THE AUDITORY MODELING TOOLBOX
Go to function
EXP_REIJNIERS2014
Experiments of Reijniers et al. (2014)
Program code:
function varargout = exp_reijniers2014(varargin)
%EXP_REIJNIERS2014 Experiments of Reijniers et al. (2014)
% Usage: [] = exp_reijniers2014(flag)
%
% EXP_REIJNIERS2014(flag) reproduces figures of the study from
% Reijniers et al. (2014). Note that HRTFs from the ARI database
% are used for Fig. 4-6, which is most probably different to the HRTFs
% used for the calculations in the paper. For Fig. 2 and 3 HRTFs from the
% Symare database are used, which are also most probably non original.
%
%
% The following flags can be specified
%
% 'fig2a' Reproduce Fig. 2 (a):
% The results for simulated trial of a locali-
% zation experiment for subject 1 [see also Fig. 3(a)]
% in front Theta = (-34,45)deg (a) is shown. The
% figures show the simulated posterior angular
% probabilities and the templates corresponding to the
% original direction Theta (solid black line) and
% estimated direction Theta_est (dotted black line).
% The simulated auditory input is shown as the solid
% red line.
%
% 'fig2b' Reproduce Fig. 2 (b):
% The results for simulated trial of a locali-
% zation experiment for subject 1 [see also Fig. 3(a)]
% in back Theta = (14,171)deg (b) is shown. The figures
% show the simulated posterior angular probabilities
% and the templates corresponding to the original
% direction Theta (solid black line) and estimated
% direction Theta_est (dotted black line). The
% simulated auditory input is shown as the solid red
% line.
%
% 'fig3' Reproduce Fig. 3:
% The simulated mean spherical error as function
% of the source position for 3 different subjects. The
% average was taken over 500 localization trials for
% each source position.
%
% 'fig4' Reproduce Fig. 4(a):
% The mean localization performance when the
% simulation results are averaged over 100 subjects.
% Superimposed arrows indicate the size and direction
% of local population response biases for different
% source positions.
%
% 'fig5' Reproduce Fig. 5:
% Sensitivity analysis of the Bayesian localization
% model. The simulated mean spherical error is shown
% as function of the source position, when each of the
% model parameters is varied separately. The average
% was taken over 500 localization trials for each
% source position and 100 subjects were pooled.
% (a) The model with input parameters as described in
% the main text. The standard deviation is doubled,
% respectively for (b) the noise on the ITD, (c) the
% internal noise and the variation on (d) the source
% spectrum. In (e), the bandwidth of the source is
% reduced to [300Hz-8kHz].
%
% 'fig6' Reproduce Fig. 6:
% The mean spherical error for different values of the
% SNR. SNR=75dB corresponds to the control situation,
% see Fig.5(a), as the magnitude in all frequency
% channels is above the system noise level.
%
% 'tab1_barumerli2020aes' Reproduce Tab. 1:
% Comparison between actual (majdak2010) and simulated,
% performances by relying on different perceptual
% metrics (middlebrooks1999b). The variable 'multiplier'
% allows to tune the internal noise.
%
% 'fig2_barumerli2020forum' Reproduce Fig.2 of Barumerli et al. (2020):
% comparison between virtual estimations
% and real data (see
% data_middlebrooks1999()) for individual
% and non-individual DTFs
%
% 'fig3_barumerli2020forum' Reproduce Fig.3 of Barumerli et al. (2020):
% estimations' evaluation with band limited spectra.
% See Best et al. 2005 and Fig 11 baumgartner2014
%
% 'fig4_barumerli2020forum' Reproduce Fig.4 of Barumerli et al. (2020):
% estimations' evaluation with rippled
% spectra. See Macpherson and Middlebrooks 2003
% and Fig 10 baumgartner2014
%
% Further, cache flags (see amt_cache) and plot flags can be specified
% (Warning: Enforcing recalculation of cached data might take several
% hours).
% 'no_plot' Do not compute plots. Flag for cluster execution.
%
% 'interp' Plot scattered interpolated data (default).
%
% 'scatter' Plot only discrete scattered data instead of inter-
% polated scattered data.
%
% 'redo_fast' Quickly recalculate data for figures without using
% cache. To speed up computation the amount of locali-
% zation trials for Fig. 3 is reduced to 20 and the
% amount of subjects for Fig. 4-6 is reduced to 12.
%
% Requirements:
% -------------
%
% 1) SOFA Toolbox v2.x or higher from
% http://sourceforge.net/projects/sofacoustics for Matlab
%
% Examples:
% ---------
%
% To display right part of Fig. 2 (a) use :
%
% exp_reijniers2014('fig2a');
%
% To display Fig. 3 and do a quick recalculation use :
%
% exp_reijniers2014('fig3','redo_fast');
%
%
%
% See also: reijniers2014 plot_reijniers2014 reijniers2014_featureextraction
%
% References:
% R. Barumerli, P. Majdak, R. Baumgartner, J. Reijniers, M. Geronazzo,
% and F. Avanzini. Predicting directional sound-localization of human
% listeners in both horizontal and vertical dimensions. In Audio
% Engineering Society Convention 148. Audio Engineering Society, 2020.
%
% R. Barumerli, P. Majdak, R. Baumgartner, M. Geronazzo, and F. Avanzini.
% Evaluation of a human sound localization model based on bayesian
% inference. In Forum Acusticum, 2020.
%
% J. Reijniers, D. Vanderleist, C. Jin, C. S., and H. Peremans. An
% ideal-observer model of human sound localization. Biological
% Cybernetics, 108:169--181, 2014.
%
%
% Url: http://amtoolbox.org/amt-1.6.0/doc/experiments/exp_reijniers2014.php
% This file is licensed unter the GNU General Public License (GPL) either
% version 3 of the license, or any later version as published by the Free Software
% Foundation. Details of the GPLv3 can be found in the AMT directory "licences" and
% at <https://www.gnu.org/licenses/gpl-3.0.html>.
% You can redistribute this file and/or modify it under the terms of the GPLv3.
% This file is distributed without any warranty; without even the implied warranty
% of merchantability or fitness for a particular purpose.
% #Author: Jonas Reijniers (201X): original implementation
% #Author: Roberto Barumerli (2019): Modified and adapted for the AMT
% #Author: Michael Sattler (2019): Modified and adapted for the AMT
% #Author: Clara Hollomey (2021): Modified and adapted for AMT 1.0
%% ------ Check input options ---------------------------------------------
definput.import = {'amt_cache'};
definput.keyvals.MarkerSize = 6;
definput.keyvals.FontSize = 12;
definput.flags.type = {'missingflag', 'fig2a','fig2b', ...
'fig3','fig4','fig5','fig6', ...
'tab1_barumerli2020aes', ...
'fig2_barumerli2020forum', ...
'fig3_barumerli2020forum',...
'fig4_barumerli2020forum'};
definput.flags.plot = {'plot', 'no_plot'};
definput.flags.plot_type = {'interp','scatter'};
definput.flags.redo = {'no_redo_fast','redo_fast'};
[flags,kv] = ltfatarghelper({},definput,varargin);
if flags.do_missingflag
flagnames=[sprintf('%s, ',definput.flags.type{2:end-2}),...
sprintf('%s or %s',definput.flags.type{end-1},...
definput.flags.type{end})];
error('%s: You must specify one of the following flags: %s.', ...
upper(mfilename),flagnames);
end
%% ------ fig2a -----------------------------------------------------------
if flags.do_fig2a
if flags.do_redo_fast || flags.do_redo
num_exp = 1;
fig2a = [];
else
num_exp = 1; % definedin case of flag 'redo'
fig2a = amt_cache('get','fig2a',flags.cachemode);
end
if isempty(fig2a)
% Load SOFA file
SOFA_obj=amt_load('reijniers2014','HA01.sofa');
% Get index of closest directions to Theta shown in Fig. 2(a)
az = -34;
el = 45;
% Preprocessing source information for both directions
[template, target] = reijniers2014_featureextraction(SOFA_obj, ...
