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matlab/tfgm/exp_tf_filtering/exp_car_cuicui_one_region.m deleted 100644 → 0
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View file @ 1a5662ba
clc; clear; close all;
%%
% experiments with
% - wideband sound: engine(Genesis)
% - localized sound: birdsong (Genesis)
%%
make_wav_pairs() % make sure alls pairs are ok
%% Figures Directory
pwd;
pathname ='figures_JSTSP';
if ~exist('figures_JSTSP','dir')
mkdir('figures_JSTSP');
end
addpath('figures_JSTSP')
%%
resampling_fs=8000;
sig_len = 16384;
win_type_list={'hann', 'gauss'};
win_len_list = [256, 128];
mask_params_hann =[4, 0.001, 1];
mask_params_gauss =[3, 0.002, 3];
mask_params = {mask_params_hann, mask_params_gauss};
eigsvect_hann = [45, 18, 1945, 2051];
eigsvect_gauss = [45, 8, 1650, 1438];
eigsvect = {eigsvect_hann, eigsvect_gauss };
e_target_list = [0.04, 0.049];
%%
for k=1:length(win_type_list)
k=2;
%% DGT parameters - gabor frames operators - analysis window
win_type = win_type_list{k};
win_len = win_len_list(k);
fprintf('analysis window : %s\n', win_type);
fprintf('win_len : %.f \n', win_len);
signal_params = generate_signal_parameters(resampling_fs, sig_len);
dgt_params = generate_dgt_parameters(win_type, win_len);
[dgt, idgt] = get_stft_operators(dgt_params, signal_params);
% plot associated window
figure;
plot_win(dgt_params.win, signal_params.fs, signal_params.sig_len, win_type)
title([num2str(win_type), ' - window']);
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, [num2str(win_type), num2str(win_len),'_window.png']));
%% Load signals - get mixtures - their spectrograms
ind_engine = 3;
ind_bird = 5;
deb = 0;
[x_engine, x_bird] = load_pairs(ind_engine, ind_bird, resampling_fs, signal_params.sig_len, deb);
signals = generate_mix_signal(x_engine, x_bird);
% compute dgt
tf_mat_engine = compute_dgt(signals.target, dgt );
tf_mat_bird = compute_dgt(signals.noise, dgt );
tf_mat_mix = compute_dgt(signals.mix, dgt );
% mix -sdr
sdr_mix = sdr(signals.target, signals.mix);
fprintf('The SDR of the mixture is : %e\n', sdr_mix)
%% Generate mask
% mask
alpha= mask_params{k}(1) ;
seuil = mask_params{k}(2);
radius = mask_params{k}(3);
mask = generate_mask(tf_mat_engine, tf_mat_bird, alpha, seuil, radius);
[mask_area, mask_area_ratio] = get_mask_area(mask);
%% Baselines reconstruction
%zero value method
x_zero = solver_tfgm_zero(tf_mat_mix, mask, idgt);
fprintf('Zeros filling SDR is : %e\n', sdr(signals.target, x_zero));
%interpolation + random phases method
x_interp= solver_tfgm_interp(tf_mat_mix, mask, idgt);
fprintf('interp + random phases filling SDR is : %e\n', sdr(signals.target, x_interp));
%% generate Gabor mutliplier
gab_mul = gen_gabmul_operator(dgt, idgt, mask);
%% evd via halko
% halko parameters
tolerance_arrf = 1e-6;
proba_arrf = 0.9999;
r = compute_r(signal_params.sig_len, signal_params.sig_len, proba_arrf);
% stage 1 Halko
tic;
q_mat = adaptative_randomized_range_finder(gab_mul, signal_params.sig_len, tolerance_arrf, r);
t_arrf = toc;
fprintf('Q shape : %.f %.f\n', size(q_mat));
% stage 2 : Halko
