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Commit 54baef77 authored by Marina Kreme's avatar Marina Kreme
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load mask

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matlab/tfgm/scripts/exp_gabmul_eigs_properties.m
+ 55
− 65
View file @ 54baef77
...@@ -5,12 +5,8 @@ clc; clear; close all; ...@@ -5,12 +5,8 @@ clc; clear; close all;
% the time-frequency parameters generated with a Gauss window of 256 time samples. % the time-frequency parameters generated with a Gauss window of 256 time samples.
%% Load time-frequency mask from .mat file %% Load time-frequency mask from .mat file
mask_gauss = load('mask_with_gauss256_win.mat');
mask_hann = load('mask_with_hann512_win.mat');
%save masks in a structure mask_gauss = load('mask.mat');
mask_dict.('Gauss256') = mask_gauss;
mask_dict.('Hann512') = mask_hann;
%% directory for figures %% directory for figures
...@@ -64,69 +60,63 @@ end ...@@ -64,69 +60,63 @@ end
%% Compute the evd of the multiplier by using the DGT and IDGT %% Compute the evd of the multiplier by using the DGT and IDGT
% generated from the Gauss window. The calculation of the evd is % generated from the Gauss window
% done for each of the two Gauss and Hann masks.
%% %%
nb_eigvalues=4000; nb_eigvalues=4000;
masks = fieldnames(mask_dict);
evdn_gauss ={};
sig_len = param_dict.Gauss256.signal_params.sig_len; sig_len = param_dict.Gauss256.signal_params.sig_len; % signal length
for k=1: length(masks)
disp(k);
gab_mul = gen_gabmul_operator(param_dict.('Gauss256').dgt, ..., gab_mul = gen_gabmul_operator(param_dict.('Gauss256').dgt, ...,
param_dict.('Gauss256').idgt, mask_dict.(masks{k}).mask); param_dict.('Gauss256').idgt, mask_gauss.mask);
tic; tic;
q_mat = randomized_range_finder(gab_mul, sig_len, nb_eigvalues); q_mat = randomized_range_finder(gab_mul, sig_len, nb_eigvalues);
t1= toc; t1= toc;
fprintf(" runtimes for Q: %f\n", t1); fprintf(" runtimes for Q: %f\n", t1);
tic; tic;
evdn = EVD_nystrom(gab_mul, q_mat); evdn_gauss = EVD_nystrom(gab_mul, q_mat);
t2 = toc; t2 = toc;
evdn_gauss.(masks{k}) = evdn;
fprintf(" runtimes for nystrom - gauss: %f\n", t2); fprintf(" runtimes for nystrom - gauss: %f\n", t2);
end
%% Compute the evd of the multiplier by using the DGT and IDGT %% Compute the evd of the multiplier by using the DGT and IDGT
% generated from the Hanns window. The calculation of the evd is % generated from the Hann window. The calculation of the evd is
% done for each of the two Gauss and Hann masks. % done with the Gauss mask
%%
evdn_hann = {};
%% %%
for k=1: length(masks) % gabor multiplier
gab_mul = gen_gabmul_operator(param_dict.('Hann512').dgt, ..., gab_mul = gen_gabmul_operator(param_dict.('Hann512').dgt, ...,
param_dict.('Hann512').idgt, mask_dict.(masks{k}).mask); param_dict.('Hann512').idgt, mask_gauss.mask);
%rand-evd
tic; tic;
q_mat = randomized_range_finder(gab_mul, sig_len, nb_eigvalues); q_mat = randomized_range_finder(gab_mul, sig_len, nb_eigvalues); %rand-evd
t1= toc; t1= toc;
fprintf(" runtimes for Q: %f\n", t1); fprintf(" runtimes for Q: %f\n", t1);
tic; tic;
evdn = EVD_nystrom(gab_mul, q_mat); evdn_hann = EVD_nystrom(gab_mul, q_mat);
t2 = toc; t2 = toc;
evdn_hann.((masks{k})) = evdn;
fprintf(" runtimes for nystrom-hann: %f\n", t2); fprintf(" runtimes for nystrom-hann: %f\n", t2);
end
%% eigenvalues %% eigenvalues
%%
figure; figure;
D1 = diag(evdn_hann.Gauss256.D); D1 = diag(evdn_hann.D);
D4= diag(evdn_gauss.Gauss256.D); D2= diag(evdn_gauss.D);
semilogy(D4,'b', 'Linewidth',4); semilogy(D2,'b', 'Linewidth',4);
hold on; hold on;
semilogy(D1, 'r','Linewidth',4); semilogy(D1, 'r','Linewidth',4);
semilogy(D4(1),'bo', 'Linewidth', 15,'MarkerSize',10) semilogy(D2(1),'bo', 'Linewidth', 15,'MarkerSize',10)