'targ_az', az, 'targ_el', el);
[fig2a.doa, fig2a.params] = ...
reijniers2014(template, target, 'num_exp', num_exp);
amt_cache('set','fig2a',fig2a);
end
if flags.do_plot
% plot left side of Fig. 2 (a)
plot_reijniers2014(fig2a.params.template_coords, ...
squeeze(fig2a.params.post_prob), ...
'target', fig2a.doa.real, ...
flags.plot_type,flags.type);
title('Fig. 2(a), posterior probability density');
% plot right side of fig 2(a)
figure('NumberTitle', 'off', 'Name', 'Fig. 2 (a) templates');
set(gcf, 'Position', [100,150, 1100, 550]);
s1 = subplot(1,3,1);
set(s1, 'position', [0.07, 0.11, 0.05, 0.8] );
plot(0, fig2a.params.T_target(fig2a.params.Tidx.itd),'k.', ...
0, fig2a.params.T_template(fig2a.params.Tidx.itd),'b.', ...
0, fig2a.params.X(fig2a.params.Tidx.itd),'r.','MarkerSize',20);
set(gca,'Color',[0.99,0.95,0.93]);
ylim([-10,10]);
xlim([-0.2,0.2]);
ax1 = gca;
title('ITD','FontSize', 14);
ylabel('itd (jnd)','FontSize', 13);
set(ax1,'xticklabel',[]);
s2 = subplot(1,3,2);
set(s2, 'position', [0.19, 0.11, 0.35, 0.8] );
plot(fig2a.params.freq_channels,...
fig2a.params.T_template(fig2a.params.est_idx, fig2a.params.Tidx.Hm),'k', ...
fig2a.params.freq_channels,fig2a.params.T_target(fig2a.params.Tidx.Hm),'k:', ...
fig2a.params.freq_channels,squeeze(fig2a.params.X(1,1,fig2a.params.Tidx.Hm)),'r', ...
fig2a.params.freq_channels,squeeze(fig2a.params.X(1,1,fig2a.params.Tidx.Hm)),'r.', ...
'MarkerSize',20,'LineWidth',1.5);
legend({'$T_{-}(\Theta)$','$T_{-}(\widehat{\Theta})$', ...
'$X_{-}(\Theta)$'},'Interpreter','latex','FontSize',13);
title('spectral difference','FontSize', 14);
ylabel('logmagnitude (dB)','FontSize',13);
xlabel('log_{10}(f) (Hz)','Interpreter','tex','FontSize',13);
ylim([-20,30]);
xlim([min(fig2a.params.freq_channels) max(fig2a.params.freq_channels)]);
set(gca,'Xscale','log')
s3 = subplot(1,3,3);
set(s3, 'position', [0.61, 0.11, 0.35, 0.8] );
plot(fig2a.params.freq_channels,...
fig2a.params.T_template(fig2a.params.est_idx, fig2a.params.Tidx.Hp),'k', ...
fig2a.params.freq_channels,fig2a.params.T_target(fig2a.params.Tidx.Hp),'k:', ...
fig2a.params.freq_channels,squeeze(fig2a.params.X(1,1,fig2a.params.Tidx.Hp)),'r', ...
fig2a.params.freq_channels,squeeze(fig2a.params.X(1,1,fig2a.params.Tidx.Hp)),'r.', ...
'MarkerSize',20,'LineWidth',1.5);
legend({'$T_{+}(\Theta)$','$T_{+}(\widehat{\Theta})$', ...
'$X_{+}(\Theta)$'},'Interpreter','latex','FontSize',13);
title('spectral sum','FontSize', 14);
ylabel('logmagnitude (dB)','FontSize',13);
xlabel('log_{10}(f) (Hz)','Interpreter','tex','FontSize',13);
ylim([-20,30]);
xlim([min(fig2a.params.freq_channels) max(fig2a.params.freq_channels)]);
set(gca,'Xscale','log');
end
end
%% ------ fig2b -----------------------------------------------------------
if flags.do_fig2b
if flags.do_redo_fast || flags.do_redo
num_exp = 1;
fig2b = [];
else
num_exp = 1;
fig2b = amt_cache('get','fig2b',flags.cachemode);
end
if isempty(fig2b)
% Load SOFA file
SOFA_obj=amt_load('reijniers2014','HA01.sofa');
% Get index of closest directions to Theta shown in Fig. 2(a)
az = 14;
el = 171;
% Preprocessing source information for both directions
[template, target] = reijniers2014_featureextraction(SOFA_obj, ...
'targ_az', az, 'targ_el', el);
% Model
[fig2b.doa, fig2b.params] = ...
reijniers2014(template, target, 'num_exp', num_exp);
amt_cache('set','fig2b',fig2b);
end
if ~flags.do_no_plot
% plot left side of Fig. 2b
plot_reijniers2014(fig2b.params.template_coords, ...
squeeze(fig2b.params.post_prob), ...
'target', fig2b.doa.real, ...
flags.plot_type,flags.type);
title('Fig. 2(b), posterior probability density');
% plot right side of fig 2(b)
figure('NumberTitle', 'off', 'Name', 'Fig. 2 (b) templates');
set(gcf, 'Position', [100,150, 1100, 550]);
s1 = subplot(1,3,1);
set(s1, 'position', [0.07, 0.11, 0.05, 0.8] );
plot(0, fig2b.params.T_target(fig2b.params.Tidx.itd),'k.', ...
0, fig2b.params.T_template(fig2b.params.Tidx.itd),'b.', ...
0, fig2b.params.X(fig2b.params.Tidx.itd),'r.','MarkerSize',20);
set(gca,'Color',[0.99,0.95,0.93]);
ylim([-10,10]);
xlim([-0.2,0.2]);
ax1 = gca;
title('ITD','FontSize', 14);
ylabel('itd (jnd)','FontSize', 13);
set(ax1,'xticklabel',[]);
s2 = subplot(1,3,2);
set(s2, 'position', [0.19, 0.11, 0.35, 0.8] );
plot(fig2b.params.freq_channels,...
fig2b.params.T_template(fig2b.params.est_idx, fig2b.params.Tidx.Hm),'k', ...
fig2b.params.freq_channels,fig2b.params.T_target(fig2b.params.Tidx.Hm),'k:', ...
fig2b.params.freq_channels,squeeze(fig2b.params.X(1,1,fig2b.params.Tidx.Hm)),'r', ...
fig2b.params.freq_channels,squeeze(fig2b.params.X(1,1,fig2b.params.Tidx.Hm)),'r.', ...
'MarkerSize',20,'LineWidth',1.5);
legend({'$T_{-}(\Theta)$','$T_{-}(\widehat{\Theta})$',...
