def plot_doa(x_t, title):
x = psa.Signal(x_t, fs, 'acn', 'sn3d')
X = psa.Stft.fromSignal(x,
window_size=window_size,
window_overlap=window_overlap,
nfft=nfft)
X_doa = psa.compute_DOA(X)
psa.plot_doa(X_doa, title)
plt.show()
return X_doa
window_overlap = window_size//2
_, _, S_dir = scipy.signal.stft(s_dir, fs, nperseg=window_size, noverlap=window_overlap )
s_tot_ambi = psa.Signal(bformat, fs, 'acn', 'n3d')
S_tot_ambi = psa.Stft.fromSignal(s_tot_ambi,
window_size=window_size,
window_overlap=window_overlap
)
doa = psa.compute_DOA(S_tot_ambi)
directivity = S_tot_ambi.compute_ita_re(r=r)
psa.plot_signal(s_tot_ambi)
psa.plot_magnitude_spectrogram(S_tot_ambi)
psa.plot_doa(doa)
psa.plot_directivity(directivity)
# psa.plot_directivity(directivity.sqrt())
est_S_dir_ambi = S_tot_ambi.apply_mask(directivity.sqrt())
est_S_dir = est_S_dir_ambi.data[0]
# psa.plot_magnitude_spectrogram(est_S_dir_ambi)
doa_est_S_dir_ambi = psa.compute_DOA(est_S_dir_ambi)
directivity_est_S_dir_ambi = est_S_dir_ambi.compute_ita_re(r=r)
psa.plot_doa(doa_est_S_dir_ambi,title='after method')
# psa.plot_directivity(directivity_est_S_dir_ambi)
plt.show()
est_s_dir_ambi = psa.Signal.fromStft(est_S_dir_ambi,
window_size=window_size,
window_overlap=window_overlap
def estimate_doa(data, sr, params):
"""
Given an input audio, get the most significant tf bins per frame
:param data: np.array (num_frames, num_channels)
:param sr: sampling rate
:param params: params dict
:return: an array in the form :
[frame, [class_id, azi, ele],[class_id, azi, ele]... ]
without repeated frame instances, quantized at hop_size,
containing all valid tf bins doas.
"""
### Preprocess data
X = preprocess(data, sr, params)
N = X.get_num_time_bins()
K = X.get_num_frequency_bins()
r = params['r']
### Diffuseness mask
doa = psa.compute_DOA(X)
directivity = X.compute_ita_re(r=r)
directivity_mask = directivity.compute_mask(th=params['directivity_th'])
### Energy density mask
e = psa.compute_energy_density(X)
block_size = params['energy_density_local_th_size']
tl = e.compute_threshold_local(block_size=block_size)
e_mask = e.compute_mask(tl)
### DOA Variance mask (computed on azimuth variance)
vicinity_radius = params['doa_std_vicinity_radius']
if np.size(vicinity_radius) == 1:
# Square!
r_k = vicinity_radius
r_n = vicinity_radius
elif np.size(vicinity_radius) == 2:
# Rectangle! [k, n]
r_k = vicinity_radius[0]
r_n = vicinity_radius[1]
else:
Warning.warn()
# TODO: optimize the for loop
std = np.zeros((K, N))
doa0_k_array = []
for r in range(-r_n,r_n+1):
doa0_k_array.append(np.roll(doa.data[0,:,:],r))
doa0_k = np.stack(doa0_k_array, axis=0)
for k in range(r_k, K - r_k):
std[k, :] = scipy.stats.circstd(doa0_k[:, k - r_k:k + r_k + 1, :], high=np.pi, low=-np.pi, axis=(0, 1))