% Evd decomposition via Nystrom
tic;
evdn = EVD_nystrom(gab_mul, q_mat);
t_evdn = toc;
%% tf filtering reconstruction
u_mat = evdn.U;
s_vec = diag(evdn.D);
ut_x = U_transpose_observation( signals.mix, u_mat);
%%
e_target = e_target_list(k);
x_rec= solver_tfgm( signals.mix, u_mat,s_vec, ut_x);
obj_fun = @(lambda_coef) abs(e_target - norm(mask.*dgt(x_rec(lambda_coef))));
sdr_engine =@(lambda_coef) sdr(signals.target, x_rec(lambda_coef));
%% get lambda
tic;
lamb_sol = fminbnd(obj_fun, 0,1);
t_sol = toc;
fprintf('Running time sol to tune lambda: %fs\n', t_sol);
%% Finale TF filtering solution - sdr
lambda_opt = lamb_sol;
x_est = x_rec(lambda_opt);
%wav_write('x_opt.wav', x_est, signal_params.fs);
sdr_opt = sdr(signals.target, x_est);
sdr_zero = sdr(signals.target, x_zero);
sdr_interp = sdr(signals.target, x_interp);
sdr_mix = sdr( signals.target, signals.mix);
%
fprintf('Optimal lambda: %e\n', lambda_opt);
fprintf('Optimal SDR: :%e dB\n', sdr_opt);
fprintf('Zero filling SDR: %e dB\n',sdr_zero);
fprintf('Interp + random phases filling SDR: %e dB\n',sdr_interp);
fprintf('Mix SDR: %e dB\n',sdr_mix);
%% plot EVD Results
%% Eigenvalue plot - suplots
l1 = eigsvect{k}(1);
l2 = eigsvect{k}(2);
l3 = eigsvect{k}(3);
l4 = eigsvect{k}(4);
figure;
%set(gcf,'position',[1, 1 1100 400]);
%subplot(121);
plot_spectrogram(mask, dgt_params,signal_params, dgt);
set(gca, 'FontSize', 20, 'fontName','Times');
axis square
saveas(gcf,fullfile(pathname, ['mask_cuicui_', num2str(win_type), '.png']));
%subplot(122);
figure;
semilogy(diag(evdn.D), 'Linewidth',3);
hold on;
plot(l1,s_vec(l1),'k-*','Linewidth',3);
plot(l2,s_vec(l2),'m-*','Linewidth',3);
plot(l3,s_vec(l3),'g-*','Linewidth',3);
plot(l4,s_vec(l4),'c-*','Linewidth',3);
grid on;
xlabel('$k$','Interpreter','latex')
legend({'$\sigma_k$',['$\lambda$ =',num2str(l1)],['$\lambda$ =', num2str(l2)],....,
['$\lambda$ =', num2str(l3)], ['$\lambda$ =', num2str(l4)]},...,
'Interpreter','latex','Location','southwest')
axis square
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['eigval_cuicui_', num2str(win_type), '.png']));
%%
figure;
%set(gcf,'position',[1, 1 900 400]);
%subplot(221);
plot_spectrogram(evdn.U(:,l1), dgt_params,signal_params, dgt);
axis square
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['eigvect_', num2str(l1), '.png']));
%subplot(222);
figure;
plot_spectrogram(evdn.U(:,l2), dgt_params,signal_params, dgt);
axis square
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['eigvect_', num2str(l2), '.png']));
%subplot(223);
figure;
plot_spectrogram(evdn.U(:,l3), dgt_params,signal_params, dgt);
axis square
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['eigvect_', num2str(l3), '.png']));
%subplot(224);
figure;
plot_spectrogram(evdn.U(:,l4), dgt_params,signal_params, dgt);
axis square
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['eigvect_', num2str(l4), '.png']));
%%
%figure;
%set(gcf,'position',[1, 1 950 400]);
figure;
plot_spectrogram(signals.noise, dgt_params, signal_params, dgt)
title('perturbation: birdsong' );
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['birdsong_' , num2str(win_type),'_' , num2str(win_len),'.png']));
