diff --git a/matlab/tfgm/illustrations_gaborTools/compare_eigs_halko_gauss.m b/matlab/tfgm/illustrations_gaborTools/compare_eigs_halko_gauss.m
deleted file mode 100644
index d2b05c820890e0b441b1e2b25973829677bd31c4..0000000000000000000000000000000000000000
--- a/matlab/tfgm/illustrations_gaborTools/compare_eigs_halko_gauss.m
+++ /dev/null
@@ -1,132 +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
-
-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;
-
-[signals, dgt_params, signal_params, mask, dgt,idgt ] = get_mix(ind_loc, ...,
-    ind_wd, deb_ind_loc, deb_ind_wd, resampling_fs, sig_len,...,
-    win_len, win_type, alpha, seuil, radius);
-
-
-
-%%
-% 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)
-
-
-%%
-
-figure('name','engine'); 
-plot_spectrogram(mask,dgt_params, signal_params, dgt);
-title('Source')
-set(gca, 'FontSize', 20, 'fontName','Times');
-
-
-%%
-
-
-%% generate Gabor mutliplier
-
-gab_mul = gen_gabmul_operator(dgt, idgt, mask);
-
-%% evd  via halko
-
-% halko parameters
-
-tolerance_arrf = 1e-3;
-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;
-
-
-
-%% evd via eigs
-
-
-[U, D] = eigs(gab_mul, signal_params.sig_len, size(q_mat,2)) ;
-
-
-
-%%
-figure; semilogy(diag(D),'r-','LineWidth',2); hold on;
-semilogy(diag(evdn.D),'b-o','LineWidth',2);
-
-
-%%  similarit� valeurs propres
-abs_err = abs(diag(evdn.D)-diag(D))./abs(diag(D));
-
-%%
-figure; semilogy(abs_err,'LineWidth',2);
-grid on;
-set(gca, 'FontSize', 20, 'fontName','Times');
-axis square;
-xlabel('$k$','Interpreter','latex');
-ylabel('Relative absolute error')
-saveas(gcf,fullfile(pathname, 'relerror_eigs_sevd_gauss.png'));
-saveas(gcf,fullfile(pathname, 'relerror_eigs_sevd_gauss.fig'));
-
-
-%% Correlations
-
-Gram_mat =abs( evdn.U.*U);
-figure; imagesc(Gram_mat)
-
-corr = max(Gram_mat, [], 2);
-%%
-figure; 
-semilogy(sort(corr,'descend'),'LineWidth',2);
-% grid on;
-% axis square;
-% xlabel('$k$','Interpreter','latex');
-% ylabel('Correlation');
-% set(gca, 'FontSize', 20, 'fontName','Times');
-% saveas(gcf,fullfile(pathname, 'correlation_eigs_sevd_gauss.png'));
-
-%%