# not optimized version...
# for k in range(r_k, K-r_k):
# for n in range(r_n, N-r_n):
# # azi
# std[k, n] = scipy.stats.circstd(doa.data[0, k-r_k:k+r_k+1, n-r_n:n+r_n+1], high=np.pi, low=-np.pi)
# # ele
# # std[k, n] = np.std(doa.data[1, k-r_k:k+r_k+1, n-r_n:n+r_n+1])
# Edges: largest value
std_max = np.max(std)
std[0:r_k, :] = std_max
std[K-r_k:K, :] = std_max
std[:, 0:r_n] = std_max
std[:, N - r_n:N] = std_max
# Scale values to min/max
std_scaled = std / std_max
# Invert values
std_scaled_inv = 1 - std_scaled
# Compute mask
doa_std = psa.Stft(doa.t, doa.f, std_scaled_inv, doa.sample_rate)
doa_std_mask = doa_std.compute_mask(th=params['doa_std_th'])
mask_all = doa_std_mask.apply_mask(directivity_mask).apply_mask(e_mask)
doa_th = doa.apply_mask(mask_all)
## Median average
median_averaged_doa = np.empty(doa.data.shape)
median_averaged_doa.fill(np.nan)
vicinity_size = (2*r_k-1) + (2*r_n-1)
doa_median_average_nan_th = params['doa_median_average_nan_th']
vicinity_radius = params['median_filter_vicinity_radius']
if np.size(vicinity_radius) == 1:
# Square!
r_k = vicinity_radius
r_n = vicinity_radius
elif np.size(vicinity_radius) == 2:
# Rectangle! [k, n]
r_k = vicinity_radius[0]
r_n = vicinity_radius[1]
else:
Warning.warn()
# TODO: optimize the for loop
for k in range(r_k, K - r_k):
for n in range(r_n, N - r_n):
azis = discard_nans(doa_th.data[0, k - r_k:k + r_k + 1, n - r_n:n + r_n + 1].flatten())
if azis.size > vicinity_size * doa_median_average_nan_th:
median_averaged_doa[0, k, n] = circmedian(azis, 'rad')
eles = discard_nans(doa_th.data[1, k - r_k:k + r_k + 1, n - r_n:n + r_n + 1].flatten())
if eles.size > vicinity_size * doa_median_average_nan_th:
median_averaged_doa[1, k, n] = np.median(eles)
doa_th_median = psa.Stft(doa.t, doa.f, median_averaged_doa, doa.sample_rate)
## Plot stuff
if params['plot']:
psa.plot_doa(doa, title='doa')
psa.plot_doa(doa.apply_mask(e_mask), title='e mask')
psa.plot_doa(doa.apply_mask(directivity_mask), title='directivity mask')
psa.plot_doa(doa.apply_mask(doa_std_mask), title='doa std mask')
psa.plot_doa(doa_th, title='doa mask all')
psa.plot_doa(doa_th_median, title='doa circmedian')
plt.show()
## Fold values into a vector
# Get a list of bins with the position estimation according to the selected doa_method
# TODO: OPTIMIZE
active_windows = []
position = []
for n in range(N):
azi = discard_nans(doa_th_median.data[0, :, n])
ele = discard_nans(doa_th_median.data[1, :, n])
if np.size(azi) < params['num_min_valid_bins']:
# Empty! not enough suitable doa values in this analysis window
pass
else:
active_windows.append(n)
position.append([rad2deg(azi), rad2deg(ele)])
# result = [bin, class_id, azi, ele] with likely repeated bin instances
result = []
label = params['default_class_id']
for window_idx, window in enumerate(active_windows):
num_bins = np.shape(position[window_idx])[1]
for b in range(num_bins):
azi = position[window_idx][0][b]
ele = position[window_idx][1][b]
result.append([window, label, azi, ele])
# Perform the window transformation by averaging within frame
## TODO: assert our bins are smaller than required ones
current_window_hop = (params['window_size'] - params['window_overlap']) / float(sr)
window_factor = params['required_window_hop'] / current_window_hop
# Since frames are ordered (at least they should), we can optimise that a little bit
last_frame = -1
# result_quantized = [frame, [class_id, azi, ele],[class_id, azi, ele]... ] without repeated bin instances
result_quantized = []