'$X_{-}(\Theta)$'},'Interpreter','latex','FontSize',13);
title('spectral difference','FontSize', 14);
ylabel('logmagnitude (dB)','FontSize',13);
xlabel('log_{10}(f) (Hz)','Interpreter','tex','FontSize',13);
ylim([-20,30]);
xlim([min(fig2b.params.freq_channels) max(fig2b.params.freq_channels)]);
set(gca,'Xscale','log');
s3 = subplot(1,3,3);
set(s3, 'position', [0.61, 0.11, 0.35, 0.8] );
plot(fig2b.params.freq_channels,...
fig2b.params.T_template(fig2b.params.est_idx, fig2b.params.Tidx.Hp),'k', ...
fig2b.params.freq_channels,fig2b.params.T_target(fig2b.params.Tidx.Hp),'k:', ...
fig2b.params.freq_channels,squeeze(fig2b.params.X(1,1,fig2b.params.Tidx.Hp)),'r', ...
fig2b.params.freq_channels,squeeze(fig2b.params.X(1,1,fig2b.params.Tidx.Hp)),'r.', ...
'MarkerSize',20,'LineWidth',1.5);
legend({'$T_{+}(\Theta)$','$T_{+}(\widehat{\Theta})$', ...
'$X_{+}(\Theta)$'},'Interpreter','latex','FontSize',13);
title('spectral sum','FontSize', 14);
ylabel('logmagnitude (dB)','FontSize',13);
xlabel('log_{10}(f) (Hz)','Interpreter','tex','FontSize',13);
ylim([-20,30]);
xlim([min(fig2b.params.freq_channels) max(fig2b.params.freq_channels)]);
set(gca,'Xscale','log');
end
end
%% ------ fig3 ------------------------------------------------------------
%if flags.do_fig3
% if flags.do_redo_fast
% num_exp = 20;
% fig3 = [];
% else
% num_exp = 500;
% fig3 = amt_cache('get','fig3',flags.cachemode);
% end
%
% if isempty(fig3)
% filenames={'HA03.sofa'; 'HA01.sofa';'HA06.sofa'};
% for i = 1:length(filenames)
% path = ['db://reijniers2014/',filenames{i}];
% SOFA_obj(i)=amt_load('reijniers2014',filenames{i});
% end
% % Preallocation
% fig3 = struct('metrics', struct([]), 'err',struct([]), 'doa_real',struct([]));
% fig3 = repmat(fig3,length(filenames),1);
%
% amt_disp('Processing three subjects in parallel...');
%
% for i = 1:length(filenames)
% amt_disp(['Processing subject #' num2str(i)]);
% % Get directions from SOFA files
% [template, target] = reijniers2014_featureextraction(SOFA_obj(i));
% doa = reijniers2014(template, target,'num_exp',num_exp);
%
% fig3(i).err = reijniers2014_metrics(doa);
% fig3(i).doa_real = doa.real;
% fig3(i).metrics = reijniers2014_metrics(doa, 'middle_metrics');
% end
%
% if ~flags.do_redo_fast
% amt_cache('set','fig3',fig3);
% end
% end
% if flags.do_plot
% % plot for each subject
% for i = 1:length(fig3)
% [~, cbar] = plot_reijniers2014(fig3(i).doa_real, ...
% fig3(i).err, ...
% flags.plot_type, flags.type);
% title(sprintf('Fig. 3(a), Subject %i', i));
% caxis([0,20]);
% cbar.Label.String = 'Mean spherical error [^\circ]';
% cbar.Label.FontSize = 12;
% ct1=get(cbar,'TickLabels');
% for k=1:numel(ct1)
% ct1{k}=sprintf('%s',ct1{k});
% end
% set(cbar,'xticklabel',ct1);
% end
% end
%
% for i = 1:length(fig3)
% amt_disp(sprintf('\nSUBJECT %i', i),'documentation');
% amt_disp(sprintf('\t\t\tSIM'),'documentation');
% amt_disp(sprintf('lateral_bias [deg]:\t%0.2f', ...
% mean([fig3(i).metrics.accL])),'documentation');
% amt_disp(sprintf('lateral_rms_error [deg]:\t%0.2f', ...
% mean([fig3(i).metrics.rmsL])),'documentation');
% amt_disp(sprintf('elevation_bias [deg]:\t%0.2f', ...
% mean([fig3(i).metrics.accE])),'documentation');
% amt_disp(sprintf('local_rms_polar [deg]:\t%0.2f', ...
% mean([fig3(i).metrics.rmsP])),'documentation');
% amt_disp(sprintf('quadrant_err [%%]:\t%0.2f', ...
% mean([fig3(i).metrics.querr])),'documentation');
% end
%end
%% ------ fig4 ------------------------------------------------------------
if flags.do_fig4
if flags.do_redo_fast
num_exp = 20;
num_sub = 12;
fig4 = [];
else
num_exp = 500;
num_sub = 100;
fig4 = amt_cache('get','fig4',flags.cachemode);
end
if isempty(fig4)
offset = 13; % start at 14th file in folder
% Get names of all used hrtfs
x=amt_load('reijniers2014','hrtfnames.mat');
% Load SOFA files
for i=1:num_sub
SOFA_obj(i)=amt_load('reijniers2014',x.hrtfnames{i+offset});
end
% Preallocation
fig4 = struct('exp',struct(), ...
'err',struct([]), ...
'bias',struct([]), ...
'doa_real',struct([]));
fig4 = repmat(fig4,num_sub,1);
% Compute mean spherical error for all subjects
amt_disp(['Processing ' num2str(num_sub) ...
' subjects in parallel...']);
for i = 1:num_sub
amt_disp(['Processing subject #' num2str(i)]);
% Get directions from SOFA files
[template, target] = reijniers2014_featureextraction(SOFA_obj(i));
doa = reijniers2014(template, target,'num_exp',num_exp);
[fig4(i).err, fig4(i).bias] = reijniers2014_metrics(doa);
fig4(i).doa_real = doa.real;
fig4(i).exp = reijniers2014_metrics(doa, 'middle_metrics');
end
if ~flags.do_redo_fast
amt_cache('set','fig4',fig4);
end
end
% remove ill formed hrtf
if(length(fig4(51).err) ~= length(fig4(1).err))
fig4(51) = [];
end
if flags.do_plot
% Calculate averages
mean_error = zeros(size(fig4(1).doa_real, 1), 1);
mean_bias = zeros(size(fig4(1).doa_real, 1), 3);
for k = 1:length(fig4)
mean_error = mean_error + fig4(k).err;
mean_bias = mean_bias + fig4(k).bias;
end
mean_error = abs(mean_error/length(fig4));
mean_bias = mean_bias/length(fig4);
[~, cbar] = plot_reijniers2014(fig4(1).doa_real, ... % assuming same dirs
mean_error,'bias', ...
mean_bias,flags.plot_type,flags.type);
title('Fig. 4(a), simulation');
caxis([0,35]);
cbar.Label.String = 'Mean spherical error [^\circ]';
cbar.Label.FontSize = 12;
ct1=get(cbar,'TickLabels');
for k=1:numel(ct1)
ct1{k}=sprintf('%s',ct1{k});
end
set(cbar,'xticklabel',ct1);
end
metrics = struct2cell(fig4);
metrics = cell2mat(metrics(1,:));
amt_disp(sprintf('\t\t\tSIM'))
amt_disp(sprintf('lateral_bias [dg]:\t%0.2f', ...
mean([metrics.accL], 'all')),'documentation');
amt_disp(sprintf('lateral_rms_error [deg]:\t%0.2f', ...
mean([metrics.rmsL], 'all')),'documentation');
amt_disp(sprintf('elevation_bias [deg]:\t%0.2f', ...
mean([metrics.accE], 'all')),'documentation');
amt_disp(sprintf('local_rms_polar [deg]:\t%0.2f', ...
mean([metrics.rmsP], 'all')),'documentation');
amt_disp(sprintf('quadrant_err [%%]:\t%0.2f', ...
mean([metrics.querr], 'all')),'documentation');
end
%% ------ fig5 ------------------------------------------------------------
if flags.do_fig5
if flags.do_redo_fast
num_exp = 20;
num_sub = 12;
fig5 = [];
else
num_exp = 500;
num_sub = 100;
fig5 = amt_cache('get','fig5',flags.cachemode);
end
if isempty(fig5)
offset = 13; % start at 14th file in folder
% Get names of all used hrtfs
x=amt_load('reijniers2014','hrtfnames.mat');
% Load SOFA files
for i=1:num_sub
SOFA_obj(i)=amt_load('reijniers2014',x.hrtfnames{i+offset});
end
% preallocation
fig5 = struct('a',struct('err',[], 'exp', struct([])), ...