figure;
plot_spectrogram(signals.mix, dgt_params, signal_params, dgt)
title(['car+birdsong : SDR= ',num2str(sdr_mix),'dB']);
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['mix_birdsong_' , num2str(win_type),'_' , num2str(win_len),'.png']));
figure;
plot_spectrogram(signals.target, dgt_params, signal_params,dgt)
title('true source: car')
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['car_' , num2str(win_type),'_' , num2str(win_len),'.png']));
figure;
plot_spectrogram(mask, dgt_params,signal_params, dgt);
title(['mask car +birdsong : area = ',num2str(mask_area)]);
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['mask_' , num2str(win_type),'_' , num2str(win_len),'.png']));
figure;
plot_spectrogram(x_zero, dgt_params, signal_params,dgt)
title(['Zero fill SDR= ', num2str(sdr_zero),'dB'])
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['x_zero_' , num2str(win_type),'_' , num2str(win_len),'.png']));
figure;
plot_spectrogram(x_interp, dgt_params, signal_params,dgt)
title(['interp SDR= ', num2str(sdr_interp),'dB'])
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['interp_' , num2str(win_type),'_' , num2str(win_len),'.png']));
figure;
plot_spectrogram(x_est, dgt_params, signal_params,dgt)
title(['\lambda\_op = ' ,num2str(lambda_opt,2), ' - SDR=' , num2str(sdr_opt), 'dB'])
set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname, ['x_est_' , num2str(win_type),'_' , num2str(win_len),'.png']));
%%
file_name = ['exp_engine_cuicui_1area_', num2str(win_type),'.mat'];
save(file_name,'signal_params','dgt_params', 'signals','mask','mask_area',....,
'gab_mul', 'q_mat', 'evdn','sdr_mix', 'sdr_opt','sdr_zero','sdr_interp','x_zero','x_interp','x_est','lambda_opt','e_target')
end
matlab/tfgm/exp_tf_filtering/exp_filtering_out_car_cuicui_Pareas.m deleted 100644 → 0
+ 0
− 278
View file @ 1a5662ba
clc; clear; close all;
%
%%
pwd;
pathname ='figures_JSTSP';
if ~exist('figures_JSTSP','dir')
mkdir('figures_JSTSP');
end
addpath('figures_JSTSP')
%%
ind_loc = 5;
ind_wd = 3;
deb_ind_loc = 0;
deb_ind_wd=0;
resampling_fs = 8000;
sig_len = 16384;
%% DGT params - signals - mask
clc;
param_gauss = get_win_gauss_param();
win_len = param_gauss.win_len;
win_type = param_gauss.win_type;
alpha = param_gauss.alpha;
seuil = param_gauss.seuil;
radius = param_gauss.radius;
approx_win_len = 128;
hop =32;
nbins=512;
[signals, dgt_params, signal_params, mask, dgt,idgt] = get_mix(ind_loc, ...,
ind_wd, deb_ind_loc, deb_ind_wd, resampling_fs, sig_len,...,
approx_win_len,hop, nbins, win_type, alpha, seuil, radius);
[mask_area, mask_area_ratio] = get_mask_area(mask);
fprintf("We work with %s window of length %.f\n", win_type, win_len);
fprintf("Gabor transform parameters are: \n")
fprintf('hop :%2.f\n', dgt_params.hop);
fprintf('n_bins: %2.f\n', dgt_params.nbins);
fprintf("The parameters for smoothing the mask are: \n")
fprintf("alpha = %f\n", alpha);
fprintf("seuil = %f\n", seuil);
fprintf("radius = %f\n", radius);
% plot associated window
figure;
plot_win(dgt_params.win, signal_params.fs, signal_params.sig_len, win_type)
