-
Notifications
You must be signed in to change notification settings - Fork 419
/
beamforming_time_domain.py
272 lines (220 loc) · 6.97 KB
/
beamforming_time_domain.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
"""
This is a longer example that applies time domain beamforming towards a source
of interest in the presence of a strong interfering source.
"""
from __future__ import division, print_function
import os
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import wavfile
import pyroomacoustics as pra
from pyroomacoustics.transform import stft
# Spectrogram figure properties
figsize = (15, 7) # figure size
fft_size = 512 # fft size for analysis
fft_hop = 8 # hop between analysis frame
fft_zp = 512 # zero padding
analysis_window = pra.hann(fft_size)
t_cut = 0.83 # length in [s] to remove at end of signal (no sound)
# Some simulation parameters
Fs = 8000
absorption = 0.1
max_order_sim = 2
sigma2_n = 5e-7
# Microphone array design parameters
mic1 = np.array([2, 1.5]) # position
M = 8 # number of microphones
d = 0.08 # distance between microphones
phi = 0.0 # angle from horizontal
max_order_design = 1 # maximum image generation used in design
shape = "Linear" # array shape
Lg_t = 0.100 # Filter size in seconds
Lg = np.ceil(Lg_t * Fs) # Filter size in samples
delay = 0.050 # Beamformer delay in seconds
# Define the FFT length
N = 1024
# Create a microphone array
if shape is "Circular":
R = pra.circular_2D_array(mic1, M, phi, d * M / (2 * np.pi))
else:
R = pra.linear_2D_array(mic1, M, phi, d)
# path to samples
path = os.path.dirname(__file__)
# The first signal (of interest) is singing
rate1, signal1 = wavfile.read(path + "/input_samples/singing_" + str(Fs) + ".wav")
signal1 = np.array(signal1, dtype=float)
signal1 = pra.normalize(signal1)
signal1 = pra.highpass(signal1, Fs)
delay1 = 0.0
# The second signal (interferer) is some german speech
rate2, signal2 = wavfile.read(path + "/input_samples/german_speech_" + str(Fs) + ".wav")
signal2 = np.array(signal2, dtype=float)
signal2 = pra.normalize(signal2)
signal2 = pra.highpass(signal2, Fs)
delay2 = 1.0
# Create the room
room_dim = [4, 6]
room1 = pra.ShoeBox(
room_dim,
absorption=absorption,
fs=Fs,
max_order=max_order_sim,
sigma2_awgn=sigma2_n,
)
# Add sources to room
good_source = np.array([1, 4.5]) # good source
normal_interferer = np.array([2.8, 4.3]) # interferer
room1.add_source(good_source, signal=signal1, delay=delay1)
room1.add_source(normal_interferer, signal=signal2, delay=delay2)
"""
MVDR direct path only simulation
"""
# compute beamforming filters
mics = pra.Beamformer(R, Fs, N=N, Lg=Lg)
room1.add_microphone_array(mics)
room1.compute_rir()
room1.simulate()
mics.rake_mvdr_filters(
room1.sources[0][0:1],
room1.sources[1][0:1],
sigma2_n * np.eye(mics.Lg * mics.M),
delay=delay,
)
# process the signal
output = mics.process()
# save to output file
input_mic = pra.normalize(pra.highpass(mics.signals[mics.M // 2], Fs))
wavfile.write(path + "/output_samples/input.wav", Fs, input_mic)
out_DirectMVDR = pra.normalize(pra.highpass(output, Fs))
wavfile.write(path + "/output_samples/output_DirectMVDR.wav", Fs, out_DirectMVDR)
"""
Rake MVDR simulation
"""
# Add the microphone array and compute RIR
mics = pra.Beamformer(R, Fs, N, Lg=Lg)
room1.mic_array = mics
room1.compute_rir()
room1.simulate()
# Design the beamforming filters using some of the images sources
good_sources = room1.sources[0][: max_order_design + 1]
bad_sources = room1.sources[1][: max_order_design + 1]
mics.rake_mvdr_filters(