'b',struct('err',[], 'exp', struct([])), ...
'c',struct('err',[], 'exp', struct([])), ...
'd',struct('err',[], 'exp', struct([])), ...
'e',struct('err',[], 'exp', struct([])), ...
'doa_real', []);
fig5 = repmat(fig5,num_sub,1);
amt_disp(['Processing ' num2str(num_sub) ...
' subjects in parallel...']);
% Compute mean sphercial error for all subjects
for i = 1:num_sub
%% Preprocessing source and template information
amt_disp(['Processing subject #' num2str(i)]);
[template, target] = reijniers2014_featureextraction(SOFA_obj(i));
% Model
% Fig. 5 (a)
amt_disp('Doing control');
doa = reijniers2014(template, target, ...
'num_exp',num_exp);
fig5(i).a.err = reijniers2014_metrics(doa);
fig5(i).a.exp = reijniers2014_metrics(doa, 'middle_metrics');
% Fig. 5 (b)
amt_disp('Doing sigma_itd doubled');
doa = reijniers2014(template, target, ...
'sig_itd',0.569*2, 'num_exp',num_exp);
fig5(i).b.err = reijniers2014_metrics(doa);
fig5(i).b.exp = reijniers2014_metrics(doa, 'middle_metrics');
% Fig. 5 (c)
amt_disp('Doing sigma_I doubled');
doa = reijniers2014(template, target, ...
'sig_I',3.5*2,'num_exp',num_exp);
fig5(i).c.err = reijniers2014_metrics(doa);
fig5(i).c.exp = reijniers2014_metrics(doa, 'middle_metrics');
% Fig. 5 (d)
amt_disp('Doing sigma_S doubled');
doa = reijniers2014(template, target, ...
'sig_S',3.5*2,'num_exp',num_exp);
fig5(i).d.err = reijniers2014_metrics(doa);
fig5(i).d.exp = reijniers2014_metrics(doa, 'middle_metrics');
% Fig. 5 (e)
amt_disp('Doing LPF');
[templpf, targlpf] = reijniers2014_featureextraction(SOFA_obj(i), ...
'fb_low', 3e2, 'fb_high', 8e3);
doa = reijniers2014(templpf, targlpf, ...
'num_exp',num_exp);
fig5(i).e.err = reijniers2014_metrics(doa);
fig5(i).e.exp = reijniers2014_metrics(doa, 'middle_metrics');
fig5(i).doa_real = doa.real;
end
if ~flags.do_redo_fast
amt_cache('set','fig5',fig5);
end
end
graphs_lb = {'a', 'b', 'c', 'd', 'e'};
if flags.do_plot
% remove anomalous subject
fig5(51) = [];
num_sub = length(fig5);
% Calculate averages and plot
mean_error = zeros(length(fig5(1).a.err), length(graphs_lb));
titles = {'Fig. 5 (a), control', ...
'Fig. 5 (b), $2\sigma_{itd}$', ...
'Fig. 5 (c), $2\sigma_{I}$',...
'Fig. 5 (d), $2\sigma_{S}$',...
'Fig. 5 (e), LPF'};
for j =1:length(graphs_lb)
for k = 1:num_sub
mean_error(:,j) = mean_error(:,j) + fig5(k).(graphs_lb{j}).err;
end
mean_error(:,j) = abs((mean_error(:,j)/num_sub));
[~, cbar] = plot_reijniers2014(fig5(1).doa_real, ...
mean_error(:,j),flags.plot_type,flags.type);
title(titles{j},'Interpreter','Latex');
caxis([0,20]);
cbar.Label.String = 'Mean spherical error [^\circ]';
cbar.Label.FontSize = 12;
ct1=get(cbar,'TickLabels');
for k=1:numel(ct1)
ct1{k}=sprintf('%s',ct1{k});
end
set(cbar,'xticklabel',ct1);
end
end
accL = zeros(length(fig5(1).a.err), length(graphs_lb));
rmsL = zeros(length(fig5(1).a.err), length(graphs_lb));
accE = zeros(length(fig5(1).a.err), length(graphs_lb));
rmsP = zeros(length(fig5(1).a.err), length(graphs_lb));
querr = zeros(length(fig5(1).a.err), length(graphs_lb));
for j =1:length(graphs_lb)
for k = 1:num_sub
accL(k,j) = accL(k,j) + fig5(k).(graphs_lb{j}).exp.accL;
rmsL(k,j) = rmsL(k,j) + fig5(k).(graphs_lb{j}).exp.rmsL;
accE(k,j) = accE(k,j) + fig5(k).(graphs_lb{j}).exp.accE;
rmsP(k,j) = rmsP(k,j) + fig5(k).(graphs_lb{j}).exp.rmsP;
querr(k,j) = querr(k,j) + fig5(k).(graphs_lb{j}).exp.querr;
end
amt_disp(sprintf('\n EXP %s', graphs_lb{j}),'documentation');
amt_disp(sprintf('\t\t\tSIM'),'documentation');
amt_disp(sprintf('lateral_bias [deg]:\t%0.3f', ...
mean(accL(:,j), 'all')),'documentation');
amt_disp(sprintf('lateral_rms_error [deg]:\t%0.3f', ...
mean(rmsL(:,j), 'all')),'documentation');
amt_disp(sprintf('elevation_bias [deg]:\t%0.3f', ...
mean(accE(:,j), 'all')),'documentation');
amt_disp(sprintf('local_rms_polar [deg]:\t%0.3f', ...
mean(rmsP(:,j), 'all')),'documentation');
amt_disp(sprintf('quadrant_err [%%]:\t%0.3f', ...
mean(querr(:,j), 'all')),'documentation');
end
end
%% ------ fig6 ------------------------------------------------------------
if flags.do_fig6
if flags.do_redo_fast
num_exp = 20;
num_sub = 12;
fig6 = [];
else
num_exp = 500;
num_sub = 100;
fig6 = amt_cache('get','fig6',flags.cachemode);
end
SNRs = [75, 50, 40, 30];
graphs_lb = {'a', 'b', 'c', 'd'};
if isempty(fig6)
offset = 13; % start at 14th file in folder
% Get names of all used hrtfs
x=amt_load('reijniers2014','hrtfnames.mat');
% Load SOFA files
for i=1:num_sub
SOFA_obj(i)=amt_load('reijniers2014',x.hrtfnames{i+offset});
end
% preallocation
fig6 = struct('a',struct('err',[], 'exp', struct([])), ...
'b',struct('err',[], 'exp', struct([])), ...
'c',struct('err',[], 'exp', struct([])), ...
'd',struct('err',[], 'exp', struct([])), ...
'doa_real', []);
fig6 = repmat(fig6,num_sub,1);
% Compute mean sphercial error for all subjects
amt_disp(['Processing ' num2str(num_sub) ...