for row in result:
frame = row[0]
new_frame = int(np.floor(frame / window_factor))
if new_frame == last_frame:
result_quantized[-1].append([row[1], row[2], row[3]])
else:
result_quantized.append([new_frame, [row[1], row[2], row[3]]])
last_frame = new_frame
return result_quantized
psa.plot_diffuseness(diffuseness_stft, title='diffuseness')
## Apply diffuseness mask to separate background and foreground
background_stft = stft.apply_mask(diffuseness_stft)
foreground_stft = stft.apply_mask(directivity_stft)
psa.plot_magnitude_spectrogram(background_stft,
title='background magnitude spectrogram')
psa.plot_magnitude_spectrogram(foreground_stft,
title='foreground magnitude spectrogram')
## Find DOA on the foreground
doa = psa.compute_DOA(stft)
psa.plot_doa(doa, title='doa')
doa_foreground = psa.compute_DOA(foreground_stft)
psa.plot_doa(doa_foreground, title='doa_foreground')
doa_background = psa.compute_DOA(background_stft)
psa.plot_doa(doa_background, title='doa_background')
## Just get the most directive values
directivity_mask = directivity_stft.compute_mask(th=0.95)
psa.plot_mask(directivity_mask, title='directivity mask')
masked_doa_foreground = doa_foreground.apply_mask(directivity_mask)
psa.plot_doa(masked_doa_foreground, title='masked_doa_foreground')
def compute_peak_statistics(ir,
sample_rate,
ambisonics_ordering,
ambisonics_normalization,
plot=False,
plot_title = ''):
## Signal
signal = psa.Signal(ir, int(sample_rate), ambisonics_ordering, ambisonics_normalization)
if plot:
psa.plot_signal(signal,title=plot_title+'IR')
stft = psa.Stft.fromSignal(signal,
window_size=analysis_window_size,
window_overlap=window_overlap,
nfft=fft_size,
)
stft = stft.limit_bands(fmin=fmin, fmax=fmax)
if plot:
psa.plot_magnitude_spectrogram(stft,title=plot_title+'IR Magnitude Spectrogram, w='+str(analysis_window_size))
### Energy Density
energy_density_t = psa.compute_energy_density(signal)
if plot:
psa.plot_signal(energy_density_t,'Energy Density', y_scale='log')
# # Smoothed signal
# L = gaussian_window_length
# smooth_window = scipy.signal.general_gaussian(L, p=gaussian_window_shape, sig=gaussian_window_std)
# smoothed_energy_density_t = scipy.signal.fftconvolve(smooth_window, energy_density_t.data[0, :])
# smoothed_energy_density_t = (np.average(energy_density_t.data[0, :]) / np.average(smoothed_energy_density_t)) * smoothed_energy_density_t
# smoothed_energy_density_t = np.roll(smoothed_energy_density_t, -((L - 1) / 2))
# smoothed_energy_density_t = smoothed_energy_density_t[:-(L - 1)] # same length
#
# ### Peak peaking
#
# # WAVELET
# cwt_widths = np.arange(0.5*L,1.5*L) # Find peaks of shape among 2 gaussian window lengths
# smoothed_peaks = scipy.signal.find_peaks_cwt(smoothed_energy_density_t, widths=cwt_widths)
# # Fine sample correction of peaks: find local maxima over a gaussian window length
# corrected_peaks = copy.deepcopy(smoothed_peaks)
# for peak_idx,peak in enumerate(smoothed_peaks):
# local_energy = smoothed_energy_density_t[peak - (L / 2):peak + (L / 2)]
# corrected_peaks[peak_idx] = np.argmax(local_energy) + peak - (L / 2)
#
# if plot:
# plt.figure()
# plt.suptitle('Smoothed Energy Density & peaks')
# ax = plt.subplot(111)
# ax.semilogy(energy_density_t.data[0,:])
# ax.semilogy(smoothed_energy_density_t)
#
# # plot peak estimates
# for peak in corrected_peaks:
# plt.axvline(x=peak, color='g')
#
# # plot time frames
# for x in np.arange(0,processing_window_samples,analysis_window_size):
# plt.axvline(x=x, color='r', alpha=0.3)
#
# plt.grid()
#
# peak_time_bins = []
# for peak in corrected_peaks:
# peak_time_bins.append(find_maximal_time_bin(peak, stft, overlap_factor))
## Raw Estimates
doa = psa.compute_DOA(stft)
