diff --git a/matlab/tfgm/exp_tf_filtering/exp_car_cuicui_one_region.m b/matlab/tfgm/exp_tf_filtering/exp_car_cuicui_one_region.m
deleted file mode 100644
index ee0a69cf60ff1a757ff5d8a85deaa1ce08d12c9f..0000000000000000000000000000000000000000
--- a/matlab/tfgm/exp_tf_filtering/exp_car_cuicui_one_region.m
+++ /dev/null
@@ -1,273 +0,0 @@
-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
diff --git a/matlab/tfgm/exp_tf_filtering/exp_filtering_out_car_cuicui_Pareas.m b/matlab/tfgm/exp_tf_filtering/exp_filtering_out_car_cuicui_Pareas.m
deleted file mode 100644
index c4075d182701861593f065d36f085e0f770fcc61..0000000000000000000000000000000000000000
--- a/matlab/tfgm/exp_tf_filtering/exp_filtering_out_car_cuicui_Pareas.m
+++ /dev/null
@@ -1,278 +0,0 @@
-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
-%
-%