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Commit 7b1250ec authored by Marina Kreme's avatar Marina Kreme
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matlab/tfgm/illustrations_gaborTools/illustration_cuicui_eigenvalues.m deleted 100644 → 0
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− 144
View file @ 566d7f54
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
matlab/tfgm/illustrations_gaborTools/rank_asfunctionof_masksize.m deleted 100644 → 0
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− 100
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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'));
matlab/tfgm/illustrations_gaborTools/rank_estimation_halko_vs_eigs_gausswin.m deleted 100644 → 0
+ 0
− 99
View file @ 566d7f54
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'));
matlab/tfgm/illustrations_gaborTools/run_illustration_cuicui_eigenvalues.m deleted 100644 → 0
+ 0
− 14
View file @ 566d7f54
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 );
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