title([num2str(win_type), ' - window']);
set(gca, 'FontSize', 20, 'fontName','Times');
%saveas(gcf,fullfile(pathname, [num2str(win_type),'_window.png']));
%% Spectrogrammes - Mask
tf_mat_engine = compute_dgt(signals.target, dgt);
tf_mat_bird = compute_dgt(signals.noise, dgt);
tf_mat_mix = compute_dgt(signals.mix, dgt);
%plot_spectrogram(x, dgt_params, signal_params, dgt, dynrange, clim)
dynrange=90;
figure('name','engine'); plot_spectrogram(tf_mat_engine, dgt_params, signal_params, dgt);
title('Source')
set(gca, 'FontSize', 20, 'fontName','Times');
figure('name','bird'); plot_spectrogram(tf_mat_bird, dgt_params, signal_params, dgt);
title('perturbation')
set(gca, 'FontSize', 20, 'fontName','Times');
figure('name','mix');
plot_spectrogram(tf_mat_mix, dgt_params,signal_params, dgt);
set(gca, 'FontSize', 20, 'fontName','Times');
%title('mix')
%saveas(gcf,fullfile(pathname, 'engine_bird_mixture.png'));
%plot mask
figure('name','mask'); plot_spectrogram(mask, dgt_params,signal_params, dgt );
%title(['mask : mask-area = ',num2str(mask_area)]);
set(gca, 'FontSize', 20, 'fontName','Times');
%saveas(gcf,fullfile(pathname, 'mask_cuicui_gauss.png'));
% figure;
% plot_spectrogram((1-mask).*tf_mat_bird, dgt_params,signal_params, dgt);
fprintf("mask area is: %f\n",mask_area);
%% Mix - SDR
sdr_mix = sdr(signals.target, signals.mix);
fprintf('The SDR of the mixture is : %e\n', sdr_mix)
%%
%[mask_limast,mask_labeled, mask_area_list] = make_subregions(mask, dgt_params, signal_params);
%pq_norms_val = pq_norms(sig_len,dgt,idgt,mask_list);
%pq_norms_val1 = get_pq_norms(sig_len, dgt, idgt, mask_labeled);
%pq_norms = get_pq_norms(sig_len, dgt, idgt, mask_list);
%%
tol=10^(-3);
[pq_norms_val, mask_labeled] = create_subregions(mask, dgt, idgt, ...,
dgt_params, signal_params, tol);
%%
final_mask_labeled = mask_labeled;
[gabmul_list, mask_list] = get_P_gabmul(final_mask_labeled, dgt, idgt);
%%
x_mix = signals.mix;
tolerance_arrf = 10^(-3);
proba_arrf = 0.999;
[t_arrf,t_evdn, t_ut_x, rank_q, s_vec_list, u_mat_list,...,
ut_x_list,r] = compute_decomposition(x_mix, mask_list, gabmul_list,...,
tolerance_arrf, proba_arrf);
%%
lambda_coef=0.01;
x=compute_estimate(lambda_coef, x_mix, s_vec_list, u_mat_list, ut_x_list);
%%
[lambda_est, t_est] = compute_lambda(x_mix, mask, dgt_params,...,
signal_params, dgt, s_vec_list, u_mat_list, ut_x_list,...,
gabmul_list);
%% figure
figure;
imagesc(real(log10(pq_norms_val)))
ylabel('$p$','Interpreter','latex')
xlabel('$q$', 'Interpreter', 'latex')
colorbar()
set(gca, 'FontSize', 20, 'fontName','Times');
%title('Final norms of Gabor multiplier composition')
%saveas(gcf,fullfile(pathname, 'norm_mulpq.png'));
%% fixed
gabmul_list = get_P_gabmul(mask_labels, dgt, idgt);
%%
rank = 10;
x_mix = signals.mix;
[t_rrf,t_evdn, t_ut_x, s_vec_list, u_mat_list,...,
ut_x_list]=compute_decomposition_fixed_rank(x_mix, mask_labels, gabmul_list, rank);
%%
tolerance_arrf = 0.1;
proba_arrf = 0.9;
[mask_labels, mask_area_list,n_labels] = make_subregions(mask, dgt_params, signal_params);
[t_arrf,t_evdn, t_ut_x, rank_q, s_vec_list, u_mat_list,...,