if plot:
psa.plot_doa(doa,title=plot_title+'DoA estimates, w='+str(analysis_window_size))
# diffuseness = psa.compute_diffuseness(stft)
# if plot:
# psa.plot_diffuseness(diffuseness,title=plot_title+'Diffuseness, w='+str(analysis_window_size))
neighborhood_size = 3
## DOA variance
doa_var = copy.deepcopy(doa)
for n in range(doa.get_num_time_bins()):
for k in range(doa.get_num_frequency_bins()):
local_var_azi = 0
local_var_ele = 0
local_azi = []
local_ele = []
r = int(np.floor(neighborhood_size/2)) # neighborhood radius
for x in np.arange(n - r, n + r + 1):
for y in np.arange(k - r, k + r + 1):
if x < 0:
continue
elif x >= doa.get_num_time_bins():
continue
if y < 0:
continue
elif y >= doa.get_num_frequency_bins():
continue
local_azi.append(doa.data[0,y,x])
local_ele.append(doa.data[1,y,x])
# local_var_azi += np.std(doa.data[0,y,x])
# local_var_ele += np.std(doa.data[1,y,x])
local_var_azi = scipy.stats.circvar(np.array(local_azi))
local_var_ele = np.var(np.array(local_ele))
doa_var.data[0,k,n] = local_var_azi
doa_var.data[1,k,n] = local_var_ele
## DOA VAR salience
neighborhood_size = round_up_to_odd(doa_var.get_num_frequency_bins())
doa_var_salience = threshold_local(doa_var.data[0,:],block_size=neighborhood_size)
doa_var_max_salience_mask = copy.deepcopy(doa_var)
doa_var_min_salience_mask = copy.deepcopy(doa_var)
for k in range(doa_var.get_num_frequency_bins()):
for n in range(doa_var.get_num_time_bins()):
if doa_var.data[0, k, n] > doa_var_salience[k, n]:
doa_var_max_salience_mask.data[:, k, n] = 1.
else:
doa_var_max_salience_mask.data[:, k, n] = np.nan
if doa_var.data[0, k, n] < doa_var_salience[k, n]:
doa_var_min_salience_mask.data[:, k, n] = 1.
else:
doa_var_min_salience_mask.data[:, k, n] = np.nan
# MINIMUM VARIANCE DOA
masked_doa = doa.apply_mask(doa_var_min_salience_mask)
if plot:
psa.plot_doa(masked_doa,
title=plot_title + 'DOA - Minimum variance Salience Masked, w=' + str(analysis_window_size) + ' N: ' + str(
neighborhood_size))
masked_doa = doa.apply_mask(doa_var_max_salience_mask)
# if plot:
# psa.plot_doa(masked_doa,
# title=plot_title + 'DOA - Maximim variance Salience Masked, w=' + str(analysis_window_size) + ' N: ' + str(
# neighborhood_size))
# if plot:
# plt.figure()
# plt.suptitle('DOA VAR')
# plt.subplot(211)
# plt.pcolormesh(doa_var.data[0,:,:])
# plt.subplot(212)
# plt.pcolormesh(doa_var.data[1,:,:])
#
# psa.plot_mask(doa_var_max_salience_mask,title='MAX SALIENCE')
# psa.plot_mask(doa_var_min_salience_mask,title='MIN SALIENCE')
## Energy density
energy_density_tf = psa.compute_energy_density(stft)
# if plot:
# psa.plot_magnitude_spectrogram(energy_density_tf,title='Energy Density Spectrogram, w='+str(analysis_window_size))
# Energy density salience
neighborhood_size = round_up_to_odd(energy_density_tf.get_num_frequency_bins())
energy_density_salience = threshold_local(energy_density_tf.data[0,:],block_size=neighborhood_size)
energy_density_salience_mask = copy.deepcopy(energy_density_tf)
for k in range(energy_density_tf.get_num_frequency_bins()):
for n in range(energy_density_tf.get_num_time_bins()):
if energy_density_tf.data[0, k, n] > energy_density_salience[k, n]:
energy_density_salience_mask.data[:, k, n] = 1.
