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Fixed da noise mixing bug

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This commit is contained in:
Sam Perry
2019-01-17 21:33:28 +00:00
parent 9ca1f9b152
commit 740baaf793
5 changed files with 38 additions and 22 deletions
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calibration/gen_calibration_stimulus.py
+5 -2
View File
@@ -36,7 +36,8 @@ def main():
da_files = ["../da_stim/stimulus/3000_da.wav"]
story_dir = "../eeg_story_stim/stimulus"
mat_dir = "../matrix_test/speech_components"
noise_file = "../matrix_test/behavioural_stim/stimulus/wav/noise/noise.wav"
noise_file = "../matrix_test/behavioural_stim/stimulus/wav/noise/noise_norm.wav"
da_noise_file = "../da_stim/noise/wav/noise/noise_norm.wav"
story_wavs = globDir(story_dir, '*.wav')
mat_wavs = globDir(mat_dir, '*.wav')
@@ -47,9 +48,11 @@ def main():
dir_must_exist(out_dir)
dir_must_exist(out_red_dir)
dir_must_exist(out_stim_dir)
import pdb
pdb.set_trace()
story_coef = calc_potential_max(story_wavs, noise_file, out_red_dir, "story_red_coef")
mat_coef = calc_potential_max(mat_wavs, noise_file, out_red_dir, "mat_red_coef")
da_coef = calc_potential_max(da_files, noise_file, out_red_dir, "da_red_coef")
da_coef = calc_potential_max(da_files, da_noise_file, out_red_dir, "da_red_coef")
mat_cal_stim = "../matrix_test/long_concat_stim/out/stim/stim_0.wav"
da_cal_stim = "../da_stim/stimulus/wav/10min_da.wav"
da_stim/gen_da_shaped_noise.py → da_stim/gen_noise.py
+16 -16
View File
@@ -20,9 +20,9 @@ from gen_da import gen_da_stim
from pathops import dir_must_exist
try:
from signalops import rolling_window_lastaxis, calc_rms
from signalops import rolling_window_lastaxis, window_rms, block_process_wav
except ImportError:
from .signalops import rolling_window_lastaxis, block_lfilter, calc_rms
from .signalops import rolling_window_lastaxis, block_lfilter, window_rms, block_process_wav
import scipy.signal as sgnl
from scipy.stats import pearsonr
@@ -48,6 +48,7 @@ def block_lfilter_wav(b, a, x, outfile, fmt, fs, blocksize=8192):
sndfile = PySndfile(outfile, 'w', fmt, 1, fs)
i = 0
y_out = np.zeros(x.size)
y_max = 0.0
while i < x.size:
print("Filtering {0} to {1} of {2}".format(i, i+blocksize, x.size))
if i+blocksize > x.size:
@@ -58,8 +59,9 @@ def block_lfilter_wav(b, a, x, outfile, fmt, fs, blocksize=8192):
y, new_state = sgnl.lfilter(b,a,x[i:i+blocksize], zi=new_state)
sndfile.write_frames(y)
y_out[i:i+y.size] = y
y_max = np.max([y_max, np.abs(y).max()])
i += blocksize
return y_out
return y_out, y_max
def synthesize_trial(wavFileMatrix, indexes):
@@ -140,9 +142,8 @@ def gen_rms_peak(files, OutRMSDir, OutPeakDir):
rmsFilepath = os.path.join(OutRMSDir, tail)
print("Generating: "+rmsFilepath)
y, fs, _ = sndio.read(file)
y = y[:, 1]
y_rms = calc_rms(y, round(0.02*fs))
np.save(rmsFilepath, peak)
y_rms = window_rms(y, round(0.02*fs))
np.save(rmsFilepath, y_rms)
rmsFiles.append(rmsFilepath)
head, tail = os.path.split(file)
@@ -152,7 +153,6 @@ def gen_rms_peak(files, OutRMSDir, OutPeakDir):
peakFilepath = os.path.join(OutPeakDir, tail)
print("Generating: "+peakFilepath)
peak = np.abs(y).max()
pdb.set_trace()
np.save(peakFilepath, peak)
peakFiles.append(peakFilepath)
return rmsFiles, peakFiles
@@ -180,7 +180,6 @@ def calc_spectrum(files, silences, fs=44100, plot=False):
print("Calculating LTASS...")
