diff --git a/matlab/tfgm/illustrations_gaborTools/illustration_cuicui_eigenvalues.m b/matlab/tfgm/illustrations_gaborTools/illustration_cuicui_eigenvalues.m
deleted file mode 100644
index 0734b64b1a95af0e7c94a7935d65d3580c49882d..0000000000000000000000000000000000000000
--- a/matlab/tfgm/illustrations_gaborTools/illustration_cuicui_eigenvalues.m
+++ /dev/null
@@ -1,144 +0,0 @@
-function [signal_params,dgt_params, signals,sdr_mix, mask, mask_area,....,
-    gab_mul,q_mat,evdn] = illustration_cuicui_eigenvalues(win_type, resampling_fs, sig_len,approx_win_len )
-
-
-%%
-
-pwd;
-pathname ='figures_JSTSP';
-if ~exist('figures_JSTSP','dir')
-    mkdir('figures_JSTSP');
-end
-addpath('figures_JSTSP')
-%% Signals parameters ans DGT parameters
-
-signal_params = generate_signal_parameters(resampling_fs, sig_len);
-dgt_params = generate_dgt_parameters(win_type, approx_win_len);
-
-%% Gabor frame operators
-
-[direct_dgt, adjoint_dgt, tight_direct_dgt, tight_adjoint_dgt,win,...,
-    win_tight] = get_dgt_operators(dgt_params, signal_params);
-
-%% Load  bird and engine signal  from GENESIS
-
-
-ind_engine = 4;
-ind_bird = 5;   
-
-deb = 0;
-[x_engine, x_bird] =  load_pairs(ind_engine, ind_bird, resampling_fs, signal_params.sig_len, deb);
-
-%gamma=0.75;
-signals = generate_mix_signal(x_engine, x_bird);
-
-
-%% plot signals spectrograms
-
-% compute dgt
-
-dgt = tight_direct_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);
-
-
-%plot their spectrogram
-
-figure('name','engine'); plot_spectrogram(tf_mat_engine,dgt_params, signal_params, dgt);
-title('engine')
-figure('name','bird'); plot_spectrogram(tf_mat_bird, dgt_params, signal_params, dgt);
-title('Bird')
-figure('name','mix'); plot_spectrogram(tf_mat_mix, dgt_params,signal_params, dgt);
-title('mix : engine-bird')
-
-
-%% Mix - SDR 
-
-sdr_mix = sdr(signals.target, signals.mix);
-
-fprintf('The SDR of the mixture is : %e\n', sdr_mix)
-
-%% Mask generation 
-
-% mask
-alpha=2; seuil = 0.02; radius = 3;
-mask = generate_mask(tf_mat_engine, tf_mat_bird, alpha, seuil, radius);
-[mask_area, mask_area_ratio] = get_mask_area(mask);
-
-%plot mask
-figure('name','mask'); plot_spectrogram(mask, dgt_params,signal_params, dgt);
-title(['mask :  mask-area = ',num2str(mask_area)])
-
-%% EVD  with Halko
-
-tolerance_arrf = 1e-6;
-proba_arrf = 1 - 1e-9;
-
-r =  compute_r(signal_params.sig_len, signal_params.sig_len, proba_arrf);
-
-
-% Generate multiplier
-gab_mul = gen_gabmul_operator(tight_direct_dgt, tight_adjoint_dgt, mask);
-  
-%stage 1
-q_mat = adaptative_randomized_range_finder(gab_mul, signal_params.sig_len, tolerance_arrf, r);
-
-fprintf('Q shape : %.f %.f\n', size(q_mat));
-
-% stage 2 : Halko
-
-% Evd decomposition via Nystrom
- 
-evdn = EVD_nystrom(gab_mul, q_mat);
-
-%%  Eigenvalue plot
-
-ss=diag(evdn.D);
-
-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
-subplot(122);
-
-%%
-semilogy(diag(evdn.D), 'Linewidth',3);
-hold on; 
-plot(45,ss(45),'k-*','Linewidth',3);
-plot(30,ss(30),'m-*','Linewidth',3);
-plot(1650,ss(1650),'g-*','Linewidth',3);
-plot(1740,ss(1740),'c-*','Linewidth',3);
-grid on;
-xlabel('k')
-legend({'$\sigma_k$','$\lambda$ = 45','$\lambda$ = 30', '$\lambda$ = 1650','$\lambda$ = 1740'},...,
-    'Interpreter','latex','Location','southwest')
-axis square
-set(gca, 'FontSize', 20, 'fontName','Times');
-%saveas(gcf,fullfile(pathname, 'eigenvalues_full_mask.png'));
-
-%%
-%eigsvect_hann = [45, 18, 1650, 2051];
-
-figure; 
-set(gcf,'position',[1, 1 900 400]);
-subplot(221);
-plot_spectrogram(evdn.U(:,1), dgt_params,signal_params, dgt);
-set(gca, 'FontSize', 20, 'fontName','Times');
-subplot(222);
-plot_spectrogram(evdn.U(:,2), dgt_params,signal_params, dgt);
-set(gca, 'FontSize', 20, 'fontName','Times');
-subplot(223);
-plot_spectrogram(evdn.U(:,30), dgt_params,signal_params, dgt);
-set(gca, 'FontSize', 20, 'fontName','Times');
-subplot(224);
-plot_spectrogram(evdn.U(:,1438), dgt_params,signal_params, dgt);
-set(gca, 'FontSize', 20, 'fontName','Times');
-saveas(gcf,fullfile(pathname, 'eigvectors_gauss.png'));
-
-%%
-save('illustration.mat','signal_params','dgt_params', 'signals','sdr_mix', 'mask', 'mask_area',....,
-    'gab_mul', 'q_mat', 'evdn')