good_sources, bad_sources, sigma2_n * np.eye(mics.Lg * mics.M), delay=delay
)
# process the signal
output = mics.process()
# save to output file
out_RakeMVDR = pra.normalize(pra.highpass(output, Fs))
wavfile.write(path + "/output_samples/output_RakeMVDR.wav", Fs, out_RakeMVDR)
"""
Perceptual direct path only simulation
"""
# compute beamforming filters
mics = pra.Beamformer(R, Fs, N, Lg=Lg)
room1.mic_array = mics
room1.compute_rir()
room1.simulate()
mics.rake_perceptual_filters(
room1.sources[0][0:1],
room1.sources[1][0:1],
sigma2_n * np.eye(mics.Lg * mics.M),
delay=delay,
)
# process the signal
output = mics.process()
# save to output file
out_DirectPerceptual = pra.normalize(pra.highpass(output, Fs))
wavfile.write(
path + "/output_samples/output_DirectPerceptual.wav", Fs, out_DirectPerceptual
)
"""
Rake Perceptual simulation
"""
# compute beamforming filters
mics = pra.Beamformer(R, Fs, N, Lg=Lg)
room1.mic_array = mics
room1.compute_rir()
room1.simulate()
mics.rake_perceptual_filters(
good_sources, bad_sources, sigma2_n * np.eye(mics.Lg * mics.M), delay=delay
)
# process the signal
output = mics.process()
# save to output file
out_RakePerceptual = pra.normalize(pra.highpass(output, Fs))
wavfile.write(
path + "/output_samples/output_RakePerceptual.wav", Fs, out_RakePerceptual
)
"""
Plot all the spectrogram
"""
dSNR = pra.dB(room1.direct_snr(mics.center[:, 0], source=0), power=True)
print("The direct SNR for good source is " + str(dSNR))
# remove a bit of signal at the end
n_lim = int(np.ceil(len(input_mic) - t_cut * Fs))
input_clean = signal1[:n_lim]
input_mic = input_mic[:n_lim]
out_DirectMVDR = out_DirectMVDR[:n_lim]
out_RakeMVDR = out_RakeMVDR[:n_lim]
out_DirectPerceptual = out_DirectPerceptual[:n_lim]
out_RakePerceptual = out_RakePerceptual[:n_lim]
# compute time-frequency planes
F0 = stft.analysis(input_clean, fft_size, fft_hop, win=analysis_window, zp_back=fft_zp)
F1 = stft.analysis(input_mic, fft_size, fft_hop, win=analysis_window, zp_back=fft_zp)
F2 = stft.analysis(
out_DirectMVDR, fft_size, fft_hop, win=analysis_window, zp_back=fft_zp
)
F3 = stft.analysis(out_RakeMVDR, fft_size, fft_hop, win=analysis_window, zp_back=fft_zp)
F4 = stft.analysis(
out_DirectPerceptual, fft_size, fft_hop, win=analysis_window, zp_back=fft_zp
)
F5 = stft.analysis(
out_RakePerceptual, fft_size, fft_hop, win=analysis_window, zp_back=fft_zp
)
# (not so) fancy way to set the scale to avoid having the spectrum
# dominated by a few outliers
p_min = 7
p_max = 100
all_vals = np.concatenate(
(
pra.dB(F1 + pra.eps),
pra.dB(F2 + pra.eps),
pra.dB(F3 + pra.eps),
pra.dB(F0 + pra.eps),
pra.dB(F4 + pra.eps),
pra.dB(F5 + pra.eps),
)
).flatten()
vmin, vmax = np.percentile(all_vals, [p_min, p_max])
cmap = "afmhot"
interpolation = "none"
fig, ax = plt.subplots(figsize=figsize, nrows=2, ncols=3)
def plot_spectrogram(F, title):
plt.imshow(
pra.dB(F.T),
extent=[0, 1, 0, Fs / 2],
vmin=vmin,
vmax=vmax,
origin="lower",
cmap=plt.get_cmap(cmap),
interpolation=interpolation,
)
ax.set_title(title)
ax.set_ylabel("")
ax.set_xlabel("")
ax.set_aspect("auto")
ax.axis("off")
ax = plt.subplot(2, 3, 1)
plot_spectrogram(F0, "Desired Signal")
ax = plt.subplot(2, 3, 4)
plot_spectrogram(F1, "Microphone Input")
ax = plt.subplot(2, 3, 2)
plot_spectrogram(F2, "Direct MVDR")
ax = plt.subplot(2, 3, 5)
plot_spectrogram(F3, "Rake MVDR")
ax = plt.subplot(2, 3, 3)
plot_spectrogram(F4, "Direct Perceptual")
ax = plt.subplot(2, 3, 6)
plot_spectrogram(F5, "Rake Perceptual")
fig.savefig(path + "/figures/spectrograms.png", dpi=150)
plt.show()