else:
energy_density_salience_mask.data[:, k, n] = np.nan
# if plot:
# fig = plt.figure()
# fig.suptitle('energy salience, w=' + str(analysis_window_size))
#
# x = np.arange(np.shape(energy_density_salience)[0])
# y = np.arange(np.shape(energy_density_salience)[1])
# plt.pcolormesh(y, x, energy_density_salience, norm=LogNorm())
# plt.ylabel('Frequency [Hz]')
# plt.xlabel('Time [sec]')
# plt.colorbar()
# if plot:
# psa.plot_mask(energy_density_salience_mask, title='Energy Salience Mask'+str(analysis_window_size))
masked_energy = energy_density_tf.apply_mask(energy_density_salience_mask)
# if plot:
# psa.plot_magnitude_spectrogram(masked_energy, title=plot_title+'Energy - Energy Salience Masked, w='+str(analysis_window_size)+' N: '+str(neighborhood_size))
masked_doa = doa.apply_mask(energy_density_salience_mask)
if plot:
psa.plot_doa(masked_doa, title=plot_title+'DOA - Energy Salience Masked, w='+str(analysis_window_size)+' N: '+str(neighborhood_size))
masked_doa = doa.apply_mask(energy_density_salience_mask).apply_mask(doa_var_min_salience_mask)
if plot:
psa.plot_doa(masked_doa, title=plot_title+'DOA - VAR MIN, Energy Salience Masked, w='+str(analysis_window_size)+' N: '+str(neighborhood_size))
# masked_diffuseness = diffuseness.apply_mask(energy_density_salience_mask)
# if plot:
# psa.plot_diffuseness(masked_diffuseness, title=plot_title+'Diffuseness - Energy Salience Masked, w='+str(analysis_window_size)+' N: '+str(neighborhood_size))
