def no_window(nfft, D, transform, axis=0):
if D == 1:
x_local = x[:, 0]
X_local = X_numpy[:, 0]
else:
if axis == 0:
x_local = x
X_local = X_numpy
else:
x_local = x.T
X_local = X_numpy.T
# make object
dft = pra.transform.DFT(nfft, D, transform=transform, axis=axis)
# forward
X = dft.analysis(x_local)
err_fwd = pra.dB(np.linalg.norm(X_local - X))
# backward
x_r = dft.synthesis()
err_bwd = pra.dB(np.linalg.norm(x_local - x_r))
return err_fwd, err_bwd
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")
def no_overlap(D):
if D == 1:
x_local = x[:, 0]
else:
x_local = x[:, :D]
hop = block_size
# analysis
X = analysis(x_local, L=block_size, hop=hop)
# synthesis
x_r = synthesis(X, L=block_size, hop=hop)
return pra.dB(np.max(np.abs(x_local - x_r)))
def half_overlap(D):
if D == 1:
x_local = x[:, 0]
else:
x_local = x[:, :D]
hop = block_size//2
# analysis
analysis_win = pra.hann(block_size)
X = analysis(x_local, L=block_size, hop=hop, win=analysis_win)
# synthesis
x_r = synthesis(X, L=block_size, hop=hop)
return pra.dB(np.max(np.abs(x_local[:-hop, ] - x_r[hop:, ])))
def hop_one_sample(D):
if D == 1:
x_local = x[:, 0]
else:
x_local = x[:, :D]
hop = 1
# analysis
analysis_win = pra.hann(block_size)
X = analysis(x_local, L=block_size, hop=hop, win=analysis_win)
# synthesis
synthesis_win = pra.transform.compute_synthesis_window(analysis_win, hop)
x_r = synthesis(X, L=block_size, hop=hop, win=synthesis_win)
return pra.dB(
np.max(np.abs(x_local[:-block_size + hop, ] -
x_r[block_size - hop:, ])))
def append_one_sample(D):
hop = block_size // 2
n_samples = x.shape[0]
n_frames = n_samples // hop
x_local = x[:n_frames * hop - 1, :]
if D == 1:
x_local = x_local[:, 0]
else:
x_local = x_local[:, :D]
# analysis
analysis_win = pra.hann(block_size)
X = analysis(x_local, L=block_size, hop=hop, win=analysis_win)
# synthesis
x_r = synthesis(X, L=block_size, hop=hop)
return pra.dB(
np.max(
np.abs(x_local[:-block_size + hop, ] -
x_r[block_size - hop:-1, ])))
fft_size = 512 # fft size for analysis
fft_hop = 128 # hop between analysis frame
fft_zp = 512 # zero padding
analysis_window = pra.hann(fft_size)
print("Sweeping echo measure for ISM is :")
for n in range(M):
if n == 0:
S = stft.analysis(room.rir[n][0], fft_size, fft_hop, win=analysis_window, zp_back=fft_zp)
f, (ax1, ax2) = plt.subplots(2,1)
ax1.imshow(
pra.dB(S.T),
extent=[0, len(room.rir[n][0]), 0, fs / 2],
vmin=-100,
vmax=0,
origin="lower",
cmap="jet"
)
ax1.set_title("RIR for Mic location " + str(n) + " without random ISM")
ax1.set_ylabel("Frequency")
ax1.set_aspect("auto")
#plot RIR
ax2.plot(room.rir[n][0])
ax2.set_xlabel("Num samples")
ax2.set_ylabel("Amplitude")
from pyroomacoustics.directivities import cardioid_func from pyroomacoustics.doa import spher2cart azimuth = np.radians(np.linspace(start=0, stop=360, num=361, endpoint=True)) colatitude = np.radians(np.linspace(start=0, stop=180, num=180, endpoint=True)) lower_gain = -40 """ 2D """ # get cartesian coordinates cart = spher2cart(azimuth=azimuth) direction = spher2cart(azimuth=225, degrees=True) # compute response resp = cardioid_func(x=cart, direction=direction, coef=0.5, magnitude=True) resp_db = dB(np.array(resp)) # plot plt.figure() plt.polar(azimuth, resp_db) plt.ylim([lower_gain, 0]) ax = plt.gca() ax.yaxis.set_ticks(np.arange(start=lower_gain, stop=5, step=10)) plt.tight_layout() """ 3D """ # get cartesian coordinates spher_coord = all_combinations(azimuth, colatitude) cart = spher2cart(azimuth=spher_coord[:, 0], colatitude=spher_coord[:, 1]) direction = spher2cart(azimuth=0, colatitude=45, degrees=True)