' subjects in parallel...']);
for i = 1:num_sub
amt_disp(['Processing subject #' num2str(i)]);
[template, target] = reijniers2014_featureextraction(SOFA_obj(i));
for j = 1:length(SNRs)
amt_disp(sprintf('Doing SNR=%idB', SNRs(j)));
doa = reijniers2014(template, target, ...
'SNR',SNRs(j),'num_exp',num_exp);
fig6(i).(graphs_lb{j}).err = reijniers2014_metrics(doa);
fig6(i).(graphs_lb{j}).exp = reijniers2014_metrics(doa, 'middle_metrics');
fig6(i).doa_real = doa.real;
end
end
if ~flags.do_redo_fast
amt_cache('set','fig6',fig6);
end
end
if flags.do_plot
num_sub = length(fig6);
% Calculate averages and plot
mean_error = zeros(length(fig6(1).doa_real), length(graphs_lb));
for j =1:length(graphs_lb)
for k = 1:num_sub
mean_error(:,j) = mean_error(:,j) + fig6(k).(graphs_lb{j}).err;
end
mean_error(:,j) = abs((mean_error(:,j)/num_sub));
[~, cbar] = plot_reijniers2014(fig6(1).doa_real, ...
mean_error(:,j),flags.plot_type,flags.type);
title(sprintf('Fig.6 (%c), SNR=%idB',graphs_lb{j}, SNRs(j)),...
'Interpreter','Latex');
caxis([0,20]);
cbar.Label.String = 'Mean spherical error [^circ]';
cbar.Label.FontSize = 12;
ct1=get(cbar,'TickLabels');
for k=1:numel(ct1)
ct1{k}=sprintf('%s',ct1{k});
end
set(cbar,'xticklabel',ct1);
end
end
accL = zeros(length(fig6(1).a.err), length(graphs_lb));
rmsL = zeros(length(fig6(1).a.err), length(graphs_lb));
accE = zeros(length(fig6(1).a.err), length(graphs_lb));
rmsP = zeros(length(fig6(1).a.err), length(graphs_lb));
querr = zeros(length(fig6(1).a.err), length(graphs_lb));
for j =1:length(graphs_lb)
for k = 1:num_sub
accL(:,j) = accL(:,j) + fig6(k).(graphs_lb{j}).exp.accL;
rmsL(:,j) = rmsL(:,j) + fig6(k).(graphs_lb{j}).exp.rmsL;
accE(:,j) = accE(:,j) + fig6(k).(graphs_lb{j}).exp.accE;
rmsP(:,j) = rmsP(:,j) + fig6(k).(graphs_lb{j}).exp.rmsP;
querr(:,j) = querr(:,j) + fig6(k).(graphs_lb{j}).exp.querr;
end
amt_disp(sprintf('\n EXP %s', graphs_lb{j}),'documentation');
amt_disp(sprintf('\t\t\tSIM'),'documentation');
amt_disp(sprintf('lateral_bias [deg]:\t%0.2f', ...
mean([accL], 'all')),'documentation');
amt_disp(sprintf('lateral_rms_error [deg]:\t%0.2f', ...
mean([rmsL], 'all')),'documentation');
amt_disp(sprintf('elevation_bias [deg]:\t%0.2f', ...
mean([accE], 'all')),'documentation');
amt_disp(sprintf('local_rms_polar [deg]:\t%0.2f', ...
mean([rmsP], 'all')),'documentation');
amt_disp(sprintf('quadrant_err [%%]:\t%0.2f', ...
mean([querr], 'all')),'documentation');
end
end
%% ------ tab1_barumerli2020aes ----------------------------------------------
if flags.do_tab1_barumerli2020aes
data_baseline = data_majdak2010('Learn_M');
% remove subjects with no data
data_baseline(1:5) = [];
% uncertainties tuner
multiplier = 3;
amt_disp(sprintf('Uncertanties multiplied by a factor: %i\n', multiplier));
tab1_barumerli2020aes = [];
if ~flags.do_redo
tab1_barumerli2020aes = amt_cache('get','tab1_barumerli2020aes',flags.cachemode);
end
if isempty(tab1_barumerli2020aes)
amt_disp('Loading SOFA files');
for i = 1:length(data_baseline)
data_baseline(i).sofa = amt_load('reijniers2014',['dtf_' lower(data_baseline(i).id) '.sofa']);
end
% Preallocation
tab1_barumerli2020aes = struct('doa', struct([]));
tab1_barumerli2020aes = repmat(tab1_barumerli2020aes,length(data_baseline),1);
for i = 1:length(data_baseline)
amt_disp(['Processing subject #' num2str(i)]);
% Get directions from SOFA files
targ_az = data_baseline(i).mtx(:,1);
targ_el = data_baseline(i).mtx(:,2);
% preprocessing
[template, target] = ...
reijniers2014_featureextraction(data_baseline(i).sofa, ...
'targ_az', targ_az, 'targ_el', targ_el);
% model esecution
[tab1_barumerli2020aes(i).doa] = ...
reijniers2014(template, target, 'num_exp', 1, ...
'sig_itd', 0.569*multiplier, ...
'sig_I', 3.5*multiplier, ...
'sig_S', 3.5*multiplier, ...
'sig', 5*multiplier);
end
amt_cache('set','tab1_barumerli2020aes',tab1_barumerli2020aes);
end
for i = 1:length(tab1_barumerli2020aes)
% lateral_bias
exp(i).accL = reijniers2014_metrics(tab1_barumerli2020aes(i).doa, 'accL');
% lateral_rms_error
exp(i).rmsL = reijniers2014_metrics(tab1_barumerli2020aes(i).doa, 'rmsL');
% elevation_bias
exp(i).accE = reijniers2014_metrics(tab1_barumerli2020aes(i).doa, 'accE');
% local_rms_polar
exp(i).rmsP = ...
reijniers2014_metrics(tab1_barumerli2020aes(i).doa, 'rmsPmedianlocal');
% quadrant_err
exp(i).querr = ...
reijniers2014_metrics(tab1_barumerli2020aes(i).doa, 'querrMiddlebrooks');
real(i).accL = localizationerror(data_baseline(i).mtx, 'accL');
real(i).rmsL = localizationerror(data_baseline(i).mtx, 'rmsL');
real(i).accE = localizationerror(data_baseline(i).mtx, 'accE');
real(i).rmsP = ...
localizationerror(data_baseline(i).mtx, 'rmsPmedianlocal');
real(i).querr = ...
localizationerror(data_baseline(i).mtx, 'querrMiddlebrooks');
end
amt_disp(sprintf('\t\t\tSIM\t\tREAL'),'documentation');
amt_disp(sprintf('lateral_bias [deg]:\t%0.2f\t\t%0.2f', ...
mean([exp.accL]), mean([real.accL])),'documentation');
amt_disp(sprintf('lateral_rms_error [deg]:\t%0.2f\t\t%0.2f', ...
mean([exp.rmsL]), mean([real.rmsL])),'documentation');
amt_disp(sprintf('elevation_bias [deg]:\t%0.2f\t\t%0.2f', ...
mean([exp.accE]), mean([real.accE])),'documentation');
amt_disp(sprintf('local_rms_polar [deg]:\t%0.2f\t\t%0.2f', ...
mean([exp.rmsP]), mean([real.rmsP])),'documentation');
amt_disp(sprintf('quadrant_err [%%]:\t%0.2f\t\t%0.2f', ...
mean([exp.querr]), mean([real.querr])),'documentation');
end
%% ------ fig2_barumerli2020forum -------------------------------------
if flags.do_fig2_barumerli2020forum
fig2_barumerli2020forum = [];
if ~flags.do_redo
fig2_barumerli2020forum = amt_cache('get', ...
