_rep.shape)
#num_ch_oct, sr_time, nfft_scale, nfft_rate, 2)
return cortical_rep