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matlab/tfgm/tf_fading/compute_decomposition.m

@@ -9,8 +9,8 @@ function [t_arrf,t_evdn, t_ut_x, rank_q, s_vec_list, u_mat_list,...,
 %
 % Inputs:
 %     - x_mix: mixtures of signals with wideband and localized spectrograms
-%     -mask_list: list of mask
-%     -gabmul_list: list of Gabor multiplier associated to each mask
+%     -mask_list: cell of time -frequency mask
+%     -gabmul_list: cell of Gabor multiplier associated to each mask
 %     - tolerance_arrf: tolearance. see [1]
 %     - proba_arrf: probability of success. see[1]
 % Outputs:
@@ -34,11 +34,9 @@ function [t_arrf,t_evdn, t_ut_x, rank_q, s_vec_list, u_mat_list,...,
 
 sig_len = length(x_mix);
 
-
 n_areas = length(mask_list);
 r =  compute_r(sig_len, sig_len, proba_arrf);
 
-
 %%
 
 t_arrf = zeros(n_areas,1);

matlab/tfgm/tf_fading/compute_decomposition_fixed_rank.m

@@ -8,8 +8,8 @@ function  [t_rrf,t_evdn, t_ut_x, s_vec_list, u_mat_list,...,
 %
 % Inputs:
 %     - x_mix: mixtures of signals with wideband and localized spectrograms
-%     -mask_list: list of mask
-%     -gabmul_list: list of Gabor multiplier associated to each mask
+%     -mask_list: cell of time-frequency mask
+%     -gabmul_list: cell of Gabor multiplier associated to each mask
 %     - rank : matrix rank see [1]
 % Outputs:
 %     -t_rrf:  time for randomized range finder(rrf)

matlab/tfgm/tf_fading/compute_estimate.m

@@ -35,7 +35,7 @@ end
 
 x = x_mix;
 for  k= 1:n_areas
-    gamma_vec = lambda_coef(k)*s_vec_list{k}./(1-(1-lambda_coef(k))*s_vec_list{k});
+    gamma_vec = lambda_coef(k)*s_vec_list{k}./(1-(1-lambda_coef(k)).*s_vec_list{k});
     x =x-u_mat_list{k}*(gamma_vec.*ut_x_list{k});
 end
 

matlab/tfgm/tf_fading/compute_lambda.m

@@ -2,9 +2,11 @@ function [lambda_est, t_est] = compute_lambda(x_mix, mask, dgt_params,...,
     signal_params,  dgt, s_vec_list, u_mat_list, ut_x_list,...,
     gabmul_list, fig_dir, e_target)
 
-%% [lambda_est, t_est] = compute_lambda(x_mix, mask, dgt_params, signal_params,  dgt, s_vec_list, u_mat_list, ut_x_list,gabmul_list, fig_dir, e_target)
-
-% This function calculates lambda in algorithmes proposed in [1]
+%% [lambda_est, t_est] = compute_lambda(x_mix, mask, dgt_params,...,
+%    signal_params,  dgt, s_vec_list, u_mat_list, ut_x_list,...,
+%    gabmul_list, fig_dir, e_target)
+% This function  estimates the hyperparameters $\lambda$ from target energy
+% in each sub-region as we proposed in [1].
 %
 % Inputs:
 %     -x_mix:mixtures of signals with wideband and localized spectrograms

matlab/tfgm/tf_fading/compute_lambda_oracle_sdr.m

@@ -2,6 +2,10 @@ function [lambda_oracle, t_oracle] = compute_lambda_oracle_sdr(n_areas, x_wb, x_
 
 %% [lambda_oracle, t_oracle] = compute_lambda_oracle_sdr(n_areas,x_wb, x_rec)
 % Function that calculates the estimated lambda using the true energy. see[1]
+% Compute oracle value for hyperparameter $\lambda_k$ from true solution.
+%
+% If only one region is considered, *fmincon* is used.
+% If multiple sub-regions are considered *fminbnd* is used
 %
 % Inputs:
 %     - n_areas: number of regions in mask

matlab/tfgm/tf_fading/estimate_energy_in_mask.m

 function estimated_energy = estimate_energy_in_mask(x_mix, mask, dgt_params, signal_params, dgt,fig_dir)
 
 %%estimated_energy = estimate_energy_in_mask(x_mix, mask, dgt_params, signal_params, dgt,fig_dir)
-% Functions that estimates energy in the mask
+% Functions that estimates energy in time-frequency mask
 %
 % Inputs:
 %     - x_mix:mixtures of signals with wideband and localized spectrograms
@@ -11,7 +11,7 @@ function estimated_energy = estimate_energy_in_mask(x_mix, mask, dgt_params, sig
 %     - dgt: Gabor transform operator
 %     - fig_dir: firgures directory
 % Outputs:
-%     -estimated_energy:estimated  energy
+%     -estimated_energy: Estimated energy in each sub-region.
 %
 %
 % Author: Marina KREME
@@ -54,7 +54,7 @@ figure;
 plotdgtreal(sqrt(e_mat), dgt_params.hop, dgt_params.nbins, fs, 'clim', clim);
 %title(['Mask filled with average energy (total: ', num2str(estimated_energy,8),')']);
 set(gca, 'FontSize', 20, 'fontName','Times');
-saveas(gcf,fullfile(fig_dir, 'filled_mask.pdf'));
+saveas(gcf,fullfile(fig_dir, 'mask_filled__with_energy.pdf'));
 
 
 %
@@ -64,7 +64,7 @@ figure;
 plotdgtreal(x_tf_mat, dgt_params.hop, dgt_params.nbins, fs, 'clim', clim);
 %title(['Mix filled with average energy (total: ', num2str(estimated_energy,8),')']);
 set(gca, 'FontSize', 20, 'fontName','Times');
-saveas(gcf,fullfile(fig_dir, 'filled_mask.pdf'));
+saveas(gcf,fullfile(fig_dir, 'filled_mix_with_average_energy.pdf'));
 
 
 %
@@ -75,7 +75,7 @@ ylabel('Average energy')
 grid on;
 title('Average energy per frequency bin in mix');
 set(gca, 'FontSize', 20, 'fontName','Times');
-saveas(gcf,fullfile(fig_dir, 'average_energy.pdf'));
+saveas(gcf,fullfile(fig_dir, 'average_energy_per_freq_bin.pdf'));
 
 
 %%
@@ -89,7 +89,7 @@ title('Average energy per frequency bin in mix')
 legend('Before filling', 'After filling')
 grid on;
 set(gca, 'FontSize', 20, 'fontName','Times');
-saveas(gcf,fullfile(fig_dir, 'average_energy_check.pdf'));
+saveas(gcf,fullfile(fig_dir, 'average_energy_per_freq_bin_mix.pdf'));
 
 
 end

matlab/tfgm/tf_fading/obj_fun.m

 function f_lambda = obj_fun(lambda_coef, x_mix, s_vec_list, ...,
     u_mat_list, ut_x_list, mask, gab_mul, dgt, e_target)
 
-%% f_lambda = obj_fun(lambda_coef, x_mix, s_vec_list,u_mat_list, ut_x_list, mask, gab_mul, dgt, e_target)
+%%  f_lambda = obj_fun(lambda_coef, x_mix, s_vec_list, ...,
+%    u_mat_list, ut_x_list, mask, gab_mul, dgt, e_target)
+%
 % Objecitve function for finding optimal lambda value in algorithms
 % proppsed in [1]
 %