-end
\ No newline at end of file
diff --git a/matlab/tfgm/illustrations_gaborTools/rank_asfunctionof_masksize.m b/matlab/tfgm/illustrations_gaborTools/rank_asfunctionof_masksize.m
deleted file mode 100644
index dda7b5209f339314e29b48f2c4e980f64ebefdc8..0000000000000000000000000000000000000000
--- a/matlab/tfgm/illustrations_gaborTools/rank_asfunctionof_masksize.m
+++ /dev/null
@@ -1,100 +0,0 @@
-clc; clear; close all;
-
-% The script permet d'�tuider l'estimation du rang par Halko vs eigs
-%% Repertoires pour les figures
-
-pwd;
-pathname ='figures_JSTSP';
-if ~exist('figures_JSTSP','dir')
-    mkdir('figures_JSTSP');
-end
-addpath('figures_JSTSP')
-
-%%  Parametres du signal
-
-resampling_fs=8000; % frequence d'echantillonage
-sig_len = 8192; % longueur du signal
-
-tolerance = 1e-6; %  parametres pour Halko
-proba = 1-1e-4;
-r =  compute_r(sig_len, sig_len, proba);
-
-%%  Generation des parametres de la DGT
-
-
-t_lim = [0.4, 0.6];
-f_lims = 0.2:0.05:0.6;
-
-win_type = 'hann';
-
-approx_win_len = 256;
-
-dgt_params = generate_dgt_parameters(win_type, approx_win_len);
-signal_params = generate_signal_parameters(resampling_fs, sig_len);
-[dgt, idgt] = get_stft_operators(dgt_params, signal_params);
-
-
-%%
-mask_area_list = zeros(length(f_lims),1);
-ranks_arrf = zeros(length(f_lims),1);
-ranks_eigs= zeros(length(f_lims),1);
-t_arrf = zeros(length(f_lims),1);
-t_eigs = zeros(length(f_lims),1);
-s_vec_list = cell(length(f_lims),1);
-
-
-seuil = 10^(-14); % pour la EVD via eigs
-%%
-for k =1:length(f_lims)
-    % Mask Generation
-    
-    f_lim =[0.1, f_lims(k)];
-    mask = generate_rectangular_mask(dgt_params.nbins,dgt_params.hop, signal_params.sig_len, t_lim, f_lim);
-    
-    [mask_area, mask_area_ratio] = get_mask_area(mask);
-    mask_area_list(k) = mask_area;
-    
-    fprintf('mask area:%.f\n', mask_area)
-    if mask_area>signal_params.sig_len
-        fprintf('attention %.f\n',k)
-    end
-    figure(k);
-    plot_mask(mask, dgt_params.nbins, dgt_params.hop, signal_params.fs);
-  
-    %% Gabor multiplier
-    gab_mul = gen_gabmul_operator(dgt, idgt, mask);
-    
-    %% EVD via Halko
-    tic;
-    q_mat = adaptative_randomized_range_finder(gab_mul, sig_len, tolerance, r);
-    t_arrf(k) = toc;
-    
-    ranks_arrf(k)= size(q_mat,2);
-    %% EVD via eigs
-    tic;
-    [u_mat,s] = eigs(gab_mul, signal_params.sig_len, signal_params.sig_len);
-    t_eigs(k) = toc;
-    
-    s_vec = diag(s);
-    s_vec_list{k} = s_vec;
-    ranks_eigs(k) = length(s_vec(s_vec > seuil));
-end
-
-%%
-save('rank_estimation.mat','ranks_arrf','ranks_eigs', 'mask_area_list',...,
-    't_arrf','t_eigs', 's_vec_list');
-
-%% plot figures
-
-figure;
-plot(mask_area_list,ranks_arrf,'LineWidth',3); hold on;
-plot(mask_area_list,ranks_eigs,'LineWidth',3);
-set(gca, 'FontSize', 15, 'fontName','Times');
-%set(gca,'XScale','log')
-xlabel('Mask area')
-ylabel('Estimated rank')
-grid('on');
-legend('Halko', 'eigs', 'Location','northwest');
-set(gca, 'FontSize', 20, 'fontName','Times');
-saveas(gcf,fullfile(pathname,'rank_estimation_gauss.png'));
-saveas(gcf,fullfile(pathname,'rank_estimation_gauss.fig'));
diff --git a/matlab/tfgm/illustrations_gaborTools/rank_estimation_halko_vs_eigs_gausswin.m b/matlab/tfgm/illustrations_gaborTools/rank_estimation_halko_vs_eigs_gausswin.m
deleted file mode 100644
index ae7c2a8efc01af112c42250146a2b4e5e681ec79..0000000000000000000000000000000000000000
--- a/matlab/tfgm/illustrations_gaborTools/rank_estimation_halko_vs_eigs_gausswin.m
+++ /dev/null
@@ -1,99 +0,0 @@
-clc; clear; close all;
-
-% The script permet d'�tuider l'estimation du rang par Halko vs eigs
-%% Repertoires pour les figures
-
-pwd;
-pathname ='figures_JSTSP';
-if ~exist('figures_JSTSP','dir')
-    mkdir('figures_JSTSP');
-end
-addpath('figures_JSTSP')
-
-%%  Parametres du signal
-
-resampling_fs=8000; % frequence d'echantillonage
-sig_len = 8192; % longueur du signal
-
-tolerance = 1e-6; %  parametres pour Halko
-proba = 1-1e-4;
-r =  compute_r(sig_len, sig_len, proba);
-
-%%  Generation des parametres de la DGT
-
-
-t_lim = [0.4, 0.6];
-f_lims = 0.2:0.05:0.6;
-
-win_type = 'gauss';
-
-approx_win_len = 64; % changer sinon trop long.