semilogy(D1(1),'r^','Linewidth', 15,'MarkerSize',10) semilogy(D1(1),'r^','Linewidth', 15,'MarkerSize',10)
semilogy(3539,D4(3539),'bo','Linewidth', 15,'MarkerSize',10) semilogy(3199,D2(3199),'bo','Linewidth', 15,'MarkerSize',10)
semilogy(3408,D1(3408),'r^','Linewidth', 15,'MarkerSize',10) semilogy(3072,D1(3072),'r^','Linewidth', 15,'MarkerSize',10)
grid on; grid on;
...@@ -136,8 +126,8 @@ ylabel({' Eigenvalues $\sigma[k]$'},'Interpreter','latex'); ...@@ -136,8 +126,8 @@ ylabel({' Eigenvalues $\sigma[k]$'},'Interpreter','latex');
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
l = legend('Gauss','Hann',..., l = legend('Gauss','Hann',...,
'$\sigma[1]$ = 1, $\sigma[3539] = 2.699 \times 10^{-4}$',..., '$\sigma[1]$ = 1, $\sigma[3072] = 3.914 \times 10^{-4}$',...,
'$\sigma[1]$ =1, $\sigma[3408] = 1.258 \times 10^{-4}$',..., '$\sigma[1]$ =1, $\sigma[3199] = 3.865 \times 10^{-4}$',...,
'Location','southwest'); 'Location','southwest');
set(l, 'interpreter', 'latex') set(l, 'interpreter', 'latex')
...@@ -146,34 +136,34 @@ saveas(gcf,fullfile(fig_dir, 'eigenvalues_gauss_hann.png')); ...@@ -146,34 +136,34 @@ saveas(gcf,fullfile(fig_dir, 'eigenvalues_gauss_hann.png'));
%% eigenvectors %% eigenvectors
eigs_gauss = evdn_gauss.Gauss256.U; eigs_gauss = evdn_gauss.U;
figure; figure;
set(gcf,'position',[1, 1 1000 800]); set(gcf,'position',[1, 1 1000 800]);
subplot(221); subplot(221);
plot_spectrogram(eigs_gauss(:,147), param_dict.('Gauss256').dgt_params,..., plot_spectrogram(eigs_gauss(:,1), param_dict.('Gauss256').dgt_params,...,
param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt); param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt);
yticks([0,1000,2000,3000,4000]); yticks([0,1000,2000,3000,4000]);
yticklabels([0,1,2,3,4]); yticklabels([0,1,2,3,4]);
ylabel('Frequency (kHz)') ylabel('Frequency (kHz)')
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
subplot(222); subplot(222);
plot_spectrogram(eigs_gauss(:,86), param_dict.('Gauss256').dgt_params,..., plot_spectrogram(eigs_gauss(:,100), param_dict.('Gauss256').dgt_params,...,
param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt); param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt);
yticks([0,1000,2000,3000,4000]); yticks([0,1000,2000,3000,4000]);
yticklabels([0,1,2,3,4]); yticklabels([0,1,2,3,4]);
ylabel('Frequency (kHz)') ylabel('Frequency (kHz)')
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
subplot(223); subplot(223);
plot_spectrogram(eigs_gauss(:,3039), param_dict.('Gauss256').dgt_params,..., plot_spectrogram(eigs_gauss(:,3199), param_dict.('Gauss256').dgt_params,...,
param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt); param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt);
yticks([0,1000,2000,3000,4000]); yticks([0,1000,2000,3000,4000]);
yticklabels([0,1,2,3,4]); yticklabels([0,1,2,3,4]);
ylabel('Frequency (kHz)') ylabel('Frequency (kHz)')
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
subplot(224); subplot(224);
plot_spectrogram(eigs_gauss(:,3046), param_dict.('Gauss256').dgt_params,..., plot_spectrogram(eigs_gauss(:,3072), param_dict.('Gauss256').dgt_params,...,
param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt); param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt);
ylabel('Frequency (kHz)') ylabel('Frequency (kHz)')
yticks([0,1000,2000,3000,4000]); yticks([0,1000,2000,3000,4000]);
...@@ -194,7 +184,7 @@ saveas(gcf,fullfile(fig_dir, 'mask_gauss.pdf')); ...@@ -194,7 +184,7 @@ saveas(gcf,fullfile(fig_dir, 'mask_gauss.pdf'));
%% %%
save('evdn_gauss_hann.mat','evdn_hann','evdn_gauss','param_dict','mask_dict'); save('evdn_gauss_hann.mat','evdn_hann','evdn_gauss','param_dict','mask_gauss');
......
