Skip to content
Snippets Groups Projects
Commit 54baef77 authored by Marina Kreme's avatar Marina Kreme
Browse files

load mask

parent 46411446
No related branches found
No related tags found
No related merge requests found
Pipeline #6048 passed
Stage: test
Show whitespace changes
Inline Side-by-side
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
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment