| | import numpy as np |
| | import math |
| | from scipy.io import wavfile |
| | from scipy import stats |
| |
|
| | 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) |
| |
|
| | import soundfile as sf |
| | from multiprocessing import Pool |
| |
|
| | def t60_impulse(raw_signal,fs): |
| | """ |
| | 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]) |
| |
|
| | if np.max(raw_signal)==0 and np.min(raw_signal)==0: |
| | print('came 1') |
| | return .5 |
| | |
| | |
| | band_type = _check_band_type(bands) |
| |
|
| | |
| | low = octave_low(bands[0], bands[-1]) |
| | high = octave_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): |
| | |
| | filtered_signal = bandpass(raw_signal, low[band], high[band], fs, order=8) |
| | abs_signal = np.abs(filtered_signal) / np.max(np.abs(filtered_signal)) |
| |
|
| | |
| | sch = np.cumsum(abs_signal[::-1]**2)[::-1] |
| | sch_db = 10.0 * np.log10(sch / np.max(sch)) |
| | if math.isnan(sch_db[1]): |
| | print('came 2') |
| | return .5 |
| | |
| | |
| | |
| | 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] |
| |
|
| | |
| | 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 |
| | |
| | if math.isnan(mean_t60): |
| | print('came 3') |
| | return .5 |
| | return mean_t60 |
| |
|
| | def t60_error(filename1,filename2): |
| | real_wave,fs = sf.read(filename1) |
| | fake_wave,fs = sf.read(filename2) |
| |
|
| | channel = int(real_wave.size/len(real_wave)) |
| | pool = Pool(processes=8) |
| | |
| | results =[] |
| | for n in range(channel): |
| | results.append(pool.apply_async(t60_parallel, args=(n,real_wave,fake_wave,fs,))) |
| | |
| | T60_error =0 |
| | for result in results: |
| | T60_error = T60_error + result.get() |
| |
|
| | T60_error = T60_error/channel |
| | |
| | pool.close() |
| | pool.join() |
| |
|
| |
|
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | return str(T60_error) |
| |
|
| | def t60_parallel(n,real_wave,fake_wave,fs): |
| | real_wave_single = real_wave[n,:] |
| | fake_wave_single = fake_wave[n,:] |
| | Real_T60_val = t60_impulse(real_wave_single,fs) |
| | Fake_T60_val = t60_impulse(fake_wave_single,fs) |
| | T60_diff = abs(Real_T60_val-Fake_T60_val) |
| |
|
| | return T60_diff |
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| | if __name__ == '__main__': |
| | t60_impulse('/home/anton/Desktop/gamma101/data/evaluation_all/SF1/Hotel_SkalskyDvur_ConferenceRoom2-MicID01-SpkID01_20170906_S-09-RIR-IR_sweep_15s_45Hzto22kHz_FS16kHz.v00.wav') |
| | |