# # Diffuseness density salience
#
# neighborhood_size = round_up_to_odd(diffuseness.get_num_frequency_bins())
# diffuseness_salience = threshold_local(diffuseness.data[0,:],block_size=neighborhood_size)
# diffuseness_salience_mask = copy.deepcopy(diffuseness)
# for k in range(diffuseness.get_num_frequency_bins()):
# for n in range(diffuseness.get_num_time_bins()):
# if diffuseness.data[0, k, n] < diffuseness_salience[k, n]:
# diffuseness_salience_mask.data[:, k, n] = 1.
# else:
# diffuseness_salience_mask.data[:, k, n] = np.nan
#
# masked_energy = energy_density_tf.apply_mask(diffuseness_salience_mask)
# if plot:
# psa.plot_magnitude_spectrogram(masked_energy, title=plot_title + 'Energy - Diffuseness Salience Masked, w=' + str(
# analysis_window_size) + ' N: ' + str(neighborhood_size))
#
# masked_doa = doa.apply_mask(diffuseness_salience_mask)
# if plot:
# psa.plot_doa(masked_doa, title=plot_title + 'DOA - Diffuseness Salience Masked, w=' + str(
# analysis_window_size) + ' N: ' + str(neighborhood_size))
#
# masked_diffuseness = diffuseness.apply_mask(diffuseness_salience_mask)
# if plot:
# psa.plot_diffuseness(masked_diffuseness, title=plot_title + 'Diffuseness - Diffuseness Salience Masked, w=' + str(
# analysis_window_size) + ' N: ' + str(neighborhood_size))
# #
# if plot:
# psa.plot_mask(diffuseness_salience_mask, title='Diffuseness Salience Mask'+str(neighborhood_size))
# #
# masked_dif = diffuseness.apply_mask(diffuseness_salience_mask)
# if plot:
# psa.plot_diffuseness(masked_dif, title='Diffuseness - Salience Masked'+str(neighborhood_size))
#
# energy_diffuseness_mask = energy_density_salience_mask.apply_mask(diffuseness_salience_mask)
#
# masked_doa = masked_doa.apply_mask(diffuseness_salience_mask)
# if plot:
# psa.plot_doa(masked_doa, title='DOA - Salience Masked - Diffuseness Masked, w='+str(analysis_window_size))
#
# fig = plt.figure()
# fig.suptitle("diffuseness salience, block:"+str(neighborhood_size), fontsize=16)
# x = np.arange(np.shape(diffuseness_salience)[0])
# y = np.arange(np.shape(diffuseness_salience)[1])
# plt.pcolormesh(y,x, diffuseness_salience, cmap='plasma_r',norm=LogNorm())
# plt.ylabel('Frequency [Hz]')
# plt.xlabel('Time [sec]')
# plt.colorbar()
# diffuseness_energy_mask = diffuseness_mask.apply_mask(energy_density_mask)
# if plot:
# psa.plot_mask(diffuseness_energy_mask, title='Diffuseness + Energy Density Mask')
#
# masked_diffuseness = diffuseness.apply_mask(diffuseness_mask)
# if plot:
# psa.plot_diffuseness(masked_diffuseness, title='Diffuseness, diffuseness mask')
#
# masked_diffuseness = masked_diffuseness.apply_mask(energy_density_mask)
# if plot:
# psa.plot_diffuseness(masked_diffuseness, title='Diffuseness, energy density maskm diffuseness mask')
#
# masked_doa = masked_doa.apply_mask(diffuseness_mask)
# if plot:
# psa.plot_doa(masked_doa,title='DoA estimates, energy density mask, diffuseness mask')
### Find horizontal-contiguous bins on doa estimates
time_bins_with_energy = []
for n in range(energy_density_salience_mask.get_num_time_bins()):
if not np.all(np.isnan(energy_density_salience_mask.data[0,:,n])):
time_bins_with_energy.append(n)
time_region_starts = []
time_region_ends = []
for idx, b in enumerate(time_bins_with_energy):
if time_bins_with_energy[idx] - time_bins_with_energy[idx - 1] != 1:
time_region_starts.append(time_bins_with_energy[idx])
time_region_ends.append(time_bins_with_energy[idx - 1]+1)
time_region_starts.sort()
time_region_ends.sort()
assert len(time_region_starts) == len(time_region_ends)
# Compute local doa estimates on contiguous time regions
peak_stats = []
for idx in range(len(time_region_starts)):
n_range = range(time_region_starts[idx],time_region_ends[idx])