'fig2_barumerli2020forum',flags.cachemode);
end
if isempty(fig2_barumerli2020forum)
% load 23 DTFs from baumgartner2014
amt_disp('Loading SOFA files');
sbj_dtf = data_baumgartner2014('pool','cached');
% preprocess templates for each user
amt_disp('Processing subjects'' templates');
for i = 1:length(sbj_dtf)
amt_disp(['Pre-processing subject #' num2str(i)]);
[sbj_template(i), sbj_target(i)] = reijniers2014_featureextraction(sbj_dtf(i).Obj);
end
% preallocation for results
amt_disp('Allocating memory for results');
estimations = struct('doa', struct([]));
estimations = repmat(estimations, ...
length(sbj_dtf),length(sbj_dtf)); % all vs all
for i = 1:length(sbj_dtf)
amt_disp(['Processing subject #' num2str(i)]);
for j = 1:length(sbj_dtf)
amt_disp(num2str(j));
%TODO: select points in the sphere grid
estimations(i, j).doa = ...
reijniers2014(sbj_template(i), sbj_target(j), 'num_exp', 1);
end
end
% compute metrics
for i = 1:size(estimations, 1)
for j = 1:size(estimations, 2)
% lateral bias
metrics(i, j).accL = reijniers2014_metrics(estimations(i, j).doa, 'accL');
% lateral_rms_error
metrics(i, j).rmsL = reijniers2014_metrics(estimations(i, j).doa, 'rmsL');
% elevation_bias
metrics(i, j).accE = reijniers2014_metrics(estimations(i, j).doa, 'accE');
% polar bias
metrics(i, j).accP = ...
reijniers2014_metrics(estimations(i, j).doa, 'accP');
% local rms polar
metrics(i, j).rmsP = ...
reijniers2014_metrics(estimations(i, j).doa, 'rmsPmedianlocal');
% quadrant_err
metrics(i, j).querr = ...
reijniers2014_metrics(estimations(i, j).doa, 'querrMiddlebrooks');
end
end
fig2_barumerli2020forum = metrics;
amt_cache('set','fig2_barumerli2020forum',fig2_barumerli2020forum);
end
metrics = fig2_barumerli2020forum;
varargout{1} = metrics;
% aggregate metrics
ns = size(metrics,1);
own = logical(eye(ns));
other = not(own);
quants = [0,0.05,0.25,0.5,0.75,0.95,1];
% code similar to baumgartner2014 - fig9
le_own.quantiles = quantile([metrics(own).rmsL], quants);
lb_own.quantiles = quantile([metrics(own).accL], quants);
qe_own.quantiles = quantile([metrics(own).querr], quants);
pe_own.quantiles = quantile([metrics(own).rmsP], quants);
pb_own.quantiles = quantile([metrics(own).accP], quants);
le_own.mean = mean([metrics(own).rmsL]);
lb_own.mean = mean([metrics(own).accL]);
qe_own.mean = mean([metrics(own).querr]);
pe_own.mean = mean([metrics(own).rmsP]);
pb_own.mean = mean([metrics(own).accP]);
le_other.quantiles = quantile([metrics(other).rmsL], quants);
lb_other.quantiles = quantile([metrics(other).accL], quants);
qe_other.quantiles = quantile([metrics(other).querr], quants);
pe_other.quantiles = quantile([metrics(other).rmsP], quants);
pb_other.quantiles = quantile([metrics(other).accP], quants);
le_other.mean = mean([metrics(other).rmsL]);
lb_other.mean = mean([metrics(other).accL]);
qe_other.mean = mean([metrics(other).querr]);
pe_other.mean = mean([metrics(other).rmsP]);
pb_other.mean = mean([metrics(other).accP]);
data = data_middlebrooks1999;
% baumgartner data
data_baum_temp = exp_baumgartner2014('fig9', 'no_plot');
data_baum.qe_pool = data_baum_temp(1).qe;
data_baum.pe_pool = data_baum_temp(1).pe;
data_baum.pb_pool = data_baum_temp(1).pb;
ns = size(data_baum.pe_pool,1);
own = eye(ns) == 1;
other = not(own);
data_baum.pb_pool = abs(data_baum.pb_pool);
data_baum.qe_own.quantiles = quantile(data_baum.qe_pool(own),[0,0.05,0.25,0.5,0.75,0.95,1]);
data_baum.pe_own.quantiles = quantile(data_baum.pe_pool(own),[0,0.05,0.25,0.5,0.75,0.95,1]);
data_baum.pb_own.quantiles = quantile(data_baum.pb_pool(own),[0,0.05,0.25,0.5,0.75,0.95,1]);
data_baum.qe_own.mean = mean(data_baum.qe_pool(own));
data_baum.pe_own.mean = mean(data_baum.pe_pool(own));
data_baum.pb_own.mean = mean(data_baum.pb_pool(own));
data_baum.qe_other.quantiles = quantile(data_baum.qe_pool(other),[0,0.05,0.25,0.5,0.75,0.95,1]);
data_baum.pe_other.quantiles = quantile(data_baum.pe_pool(other),[0,0.05,0.25,0.5,0.75,0.95,1]);
data_baum.pb_other.quantiles = quantile(data_baum.pb_pool(other),[0,0.05,0.25,0.5,0.75,0.95,1]);
data_baum.qe_other.mean = mean(data_baum.qe_pool(other));
data_baum.pe_other.mean = mean(data_baum.pe_pool(other));
data_baum.pb_other.mean = mean(data_baum.pb_pool(other));
% plot
if flags.do_plot
dx = 0.22;
dx_lat = 0.15;
Marker = 's-';
LineColor = [0 0.4470 0.7410];
data.Marker = 'ko-';
data.LineColor = [1 1 1]*0.3;
data_baum.Marker = 'd-';
data_baum.LineColor = [0.8500 0.3250 0.0980];
mFig = figure;
mFig.Units = 'centimeters';
mFig.Position = [5,5,35,10];
subplot(1, 5, 1)
middlebroxplot(1-dx_lat,data.le_own.quantiles,kv.MarkerSize, data.LineColor)
plot(1-dx_lat,data.le_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor)
middlebroxplot(1+dx_lat,le_own.quantiles,kv.MarkerSize, LineColor)
plot(1+dx_lat,le_own.mean,Marker,'MarkerSize', kv.MarkerSize, 'MarkerFaceColor', LineColor, 'MarkerEdgeColor', LineColor)
middlebroxplot(2-dx_lat,data.le_other.quantiles,kv.MarkerSize, data.LineColor)
plot(2-dx_lat,data.le_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor)
middlebroxplot(2+dx_lat,le_other.quantiles,kv.MarkerSize, LineColor)
plot(2+dx_lat,le_other.mean,Marker,'MarkerSize',kv.MarkerSize, 'MarkerFaceColor', LineColor, 'MarkerEdgeColor', LineColor)
ylabel('RMS Lateral Error [deg]','FontSize',kv.FontSize)
set(gca,'YLim',[-10 60],'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
subplot(1, 5, 2)
middlebroxplot(1-dx_lat,data.lb_own.quantiles,kv.MarkerSize, data.LineColor)
plot(1-dx_lat,data.lb_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor)
middlebroxplot(1+dx_lat,lb_own.quantiles,kv.MarkerSize, LineColor)
plot(1+dx_lat,lb_own.mean,Marker,'MarkerSize',kv.MarkerSize, 'MarkerFaceColor', LineColor, 'MarkerEdgeColor', LineColor)
middlebroxplot(2-dx_lat,data.lb_other.quantiles,kv.MarkerSize, data.LineColor)
plot(2-dx_lat,data.lb_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor)
middlebroxplot(2+dx_lat,lb_other.quantiles,kv.MarkerSize, LineColor)