matlab/tfgm/scripts/rank_estimation_halko_vs_eigs_gausswin.m
+ 42
− 27
View file @ 54baef77
clc; clear; close all; clc; clear; close all;
% The script permet d'tuider l'estimation du rang par Halko vs eigs % The script compares the rank of the Gabor multiplier estimated
%% Repertoires pour les figures % by rand-evd (see [1]) and eigs (see [2]).
%[1] *Finding structure with randomness: Probabilistic algorithms for`
% constructing approximate matrix decompositions*, by N. Halko, P.-G. Martinsson,
% and J. A. Tropp,
%[2]*A Krylov-Schur Algorithm for Large Eigenproblems*, by Stewart, G.W.
%% figures directory
pwd; pwd;
pathname ='figures_JSTSP'; pathname ='fig_rank_randEvd_vs_eigs';
if ~exist('figures_JSTSP','dir') if ~exist(pathname,'dir')
mkdir('figures_JSTSP'); mkdir(pathname);
end end
addpath('figures_JSTSP') addpath(pathname)
%% Parametres du signal %% Signal_params
resampling_fs=8000; % frequence d'echantillonage fs=8000;
sig_len = 8192; % longueur du signal sig_len = 16384;
signal_params = generate_signal_parameters(fs, sig_len);
tolerance = 1e-6; % parametres pour Halko
proba = 1-1e-4;
r = compute_r(sig_len, sig_len, proba);
%% Generation des parametres de la DGT %% Rand-evd params
tolerance = 1e-6; %
proba = 1-1e-4;
r = compute_r(sig_len, sig_len, proba);
%% DGT params and operators
t_lim = [0.4, 0.6]; t_lim = [0.4, 0.6];
f_lims = 0.2:0.05:0.6; f_lims = 0.2:0.05:0.6;
win_type = 'gauss'; win_type = 'gauss';
approx_win_len = 64; % changer sinon trop long. win_len = 256;
params = get_params(win_len, win_type);
dgt_params = generate_dgt_parameters(win_type, approx_win_len); hop = params.hop;
signal_params = generate_signal_parameters(resampling_fs, sig_len); nbins =params.nbins;
[dgt, idgt] = get_stft_operators(dgt_params, signal_params); dgt_params = generate_dgt_parameters(win_type, win_len, hop, nbins, sig_len);
[dgt, idgt] = get_stft_operators(dgt_params, signal_params);
%% %%
mask_area_list = zeros(length(f_lims),1); mask_area_list = zeros(length(f_lims),1);
...@@ -43,12 +55,12 @@ t_eigs = zeros(length(f_lims),1); ...@@ -43,12 +55,12 @@ t_eigs = zeros(length(f_lims),1);
s_vec_list = cell(length(f_lims),1); s_vec_list = cell(length(f_lims),1);
seuil = 10^(-14); % pour la EVD via eigs thres = 10^(-14); % using for evd with eigs
%% %%
for k =1:length(f_lims) for k =1:length(f_lims)
fprintf("Je suis a l'iteration numero %.f patiente\n",k); fprintf("first iteration %.f please wait\n",k);
% Mask Generation % Mask Generation
%%
f_lim =[0.1, f_lims(k)]; 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 = generate_rectangular_mask(dgt_params.nbins,dgt_params.hop, signal_params.sig_len, t_lim, f_lim);
...@@ -61,16 +73,17 @@ for k =1:length(f_lims) ...@@ -61,16 +73,17 @@ for k =1:length(f_lims)
end end
figure(k); figure(k);
plot_mask(mask, dgt_params.nbins, dgt_params.hop, signal_params.fs); plot_mask(mask, dgt_params.nbins, dgt_params.hop, signal_params.fs);
%% Gabor multiplier
%Gabor multiplier
gab_mul = gen_gabmul_operator(dgt, idgt, mask); gab_mul = gen_gabmul_operator(dgt, idgt, mask);
%% EVD via Halko %rand-evd
tic; tic;
q_mat = adaptative_randomized_range_finder(gab_mul, sig_len, tolerance, r); q_mat = adaptative_randomized_range_finder(gab_mul, sig_len, tolerance, r);