-
-dgt_params = generate_dgt_parameters(win_type, approx_win_len);
-signal_params = generate_signal_parameters(resampling_fs, sig_len);
-[dgt, idgt] = get_stft_operators(dgt_params, signal_params);
-
-
-%%
-mask_area_list = zeros(length(f_lims),1);
-ranks_arrf = zeros(length(f_lims),1);
-ranks_eigs= zeros(length(f_lims),1);
-t_arrf = zeros(length(f_lims),1);
-t_eigs = zeros(length(f_lims),1);
-s_vec_list = cell(length(f_lims),1);
-
-
-seuil = 10^(-14); % pour la EVD via eigs
-%%
-for k =1:length(f_lims)
-    fprintf("Je suis a l'iteration numero %.f patiente\n",k);
-    % Mask Generation
-    %%
-    f_lim =[0.1, f_lims(k)];
-    mask = generate_rectangular_mask(dgt_params.nbins,dgt_params.hop, signal_params.sig_len, t_lim, f_lim);
-    
-    [mask_area, mask_area_ratio] = get_mask_area(mask);
-    mask_area_list(k) = mask_area;
-    
-    fprintf('mask area:%.f\n', mask_area)
-    if mask_area>signal_params.sig_len
-        fprintf('attention %.f\n',k)
-    end
-    figure(k);
-    plot_mask(mask, dgt_params.nbins, dgt_params.hop, signal_params.fs);
-    %% Gabor multiplier
-    gab_mul = gen_gabmul_operator(dgt, idgt, mask);
-    
-    %% EVD via Halko
-    tic;
-    q_mat = adaptative_randomized_range_finder(gab_mul, sig_len, tolerance, r);
-    t_arrf(k) = toc;
-    
-    ranks_arrf(k)= size(q_mat,2);
-    %% EVD via eigs
-    tic;
-    [u_mat,s] = eigs(gab_mul, signal_params.sig_len, signal_params.sig_len);
-    t_eigs(k) = toc;
-    
-    s_vec = diag(s);
-    s_vec_list{k} = s_vec;
-    ranks_eigs(k) = length(s_vec(s_vec > seuil));
-end
-
-%%
-save('rank_estimation.mat','ranks_arrf','ranks_eigs', 'mask_area_list',...,
-    't_arrf','t_eigs', 's_vec_list');
-
-%% plot figures
-
-figure;
-plot(mask_area_list,ranks_arrf,'LineWidth',3); hold on;
-plot(mask_area_list,ranks_eigs,'LineWidth',3);
-%set(gca,'XScale','log')
-xlabel('Mask area')
-ylabel('Estimated rank')
-grid('on');
-legend('Rand-EVD', 'eigs', 'Location','northwest');
-set(gca, 'FontSize', 20, 'fontName','Times');
-saveas(gcf,fullfile(pathname,'rank_estimation_gauss.png'));
-saveas(gcf,fullfile(pathname,'rank_estimation_gauss.fig'));
diff --git a/matlab/tfgm/illustrations_gaborTools/run_illustration_cuicui_eigenvalues.m b/matlab/tfgm/illustrations_gaborTools/run_illustration_cuicui_eigenvalues.m
deleted file mode 100644
index ad95d49d0109fbda199ce5c590a27bfddecaebf6..0000000000000000000000000000000000000000
--- a/matlab/tfgm/illustrations_gaborTools/run_illustration_cuicui_eigenvalues.m
+++ /dev/null
@@ -1,14 +0,0 @@
-clc; clear; close all;
-
-%%
-
-resampling_fs=8000;
-sig_len = 16384;
-
-win_type = 'hann';
-approx_win_len = 128;
-
-%%
-
-[signal_params,dgt_params, signals,sdr_mix, mask, mask_area,....,
-    gab_mul,q_mat,evdn] = illustration_cuicui_eigenvalues(win_type, resampling_fs, sig_len,approx_win_len );