plot(2+dx_lat,lb_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor', LineColor, 'MarkerEdgeColor', LineColor)
ylabel('Magnitude Lateral Bias [deg]','FontSize',kv.FontSize)
set(gca,'YLim',[-10 60],'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
subplot(1, 5, 3)
middlebroxplot(1-dx,data.qe_own.quantiles,kv.MarkerSize, data.LineColor)
midd = plot(1-dx,data.qe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor);
middlebroxplot(1,qe_own.quantiles,kv.MarkerSize, LineColor)
reij = plot(1,qe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor', LineColor, 'MarkerEdgeColor', LineColor);
middlebroxplot(1+dx,data_baum.qe_own.quantiles,kv.MarkerSize, data_baum.LineColor)
baum = plot(1+dx,data_baum.qe_own.mean,data_baum.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor', data_baum.LineColor, 'MarkerEdgeColor', data_baum.LineColor);
middlebroxplot(2-dx,data.qe_other.quantiles,kv.MarkerSize, data.LineColor)
plot(2-dx,data.qe_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor)
middlebroxplot(2,qe_other.quantiles,kv.MarkerSize, LineColor)
plot(2,qe_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor', LineColor, 'MarkerEdgeColor', LineColor)
middlebroxplot(2+dx,data_baum.qe_other.quantiles,kv.MarkerSize, data_baum.LineColor)
plot(2+dx,data_baum.qe_other.mean,data_baum.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor', data_baum.LineColor, 'MarkerEdgeColor', data_baum.LineColor)
ylabel('Quadrant Errors (%)','FontSize',kv.FontSize)
set(gca,'YLim',[0 50],'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
leg = legend([midd, reij, baum], {'Actual', 'SA', 'SP'}, 'Location', 'none');
leg.Units = 'normalized';
leg.Position = [0.4745,0.831536390309064,0.097524379329044,0.159029645257883];
subplot(1, 5, 4)
middlebroxplot(1-dx,data.pe_own.quantiles,kv.MarkerSize, data.LineColor)
plot(1-dx,data.pe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor)
middlebroxplot(1,pe_own.quantiles,kv.MarkerSize, LineColor)
plot(1,pe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',LineColor, 'MarkerEdgeColor', LineColor)
middlebroxplot(1+dx,data_baum.pe_own.quantiles,kv.MarkerSize, data_baum.LineColor)
plot(1+dx,data_baum.pe_own.mean,data_baum.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data_baum.LineColor, 'MarkerEdgeColor', data_baum.LineColor)
middlebroxplot(2-dx,data.pe_other.quantiles,kv.MarkerSize, data.LineColor)
plot(2-dx,data.pe_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor)
middlebroxplot(2,pe_other.quantiles,kv.MarkerSize, LineColor)
plot(2,pe_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',LineColor, 'MarkerEdgeColor', LineColor)
middlebroxplot(2+dx,data_baum.pe_other.quantiles,kv.MarkerSize, data_baum.LineColor)
plot(2+dx,data_baum.pe_other.mean,data_baum.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data_baum.LineColor, 'MarkerEdgeColor', data_baum.LineColor)
ylabel('Local Polar RMS Error (deg)','FontSize',kv.FontSize)
set(gca,'YLim',[-10 60],'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
subplot(1, 5, 5)
middlebroxplot(1-dx,data.pb_own.quantiles,kv.MarkerSize, data.LineColor)
plot(1-dx,data.pb_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor)
middlebroxplot(1,pb_own.quantiles,kv.MarkerSize, LineColor)
plot(1,pb_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',LineColor, 'MarkerEdgeColor', LineColor)
middlebroxplot(1+dx,data_baum.pb_own.quantiles,kv.MarkerSize, data_baum.LineColor)
plot(1+dx,data_baum.pb_own.mean,data_baum.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data_baum.LineColor, 'MarkerEdgeColor', data_baum.LineColor)
middlebroxplot(2-dx,data.pb_other.quantiles,kv.MarkerSize, data.LineColor)
plot(2-dx,data.pb_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.LineColor)
middlebroxplot(2,pb_other.quantiles,kv.MarkerSize, LineColor)
plot(2,pb_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',LineColor, 'MarkerEdgeColor', LineColor)
middlebroxplot(2+dx,data_baum.pb_other.quantiles,kv.MarkerSize, data_baum.LineColor)
plot(2+dx,data_baum.pb_other.mean,data_baum.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor', data_baum.LineColor, 'MarkerEdgeColor', data_baum.LineColor)
ylabel('Magnitude of Elevation Bias (deg)','FontSize',kv.FontSize)
set(gca,'YLim',[-10 60],'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
end
end
%% ------ fig3_barumerli2020forum -------------------------------------
if flags.do_fig3_barumerli2020forum
fig3_barumerli2020forum = [];
if ~flags.do_redo
fig3_barumerli2020forum = amt_cache('get', ...
'fig3_barumerli2020forum',flags.cachemode);
end
if isempty(fig3_barumerli2020forum)
% Settings
num_exp = 1;
% Load Data
% Speech Samples from Harvard Word list
speechsample = amt_cache('get','../experiments%2Fexp_baumgartner2014.m/best2005speechSamples');
samples_num = length(speechsample);
% FIR Low-pass filters at 8kHz
% Brick-wall (aka sinc-filter): fir1(200,1/3) -> -60 dB
x=amt_load('baumgartner2014','highfreqatten_filters.mat');
LP{1} = [1 zeros(1,100)];
LP{2} = x.fir20db;
LP{3} = x.fir40db;
LP{4} = x.fir60db;
% Model Data
sbj_dtf = data_baumgartner2014('pool', 'cached');
estimations = struct('doa', struct([]));
est_expflat = repmat(estimations, length(sbj_dtf), 1);
est_expLP = repmat(estimations, ...
length(sbj_dtf),length(LP),length(speechsample));
% Simulations
amt_disp('Processing subjects HRIR');
for i = 1:length(sbj_dtf)
amt_disp(['Pre-processing subject #' num2str(i)]);
% noise stimulus
[sbj_template(i), sbj_target_flat(i)] = ...
reijniers2014_featureextraction(sbj_dtf(i).Obj);
% flat spectrum estimations
est_expflat(i, 1).doa = ...
reijniers2014(sbj_template(i), sbj_target_flat(i), 'num_exp', num_exp);
amt_disp('Computing localization');
for f = 1:length(LP)
amt_disp(sprintf('Filter %i\n', f))
for s = 1:samples_num
stim = filter(LP{f},1,speechsample{s});
[~, trgt] = ...
reijniers2014_featureextraction(sbj_dtf(i).Obj, ...