t_arrf(k) = toc; t_arrf(k) = toc;
ranks_arrf(k)= size(q_mat,2); ranks_arrf(k)= size(q_mat,2);
%% EVD via eigs eigs
tic; tic;
[u_mat,s] = eigs(gab_mul, signal_params.sig_len, signal_params.sig_len); [u_mat,s] = eigs(gab_mul, signal_params.sig_len, signal_params.sig_len);
t_eigs(k) = toc; t_eigs(k) = toc;
...@@ -80,9 +93,7 @@ for k =1:length(f_lims) ...@@ -80,9 +93,7 @@ for k =1:length(f_lims)
ranks_eigs(k) = length(s_vec(s_vec > seuil)); ranks_eigs(k) = length(s_vec(s_vec > seuil));
end end
%%
save('rank_estimation.mat','ranks_arrf','ranks_eigs', 'mask_area_list',...,
't_arrf','t_eigs', 's_vec_list');
%% plot figures %% plot figures
...@@ -97,3 +108,7 @@ legend('Rand-EVD', 'eigs', 'Location','northwest'); ...@@ -97,3 +108,7 @@ legend('Rand-EVD', 'eigs', 'Location','northwest');
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(pathname,'rank_estimation_gauss.png')); saveas(gcf,fullfile(pathname,'rank_estimation_gauss.png'));
saveas(gcf,fullfile(pathname,'rank_estimation_gauss.fig')); saveas(gcf,fullfile(pathname,'rank_estimation_gauss.fig'));
%%
save('rank_estimation.mat','ranks_arrf','ranks_eigs', 'mask_area_list',...,
't_arrf','t_eigs', 's_vec_list');
matlab/tfgm/scripts/script_gabmul_eigs_properties.m
+ 27
− 21
View file @ 54baef77
...@@ -12,18 +12,20 @@ end ...@@ -12,18 +12,20 @@ end
addpath(fig_dir) addpath(fig_dir)
%% %%
%%
figure; figure;
D1 = diag(evdn_hann.Gauss256.D); D1 = diag(evdn_hann.D);
D4= diag(evdn_gauss.Gauss256.D); D2= diag(evdn_gauss.D);
semilogy(D4,'b', 'Linewidth',4); semilogy(D2,'b', 'Linewidth',4);
hold on; hold on;
semilogy(D1, 'r','Linewidth',4); semilogy(D1, 'r','Linewidth',4);
semilogy(D4(1),'bo', 'Linewidth', 15,'MarkerSize',10) semilogy(D2(1),'bo', 'Linewidth', 15,'MarkerSize',10)
semilogy(D1(1),'r^','Linewidth', 15,'MarkerSize',10) semilogy(D1(1),'r^','Linewidth', 15,'MarkerSize',10)
semilogy(3539,D4(3539),'bo','Linewidth', 15,'MarkerSize',10) semilogy(3199,D2(3199),'bo','Linewidth', 15,'MarkerSize',10)
semilogy(3408,D1(3408),'r^','Linewidth', 15,'MarkerSize',10) semilogy(3072,D1(3072),'r^','Linewidth', 15,'MarkerSize',10)
grid on; grid on;
...@@ -33,40 +35,45 @@ ylabel({' Eigenvalues $\sigma[k]$'},'Interpreter','latex'); ...@@ -33,40 +35,45 @@ ylabel({' Eigenvalues $\sigma[k]$'},'Interpreter','latex');
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
l = legend('Gauss','Hann',..., l = legend('Gauss','Hann',...,
'$\sigma[1]$ = 1, $\sigma[3539] = 2.699 \times 10^{-4}$',..., '$\sigma[1]$ = 1, $\sigma[3072] = 3.914 \times 10^{-4}$',...,
'$\sigma[1]$ =1, $\sigma[3408] = 1.258 \times 10^{-4}$',..., '$\sigma[1]$ =1, $\sigma[3199] = 3.865 \times 10^{-4}$',...,
'Location','southwest'); 'Location','southwest');
set(l, 'interpreter', 'latex') set(l, 'interpreter', 'latex')
saveas(gcf,fullfile(fig_dir, 'eigenvalues_gauss_hann.fig')); saveas(gcf,fullfile(fig_dir, 'eigenvalues_gauss_hann.fig'));
saveas(gcf,fullfile(fig_dir, 'eigenvalues_gauss_hann.png')); saveas(gcf,fullfile(fig_dir, 'eigenvalues_gauss_hann.png'));
%% eigenvectors %% eigenvectors
eigs_gauss = evdn_gauss.U;
figure; figure;
eigs_gauss = evdn_gauss.Gauss256.U;
set(gcf,'position',[1, 1 1000 800]); set(gcf,'position',[1, 1 1000 800]);