)
# 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)
h_hat = pra.experimental.wiener_deconvolve(y, x, length=h_len, noise_variance=sigma_noise**2)
rmse = np.sqrt(np.linalg.norm(h_hat - h)**2 / h_len)
print('rmse=', rmse, '(tol=', tol, ')')
self.assertTrue(rmse < tol)
if __name__ == '__main__':
import matplotlib.pyplot as plt
h = h_hann
y, sigma_noise = generate_signals(SNR, x, h, noise)
h_hat1 = pra.experimental.deconvolve(y, x, length=h_len)
res1 = np.linalg.norm(y - fftconvolve(x, h_hat1))**2 / y.shape[0]
mse1 = np.linalg.norm(h_hat1 - h)**2 / h_len
h_hat2 = pra.experimental.wiener_deconvolve(y, x, length=h_len, noise_variance=sigma_noise**2, let_n_points=15)
res2 = np.linalg.norm(y - fftconvolve(x, h_hat2))**2 / y.shape[0]
mse2 = np.linalg.norm(h_hat2 - h)**2 / h_len
print('MSE naive: rmse=', np.sqrt(mse1), ' res=', pra.dB(res1, power=True))
print('MSE Wiener: rmse=', np.sqrt(mse2), ' res=', pra.dB(res1, power=True))
plt.plot(h)
plt.plot(h_hat1)
plt.plot(h_hat2)
plt.legend(['Original', 'Naive', 'Wiener'])
plt.show()
if __name__ == "__main__":
import matplotlib.pyplot as plt
h = h_hann
y, sigma_noise = generate_signals(SNR, x, h, noise)
h_hat1 = pra.experimental.deconvolve(y, x, length=h_len)
res1 = np.linalg.norm(y - fftconvolve(x, h_hat1))**2 / y.shape[0]
mse1 = np.linalg.norm(h_hat1 - h)**2 / h_len
h_hat2 = pra.experimental.wiener_deconvolve(y,
x,
length=h_len,
noise_variance=sigma_noise**2,
let_n_points=15)
res2 = np.linalg.norm(y - fftconvolve(x, h_hat2))**2 / y.shape[0]
mse2 = np.linalg.norm(h_hat2 - h)**2 / h_len
print("MSE naive: rmse=", np.sqrt(mse1), " res=", pra.dB(res1, power=True))
print("MSE Wiener: rmse=", np.sqrt(mse2), " res=", pra.dB(res1,
power=True))
plt.plot(h)
plt.plot(h_hat1)
plt.plot(h_hat2)
plt.legend(["Original", "Naive", "Wiener"])
plt.show()
mics.rakePerceptualFilters(good_sources[:max_order_design+1],
bad_sources[:max_order_design+1],
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('output_samples/output_RakePerceptual.wav', Fs, out_RakePerceptual)
'''
Plot all the spectrogram
'''
dSNR = pra.dB(room1.dSNR(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 = 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 = pra.stft(input_clean, fft_size, fft_hop,
win=analysis_window,
import numpy as np
import matplotlib.pyplot as plt
import pyroomacoustics as pra
beamformer_names = ['Rake MaxSINR', 'Rake Perceptual', 'Rake MVDR']
SINR = np.load('data/SINR_data.npy')
# uncomment the following line to use the simulated data used in the paper
#SINR = np.load('data/SINR_data_Lg30ms_d20ms_SNR10_N10000_20141015.npy')
max_K, n_bf, n_monte_carlo = SINR.shape
SINR_med = np.array(np.percentile(SINR, [50, 5, 95], axis=-1))
