#################################################################
# 4 - Calc psd matrix
psd_noise_matrix = get_power_spectral_density_matrix(stft_noise_arr)
print('Calc psd matrix done!')
print(' psd_noise_matrix.shape = ', psd_noise_matrix.shape)
#################################################################
# 6 - Apply MVDR
type_reg = 1
reg_p = 0.001
w = get_mvdr_vector_svd(d_arr.T,
psd_noise_matrix,
type_reg=type_reg,
r=reg_p)
result_spec = apply_beamforming_vector(w, stft_mix_arr)
#################################################################
# 6 inverse STFT and save
sig_out = istft(result_spec.transpose((1, 0)), overlap=2)
sf.write(r"out/out_mvdr_svd_type_{}_r_{}.wav".format(type_reg, reg_p),
sig_out, sr)
#################################################################
# 7.1 - Do align
align_stft_arr = ds_align(stft_mix_arr, d_arr.T)
#################################################################
# 7.2 save ds output
result_spec = align_stft_arr.sum(axis=1) / (hor_mic_count * vert_mic_count)
sig_out = istft(result_spec.transpose((1, 0)), overlap=2)
print(' (angle_h, angle_v) = ', angle_h, angle_v)
sensor_positions = get_sensor_positions(hor_mic_count,
vert_mic_count,
dHor=dHor,
dVert=dVert)
source_position = get_source_position(angle_h, angle_v)
d_arr = propagation_vector_free_field(sensor_positions,
source_position,
N_fft=n_fft,
F_s=sr)
psd = get_power_spectral_density_matrix(stft_mix)
w = get_mvdr_vector(d_arr.T, psd)
result_mvdr_spec = apply_beamforming_vector(w, stft_mix)
align_stft_arr = ds_align(stft_mix, d_arr.T)
#################################################################
result_spec, H = zelin_filter(stft_arr=align_stft_arr,
alfa=0.7,
alg_type=0)
print('Calc zelin filter output done!')
#################################################################
result_spec = result_mvdr_spec * H
#################################################################
sig_out = istft(result_spec.transpose((1, 0)), overlap=4)
sf.write(r"out/out_mvdr_mix_zelin.wav", sig_out, sr)
psd_reg = psd + REG_COEF * np.identity(psd.shape[-1])
w = get_mvdr_vector(d_arr.T, psd_reg)
if i == 0:
taper, _ = cov_matrix_tapper_bandwidth(stft_mix[:, :, 0:mvdr_step],
hor_mic_count,
vert_mic_count,
dHor=dHor,
dVert=dVert,
angle_v=angle_v,
angle_h=angle_h,
sr=sr)
w = np.zeros((n_bins, n_sensors))
pp = apply_beamforming_vector(w, stft_mix[:, :, i:i + mvdr_step])
res_spec = pp
w = np.zeros((n_bins, n_sensors))
pp = apply_beamforming_vector(
w, stft_mix[:, :, i + mvdr_step:i + 2 * mvdr_step])
if res_spec is None:
res_spec = pp
else:
res_spec = np.hstack((res_spec, pp))
print('Result shape: {}'.format(res_spec.shape))
align_stft_arr = ds_align(stft_mix, d_arr.T)
stft_all_noise = copy.deepcopy(stft_all)
for i in range(0, n_sensors):
stft_all_noise[:, i, :] *= actual_mask
psd_noise_matrix = get_power_spectral_density_matrix(stft_all_noise)
#################################################################
# 5 -Regularisation psd matrix
psd_noise_matrix = psd_noise_matrix + 0.01 * np.identity(
psd_noise_matrix.shape[-1])
#################################################################
# 6 - Apply MVDR
w = get_mvdr_vector(d_arr.T, psd_noise_matrix)
result_spec = apply_beamforming_vector(w, stft_all)
#################################################################
# 6 inverse STFT and save
sig_out = istft(result_spec.transpose((1, 0)), overlap=2)
sf.write(r"out/out_mvdr_mask.wav", sig_out, sr)
#################################################################
# 7.1 - Do align
align_stft_arr = ds_align(stft_all, d_arr.T)
#################################################################
# 7.2 save ds output
result_spec = align_stft_arr.sum(axis=1) / (hor_mic_count * vert_mic_count)
sig_out = istft(result_spec.transpose((1, 0)), overlap=2)
sf.write(r"out/ds.wav", sig_out, sr)