for ind, file in enumerate(files):
x, fs, _ = sndio.read(file)
x = x[:, 1]
f, t, Zxx = sgnl.stft(x, window=np.ones(window), nperseg=window, noverlap=0)
sil = silences[ind]
sTemp = np.zeros((sil.shape[0], t.size), dtype=bool)
@@ -215,7 +214,9 @@ def gen_noise(OutDir, b, fs, s_rms):
dir_must_exist(noiseDir)
noiseDir = os.path.join(noiseDir, 'noise')
dir_must_exist(noiseDir)
y = block_lfilter_wav(b, [1.0], x, os.path.join(noiseDir, 'noise.wav'), 65538, 44100)
y, y_max = block_lfilter_wav(b, [1.0], x, os.path.join(noiseDir, 'noise.wav'), 65538, 44100)
block_process_wav(os.path.join(noiseDir, 'noise.wav'), os.path.join(noiseDir, 'noise_norm.wav'), lambda x: x / (y_max * 0.95))
y = y/(np.abs(y).max() * 0.95)
noise_rms_path = os.path.join(noiseRMSDir, 'noise_rms.npy')
rms = np.sqrt(np.mean(y**2))
np.save(noise_rms_path, rms)
@@ -230,7 +231,6 @@ def calc_speech_rms(files, silences, rmsDir, fs=44100, plot=False):
n = 0
for wavfile, sil in zip(f, silences):
y, fs, _ = sndio.read(wavfile)
y = y[:, 1]
t = np.arange(y.size)
sTemp = np.zeros(t.size, dtype=bool)
print("Started")
@@ -255,7 +255,7 @@ if __name__ == "__main__":
'training TRF decoder by concatenating '
'matrix test materials')
parser.add_argument('--OutDir', type=PathType(exists=None, type='dir'),
default='./stimulus', help='Output directory')
default='./noise', help='Output directory')
parser.add_argument('--CalcRMS', action='store_true')
args = {k:v for k,v in vars(parser.parse_args()).items() if v is not None}
@@ -266,15 +266,15 @@ if __name__ == "__main__":
wavDir = os.path.join(args['OutDir'], "wav")
dir_must_exist(wavDir)
if args['CalcRMS']:
daFile = gen_da_stim(3333, os.path.join(wavDir, '10min_da.wav'))
daFile = globDir('./noise_source', 'male_speech_resamp.wav')[0]
rmsFiles, peakFiles = gen_rms_peak([daFile], rmsDir, peakDir)
rmsFile = rmsFiles[0]
peakFile = peakFiles[0]
else:
daFile = globDir(wavDir, '*.wav')[0]
daFile = globDir('./noise_source', 'male_speech_resamp.wav')[0]
rmsFile = globDir(rmsDir, '*.npy')[0]
peakFile = globDir(peakDir, '*.npy')[0]
silences = detect_silences([rmsFile], 44100, None)
s_rms = calc_speech_rms([daFile], silences, rmsDir)
b = calc_spectrum([daFile], silences)
#silences = detect_silences([rmsFile], 44100, None)
s_rms = calc_speech_rms(['./stimulus/3000_da.wav'], [[]], rmsDir)
b = calc_spectrum([daFile], [np.array([])])
y = gen_noise(args['OutDir'], b, 44100, s_rms)
da_test_thread.py
+7 -3
View File
@@ -40,8 +40,8 @@ class DaTestThread(BaseThread):
'''
def __init__(self, sessionFilepath=None,
stimFolder='./da_stim/',
noiseFilepath="./matrix_test/behavioural_stim/stimulus/wav/noise/noise_norm.wav",
noiseRMSFilepath="./matrix_test/behavioural_stim/stimulus/rms/noise_rms.npy",
noiseFilepath="./da_stim/noise/wav/noise/noise_norm.wav",
noiseRMSFilepath="./da_stim/noise/rms/noise_rms.npy",
daPeakFilepath="./da_stim/stimulus/peak/10min_da_peak.npy",
red_coef="./calibration/out/reduction_coefficients/da_red_coef.npy",
cal_coef="./calibration/out/calibration_coefficients/da_cal_coef.npy",
@@ -157,7 +157,11 @@ class DaTestThread(BaseThread):
out_wavpath = os.path.join(out_dir, fp)
stim_rms = np.load(rms)
match_ratio = stim_rms/self.noise_rms
block_mix_wavs(wav, self.noise_path, out_wavpath, 1.*self.reduction_coef, snr_fs*match_ratio*self.reduction_coef)
set_trace()
block_mix_wavs(wav, self.noise_path, out_wavpath,
1.*self.reduction_coef,
snr_fs*match_ratio*self.reduction_coef,
mute_left=True)
self.stim_paths.extend([out_wavpath] * self.nTrials)
writer.writerow([wav, snr_fs, rms, si, snr])
# TODO: Output SI/snrs of each file to a CSV file
matrix_test/behavioural_stim/gen_noise.py
+1
View File
@@ -2,6 +2,7 @@
# -*- coding: utf-8 -*-
import sys
sys.path.insert(0, "../helper_modules")
sys.path.insert(0, "../matrix_test/helper_modules/")
import argparse
import os
matrix_test/helper_modules/signalops.py
+9 -1
View File
@@ -101,8 +101,14 @@ def block_process_wav(wavpath, out_wavpath, func, block_size=4096, **args):
out_wav.write_frames(y)
i += block_size
def window_rms(a, window_size):
print("Squaring...")
a2 = a**2
print("Convolving...")
window = np.ones(window_size)/float(window_size)
return np.sqrt(np.convolve(a2, window, 'same'))
def block_mix_wavs(wavpath_a, wavpath_b, out_wavpath, a_gain=1., b_gain=1., block_size=4096):
def block_mix_wavs(wavpath_a, wavpath_b, out_wavpath, a_gain=1., b_gain=1., block_size=4096, mute_left=False):
'''
Mix two wav files, applying gains to each
'''
@@ -124,6 +130,8 @@ def block_mix_wavs(wavpath_a, wavpath_b, out_wavpath, a_gain=1., b_gain=1., bloc
y[:, 0] = x1[:, 0] + x2
y[:, 1] = x1[:, 1] + x2
y[:, 2] = x1[:, 2]
if mute_left:
y[:, 0] = 0.0
else:
y = x1 + x2
out_wav.write_frames(y)