SINR_gain_5sources = pra.dB(SINR_med[0,5,:]) - pra.dB(SINR_med[0,0,:])
print 'SNR gain of using 5 sources instead of one:'
print 'Rake MaxSINR: %.2f dB' % SINR_gain_5sources[0]
print 'Rake Perceptual: %.2f dB' % SINR_gain_5sources[1]
print 'Rake MVDR: %.2f dB' % SINR_gain_5sources[2]
#---------------------------------------------------------------------
# Export the SNR figure
#---------------------------------------------------------------------
plt.figure(figsize=(4, 3))
newmap = plt.get_cmap('gist_heat')
ax1 = plt.gca()
ax1.set_color_cycle([newmap( k ) for k in np.linspace(0.25,0.9,len(beamformer_names))])
from itertools import cycle
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('output_samples/output_RakePerceptual.wav', Fs,
out_RakePerceptual)
'''
Plot all the spectrogram
'''
dSNR = pra.dB(room1.dSNR(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 = 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 = pra.stft(input_clean,
fft_size,
fft_hop,
import numpy as np
import matplotlib.pyplot as plt
import pyroomacoustics as pra
beamformer_names = ['Rake MaxSINR', 'Rake Perceptual', 'Rake MVDR']
SINR = np.load('data/SINR_data.npy')
# uncomment the following line to use the simulated data used in the paper
SINR = np.load('data/SINR_data_Lg30ms_d20ms_SNR10_N10000_20141015.npy')
max_K, n_bf, n_monte_carlo = SINR.shape
SINR_med = np.array(np.percentile(SINR, [50, 25, 75], axis=-1))
SINR_gain_5sources = pra.dB(SINR_med[0, 5, :], power=True) - pra.dB(
SINR_med[0, 0, :], power=True)
print 'SNR gain of using 5 sources instead of one:'
print 'Rake MaxSINR: %.2f dB' % SINR_gain_5sources[0]
print 'Rake Perceptual: %.2f dB' % SINR_gain_5sources[1]
print 'Rake MVDR: %.2f dB' % SINR_gain_5sources[2]
#---------------------------------------------------------------------
# Export the SNR figure
#---------------------------------------------------------------------
plt.figure(figsize=(4, 3))
newmap = plt.get_cmap('gist_heat')
ax1 = plt.gca()
ax1.set_color_cycle(
[newmap(k) for k in np.linspace(0.25, 0.9, len(beamformer_names))])
import numpy as np
import matplotlib.pyplot as plt
import pyroomacoustics as pra
beamformer_names = ['Rake MaxSINR', 'Rake Perceptual', 'Rake MVDR']
SINR = np.load('data/SINR_data.npy')
# uncomment the following line to use the simulated data used in the paper
SINR = np.load('data/SINR_data_Lg30ms_d20ms_SNR10_N10000_20141015.npy')
max_K, n_bf, n_monte_carlo = SINR.shape
SINR_med = np.array(np.percentile(SINR, [50, 25, 75], axis=-1))
SINR_gain_5sources = pra.dB(SINR_med[0,5,:], power=True) - pra.dB(SINR_med[0,0,:], power=True)
print 'SNR gain of using 5 sources instead of one:'
print 'Rake MaxSINR: %.2f dB' % SINR_gain_5sources[0]
print 'Rake Perceptual: %.2f dB' % SINR_gain_5sources[1]
print 'Rake MVDR: %.2f dB' % SINR_gain_5sources[2]
#---------------------------------------------------------------------
# Export the SNR figure
#---------------------------------------------------------------------
plt.figure(figsize=(4, 3))
newmap = plt.get_cmap('gist_heat')
ax1 = plt.gca()
ax1.set_color_cycle([newmap( k ) for k in np.linspace(0.25,0.9,len(beamformer_names))])
from itertools import cycle