local_azi = []
local_ele = []
index_of_bins_estimated = []
for n in n_range:
# Filter nans
for k in np.arange(energy_density_salience_mask.get_num_frequency_bins()):
if not np.isnan(energy_density_salience_mask.data[0, k, n]):
local_azi.append(masked_doa.data[0, k, n])
local_ele.append(masked_doa.data[1, k, n])
index_of_bins_estimated.append(n)
local_azi = np.asarray(local_azi)
local_ele = np.asarray(local_ele)
# local_dif = np.asarray(local_dif)
local_azi_mean = scipy.stats.circmean(local_azi, high=np.pi, low=-np.pi)
local_azi_std = scipy.stats.circstd(local_azi, high=np.pi, low=-np.pi)
local_ele_mean = np.mean(local_ele)
local_ele_std = np.std(local_ele)
if plot:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.suptitle(plot_title+'FREQ BIN '+str(idx)+' - bins '+str(n_range))
# cmap = plt.cm.get_cmap("copper")
plt.grid()
plt.xlim(-np.pi,np.pi)
plt.ylim(-np.pi/2,np.pi/2)
plt.scatter(local_azi, local_ele, marker='o',)
plt.scatter(local_azi_mean, local_ele_mean, c='red', s=20, marker='+')
ax.add_patch(Ellipse(xy=(local_azi_mean,local_ele_mean), width=local_azi_std,height=local_ele_std, alpha=0.5))
ax.add_patch(Ellipse(xy=(local_azi_mean,local_ele_mean), width=3*local_azi_std,height=3*local_ele_std, alpha=0.1))
mean_location = np.mean(index_of_bins_estimated)
time_resolution_ms = float(processing_window_ms) / masked_doa.get_num_time_bins()
estimated_location_ms = mean_location * time_resolution_ms
peak_stats.append(
[estimated_location_ms, [local_azi_mean, local_azi_std], [local_ele_mean, local_ele_std]])
### Return peak stats
return peak_stats
X = psa.Stft.fromSignal(x,
window_size=window_size,
window_overlap=window_overlap,
nfft=nfft)
X_doa = psa.compute_DOA(X)
psa.plot_doa(X_doa, title)
plt.show()
return X_doa
# True reverberant
Y_doa = plot_doa(y_t, 'y_t')
# Estimated reverberant
O_doa = plot_doa(est_s_t, 'output_t')
# Difference
psa.plot_doa(Y_doa - O_doa, 'difference true-estimated')
plt.show()
# Reverberant
# Difference
psa.plot_doa(y2_t, 'y2')
plt.show()
psa.plot_doa(x2_t, 'x2')
plt.show()
#
# # Resynthesis signal
# R1_doa = plot_doa(r1_t, 'r1_t')
# R2_doa = plot_doa(r2_t, 'r2_t')
#
window_overlap = window_size // 2
_, _, S_dir = scipy.signal.stft(s_dir,
fs,
nperseg=window_size,
noverlap=window_overlap)
s_tot_ambi = psa.Signal(bformat, fs, 'acn', 'n3d')
S_tot_ambi = psa.Stft.fromSignal(s_tot_ambi,
window_size=window_size,
window_overlap=window_overlap)
doa = psa.compute_DOA(S_tot_ambi)
psa.plot_signal(s_tot_ambi)
psa.plot_magnitude_spectrogram(S_tot_ambi)
psa.plot_doa(doa)
### DIFFUSENESS
ita = S_tot_ambi.compute_ita(r=r)
psa.plot_directivity(ita, title='ita')
# ita_re = S_tot_ambi.compute_ita_re(r=r)
# psa.plot_directivity(ita_re, title='ita re')
### KSI
ksi = S_tot_ambi.compute_ksi(r=r)
psa.plot_directivity(ksi, title='ksi')
# ksi_re = S_tot_ambi.compute_ksi_re(r=r)
# psa.plot_directivity(ksi_re, title='ksi re')
plt.figure()
plt.plot(sh_rirs)
plt.show()
signal_len_samples = int(np.floor(1. * fs))
signal = np.random.randn(signal_len_samples)
reverberant_signal = np.zeros((signal_len_samples, 4))
for i in range(4):
reverberant_signal[:, i] = scipy.signal.fftconvolve(
signal, sh_rirs[:, i].squeeze())[:signal_len_samples]
x = psa.Signal(reverberant_signal.T, fs, 'acn', 'sn3d')
psa.plot_signal(x, title='waveform')
analysis_window_size = 512
window_overlap = analysis_window_size // 2
fft_size = analysis_window_size
stft = psa.Stft.fromSignal(x,
window_size=analysis_window_size,
window_overlap=window_overlap,
nfft=fft_size)
psa.plot_magnitude_spectrogram(stft, title='magnitude spectrogram')
doa = psa.compute_DOA(stft)
psa.plot_doa(doa, title='doa')
plt.show()
i = psa.compute_intensity_vector(stft)
psa.plot_magnitude_spectrogram(i)
plt.show()