subplot(221); subplot(221);
plot_spectrogram(eigs_gauss(:,147), param_dict.('Gauss256').dgt_params,..., plot_spectrogram(eigs_gauss(:,1), param_dict.('Gauss256').dgt_params,...,
param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt); param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt);
yticks([0,1000,2000,3000,4000]); yticks([0,1000,2000,3000,4000]);
yticklabels([0,1,2,3,4]); yticklabels([0,1,2,3,4]);
ylabel('Frequency (kHz)') ylabel('Frequency (kHz)')
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
subplot(222); subplot(222);
plot_spectrogram(eigs_gauss(:,86), param_dict.('Gauss256').dgt_params,..., plot_spectrogram(eigs_gauss(:,100), param_dict.('Gauss256').dgt_params,...,
param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt); param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt);
yticks([0,1000,2000,3000,4000]); yticks([0,1000,2000,3000,4000]);
yticklabels([0,1,2,3,4]); yticklabels([0,1,2,3,4]);
ylabel('Frequency (kHz)') ylabel('Frequency (kHz)')
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
subplot(223); subplot(223);
plot_spectrogram(eigs_gauss(:,3039), param_dict.('Gauss256').dgt_params,..., plot_spectrogram(eigs_gauss(:,3199), param_dict.('Gauss256').dgt_params,...,
param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt); param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt);
yticks([0,1000,2000,3000,4000]); yticks([0,1000,2000,3000,4000]);
yticklabels([0,1,2,3,4]); yticklabels([0,1,2,3,4]);
ylabel('Frequency (kHz)') ylabel('Frequency (kHz)')
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
subplot(224); subplot(224);
plot_spectrogram(eigs_gauss(:,3046), param_dict.('Gauss256').dgt_params,..., plot_spectrogram(eigs_gauss(:,3072), param_dict.('Gauss256').dgt_params,...,
param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt); param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt);
ylabel('Frequency (kHz)') ylabel('Frequency (kHz)')
yticks([0,1000,2000,3000,4000]); yticks([0,1000,2000,3000,4000]);
...@@ -76,7 +83,6 @@ saveas(gcf,fullfile(fig_dir, 'eigvectors_prop_illustration.png')); ...@@ -76,7 +83,6 @@ saveas(gcf,fullfile(fig_dir, 'eigvectors_prop_illustration.png'));
saveas(gcf,fullfile(fig_dir, 'eigvectors_prop_illustration.fig')); saveas(gcf,fullfile(fig_dir, 'eigvectors_prop_illustration.fig'));
%% mask %% mask
mask_gauss = mask_dict.Gauss256;
figure; figure;
plot_spectrogram(mask_gauss.mask, param_dict.('Gauss256').dgt_params,..., plot_spectrogram(mask_gauss.mask, param_dict.('Gauss256').dgt_params,...,
param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt); param_dict.('Gauss256').signal_params, param_dict.('Gauss256').dgt);
...@@ -84,7 +90,7 @@ ylabel('Frequency (kHz)') ...@@ -84,7 +90,7 @@ ylabel('Frequency (kHz)')
yticks([0,1000,2000,3000,4000]); yticks([0,1000,2000,3000,4000]);
yticklabels([0,1,2,3,4]); yticklabels([0,1,2,3,4]);
set(gca, 'FontSize', 20, 'fontName','Times'); set(gca, 'FontSize', 20, 'fontName','Times');
saveas(gcf,fullfile(fig_dir, 'mask_gauss.pdf')); saveas(gcf,fullfile(fig_dir, 'mask_gauss.pngcd sc'));
%% %%
\ No newline at end of file
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