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import numpy as np
# import librosa
from scipy.io import wavfile
from scipy import stats
import soundfile as sf
from acoustics.utils import _is_1d
from acoustics.signal import bandpass
from acoustics.bands import (_check_band_type, octave_low, octave_high, third_low, third_high)
def t60_impulse(file_name): # pylint: disable=too-many-locals
"""
Reverberation time from a WAV impulse response.
:param file_name: name of the WAV file containing the impulse response.
:param bands: Octave or third bands as NumPy array.
:param rt: Reverberation time estimator. It accepts `'t30'`, `'t20'`, `'t10'` and `'edt'`.
:returns: Reverberation time :math:`T_{60}`
"""
bands =np.array([62.5 ,125, 250, 500,1000, 2000])
fs =16000;
# raw_signal, _ = librosa.load(file_name, sr=fs, mono=True, duration=1)
# fs, raw_signal = wavfile.read(file_name)
raw_signal,fs = sf.read(file_name)
band_type = _check_band_type(bands)
# if band_type == 'octave':
low = octave_low(bands[0], bands[-1])
high = octave_high(bands[0], bands[-1])
# elif band_type == 'third':
# low = third_low(bands[0], bands[-1])
# high = third_high(bands[0], bands[-1])
init = -0.0
end = -60.0
factor = 1.0
bands =bands[3:5]
low = low[3:5]
high = high[3:5]
t60 = np.zeros(bands.size)
for band in range(bands.size):
# Filtering signal
filtered_signal = bandpass(raw_signal, low[band], high[band], fs, order=8)
abs_signal = np.abs(filtered_signal) / np.max(np.abs(filtered_signal))
# Schroeder integration
sch = np.cumsum(abs_signal[::-1]**2)[::-1]
sch_db = 10.0 * np.log10(sch / np.max(sch))
# Linear regression
sch_init = sch_db[np.abs(sch_db - init).argmin()]
sch_end = sch_db[np.abs(sch_db - end).argmin()]
init_sample = np.where(sch_db == sch_init)[0][0]
end_sample = np.where(sch_db == sch_end)[0][0]
x = np.arange(init_sample, end_sample + 1) / fs
y = sch_db[init_sample:end_sample + 1]
slope, intercept = stats.linregress(x, y)[0:2]
# Reverberation time (T30, T20, T10 or EDT)
db_regress_init = (init - intercept) / slope
db_regress_end = (end - intercept) / slope
t60[band] = factor * (db_regress_end - db_regress_init)
mean_t60 =(t60[1]+t60[0])/2
return mean_t60
def t60_error(file_name1,file_name2):
RT_real = t60_impulse(file_name1)
RT_fake = t60_impulse(file_name2)
RT_diff = abs(RT_real-RT_fake)
return str(RT_diff)
if __name__ == '__main__':
t60_impulse('/home/anton/Anton/data/vcc2016_training/SF1/VUT_FIT_D105-MicID01-SpkID04_20170901_S-12-RIR-IR_sweep_15s_45Hzto22kHz_FS16kHz.v00.wav')
# t60_impulse('/home/anton/Desktop/data/vcc2016_training/SF1/2.wav')
# t60_impulse('/home/anton/Desktop/data/vcc2016_training/SF1/3.wav')
# t60_impulse('/home/anton/Desktop/data/vcc2016_training/SF1/4.wav')
# t60_impulse('/home/anton/Desktop/data/vcc2016_training/SF1/5.wav')
# t60_impulse('/home/anton/Desktop/data/vcc2016_training/SF1/6.wav')
# t60_impulse('/home/anton/Desktop/data/vcc2016_training/SF1/7.wav')
# t60_impulse('/home/anton/Desktop/data/vcc2016_training/SF1/8.wav')
# t60_impulse('/home/anton/Desktop/data/vcc2016_training/SF1/9.wav')
# t60_impulse('/home/anton/Desktop/data/vcc2016_training/SF1/10.wav') |