ut_x_list,r] = compute_decomposition(x_mix, mask_labels, gabmul_list,...,
tolerance_arrf, proba_arrf);
%%
n_areas = length(mask_area);
lambda_coef = ones(n_areas,1);
x=compute_estimate(lambda_coef, x_mix, n_areas,s_vec_list, u_mat_list, ut_x_list);
%%
x_target = signals.target;
[lambda_oracle, t_oracle] = compute_lambda_oracle_sdr(x_target, x_mix,...,
n_areas,s_vec_list, u_mat_list, ut_x_list);
%%
[lambda_est, t_est] = compute_lambda(x_mix, mask, dgt_params,...,
signal_params, dgt, mask_labels, s_vec_list, u_mat_list, ut_x_list,...,
gabmul_list);
%% parametres de la EVD halko
tolerance_arrf = 1e-6;
proba_arrf = 1 - 1e-4;
x_mix = signals.mix;
compute_decomposition(x_mix,mask_labels, gabmul_list,tolerance_arrf, proba_arrf);
%% create subregions
[mask_labels, mask_area_list,n_labels] = make_subregions(mask, dgt_params, signal_params);
%%
e_target = zeros(n_labels, 1);
for k=1:n_labels
e_target_k = norm(mask_labels{k}.*tf_mat_engine) ;
e_target(k) = e_target_k;
end
%%
x_mix = signals.mix;
x_target = signals.target;
[x_rec, t_arrf, t_evdn, t_ut_x, rank_q, s_vec_list, u_mat_list,...,
ut_x_list, lambda_vec_opt] = filtering_out_Pareas(x_mix, mask_labels, dgt, idgt, x_target, tolerance_arrf, proba_arrf);
%%
i_p=1;
pq_norms_val = update_pq_norms(mask_labels, pq_norms, i_p, signal_params, dgt, idgt);
%%
i_p = 2;
i_q=1;
[ mask, pq_norms_val] = merge_subregions(mask, pq_norms_val, i_p, i_q);
%%
figure;
plot_spectrogram(x_rec, dgt_params, signal_params,dgt);
%%
%%
% %% Generate list of Gabor multipliers
%
% gab_mul_list = get_P_gabmul(mask_labels, dgt, idgt);
%
% %% EVD decomposition
%
% [t_arrf, t_evdn, t_uh_x, s_vec_list, u_mat_list, t_uh_x_list, ...,
% rank_q] = compute_decomposition(mask_labels, gab_mul_list, dgt_params, signal_params, signals.mix, tolerance_arrf,r);
%
% %% Tuning Lambda
% n_areas = n_labels;
% uh_x_list = t_uh_x_list;
%
%
%%
%%
% %%
% %obj_fun = @(lambda_vec) norm(signals.target - compute_estimate(lambda_vec, s_vec_list, u_mat_list, uh_x_list,n_areas, signals.mix));
% obj_fun = @(lambda_vec) abs(e_target -norm( mask.*dgt(compute_estimate(lambda_vec, s_vec_list, u_mat_list, uh_x_list,n_areas, signals.mix))));
%
% %% Generate and save msk for each regions
% x0= ones(n_labels,1);
% tic;
% sol = fmincon(obj_fun,x0);
% t1 =toc;
%
% %%
% lambda_opt = obj_fun(x0);
% x_est = compute_estimate(lambda_opt, s_vec_list, u_mat_list, uh_x_list, n_areas,signals.mix);
%
% figure;
% plot_spectrogram(x_est, dgt_params, signal_params,dgt);
% %%
% %
% % all_mask = zeros(size(mask,1)*size(mask,2),n_labels);
% %
% %
% % for k =1:n_labels
% % [mask_label,~] = bwlabel(mask);
% %
% % % on construit chaque mask
% % mask_label(mask_label~=k)=0;
% % mask_ =mask_label;
% % all_mask(:,k) = mask_(:);
% % figure(k); plotdgtreal(mask_, dgt_params.hop, dgt_params.nbins, signal_params.fs);
% % title(['k=', num2str(k)])
% %
% % [mask_area_, mask_area_ratio_] = get_mask_area(mask_);
% % mask_area_list(k) = mask_area_;
% % fprintf('mask area = %.f\n',mask_area_);
% %
% % end
%
%
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