'source_ir', stim);
est_expLP(i, f, s).doa = ...
reijniers2014(sbj_template(i), trgt, 'num_exp', num_exp);
end
end
end
% metrics
% allocate memory for results
% aggregate over different lateral angles
ale_expflat = zeros(length(sbj_dtf),1);
ale_expLP = zeros(length(sbj_dtf),length(LP), samples_num);
ape_expflat = zeros(length(sbj_dtf),1);
ape_expLP = zeros(length(sbj_dtf),length(LP), samples_num);
qe_expflat = zeros(length(sbj_dtf),1);
qe_expLP = zeros(length(sbj_dtf),length(LP), samples_num);
for i = 1:length(sbj_dtf)
amt_disp(['Computing metrics #' num2str(i)]);
% flat spectrum estimations
ale_expflat(i,1) = reijniers2014_metrics(est_expflat(i, 1).doa, 'accabsL');
ape_expflat(i,1) = reijniers2014_metrics(est_expflat(i, 1).doa, 'accabsP');
qe_expflat(i,1) = reijniers2014_metrics(est_expflat(i, 1).doa, 'querr');
for f = 1:length(LP)
for s = 1:samples_num
ale_expLP(i, f, s) = reijniers2014_metrics(est_expLP(i, f, s).doa, 'accabsL');
ape_expLP(i, f, s) = reijniers2014_metrics(est_expLP(i, f, s).doa, 'accabsP');
qe_expLP(i, f, s) = reijniers2014_metrics(est_expLP(i, f, s).doa, 'querr');
end
end
end
% save cache
fig3_barumerli2020forum.ale_expflat = ale_expflat;
fig3_barumerli2020forum.ale_expLP = ale_expLP;
fig3_barumerli2020forum.ape_expflat = ape_expflat;
fig3_barumerli2020forum.ape_expLP = ape_expLP;
fig3_barumerli2020forum.qe_expflat = qe_expflat;
fig3_barumerli2020forum.qe_expLP = qe_expLP;
amt_cache('set','fig3_barumerli2020forum', fig3_barumerli2020forum);
end
varargout{1} = [fig3_barumerli2020forum];
ale_expflat = fig3_barumerli2020forum.ale_expflat;
ale_expLP = fig3_barumerli2020forum.ale_expLP;
ape_expflat = fig3_barumerli2020forum.ape_expflat;
ape_expLP = fig3_barumerli2020forum.ape_expLP;
qe_expflat = fig3_barumerli2020forum.qe_expflat;
qe_expLP = fig3_barumerli2020forum.qe_expLP;
% load real and baum2014 data
data = data_best2005;
% DCN enabled
[baum_temp, ~] = exp_baumgartner2014('fig11', 'no_plot');
data_baum.ape = zeros(size(ape_expLP));
data_baum.qe = zeros(size(qe_expLP));
for i = 1:size(ape_expLP,3)
data_baum.ape(:,:,i) = transpose(baum_temp{1}(:,:,i));
data_baum.qe(:,:,i) = transpose(baum_temp{2}(:,:,i));
end
data_baum.ape_expflat = baum_temp{3};
data_baum.qe_expflat = baum_temp{4};
% Pool Samples
ale_pooled = mean(ale_expLP,3);
ape_pooled = mean(ape_expLP,3);
qe_pooled = mean(qe_expLP,3);
data_baum.ape_pooled = mean(data_baum.ape,3);
data_baum.qe_pooled = mean(data_baum.qe,3);
% Confidence Intervals or standard errors
% reijniers model
df_speech = size(ale_expLP,1)-1;
tquant_speech = 1;
seale_speech = std(ale_pooled,0,1)*tquant_speech/(df_speech+1);
df_noise = size(ale_expflat,1)-1;
tquant_noise = 1;
seale_noise = std(ale_expflat)*tquant_noise/(df_noise+1);
seale = [seale_noise, seale_speech];
df_speech = size(ape_expLP,1)-1;
tquant_speech = 1;
seape_speech = std(ape_pooled,0,1)*tquant_speech/(df_speech+1);
df_noise = size(ape_expflat,1)-1;
tquant_noise = 1;
seape_noise = std(ape_expflat)*tquant_noise/(df_noise+1);
seape = [seape_noise, seape_speech];
% baumgartner2014 model
df_speech = size(data_baum.ape,1)-1;
tquant_speech = 1;
seape_speech = std(data_baum.ape_pooled,0,1)*tquant_speech/(df_speech+1);
df_noise = size(data_baum.ape_expflat,1)-1;
tquant_noise = 1;
seape_noise = std(data_baum.ape_expflat)*tquant_noise/(df_noise+1);
data_baum.seape = [seape_noise, seape_speech];
% averages
% reij2014
ale = mean([ale_expflat, ale_pooled],1);
ape = mean([ape_expflat, ape_pooled],1);
qe = mean([qe_expflat, qe_pooled],1);
% baum2014
data_baum.ape = mean([data_baum.ape_expflat', data_baum.ape_pooled],1);
data_baum.qe = mean([data_baum.qe_expflat', data_baum.qe_pooled],1);
if flags.do_plot
MarkerSize = kv.MarkerSize;
FontSize = kv.FontSize;
LineColor = [0 0.4470 0.7410];
Marker = 's-';
data.Marker = 'o-';
data.LineColor = [1 1 1]*0.3;
data_baum.Marker = 'd--';
data_baum.LineColor = [0.8500 0.3250 0.0980];
mFig = figure;
mFig.Units = 'centimeters';
mFig.Position = [5,5,13.5,15];
dx = 0;
xticks = 0:size(ale_pooled,2);
subplot(3,1,1)
plot(xticks-dx,data.ale, data.Marker,'Color', data.LineColor, 'MarkerFaceColor',data.LineColor,'MarkerSize',MarkerSize)
hold on
plot(xticks-dx,ale, Marker,'Color', LineColor, 'MarkerFaceColor',LineColor,'MarkerSize',MarkerSize)
ylabel('Lateral Error (deg)','FontSize',FontSize)
set(gca,'XTick',xticks,'XTickLabel',[],'FontSize',FontSize)
set(gca,'XLim',[-0.5 4.5],'YLim',[0 75],'YMinorTick','on')
subplot(3,1,2)
plot(xticks,data.ape,data.Marker,'Color', data.LineColor, 'MarkerFaceColor',data.LineColor,'MarkerSize',MarkerSize)
hold on
plot(xticks-dx,ape, Marker,'Color', LineColor, 'MarkerFaceColor',LineColor,'MarkerSize',MarkerSize)
plot(xticks,data_baum.ape, data_baum.Marker,'Color', data_baum.LineColor, 'MarkerFaceColor',data_baum.LineColor,'MarkerSize',MarkerSize)
ylabel('Polar Error (deg)','FontSize',FontSize)
set(gca,'XTick',xticks,'XTickLabel',[],'FontSize',FontSize)
set(gca,'XLim',[-0.5 4.5],'YLim',[0 75],'YMinorTick','on')
subplot(3,1,3)
best = plot(xticks([1 2 5]),data.qe([1 2 5]), data.Marker,'Color', data.LineColor, 'MarkerFaceColor',data.LineColor,'MarkerSize',MarkerSize);
hold on
reij = plot(xticks-dx,qe, Marker,'Color', LineColor, 'MarkerFaceColor',LineColor,'MarkerSize',MarkerSize);
baum = plot(xticks,data_baum.qe, data_baum.Marker,'Color', data_baum.LineColor, 'MarkerFaceColor',data_baum.LineColor,'MarkerSize',MarkerSize);
ylabel('Quadrant Err. (%)','FontSize',FontSize)
set(gca,'XTick',xticks,'XTickLabel',data.meta,'FontSize',FontSize,...
'XLim',[-0.5 4.5],'YLim',[-3 54],'YMinorTick','on')
leg = legend([best, reij, baum], {'Actual', 'SA', 'SP'});
leg.FontSize = FontSize - 1;
leg.Units = 'centimeters';
leg.Position = [9.281,12.762,3.466,1.561];
end
end
if flags.do_fig4_barumerli2020forum
fig4_barumerli2020forum = [];
if ~flags.do_redo
fig4_barumerli2020forum = amt_cache('get', ...
'fig4_barumerli2020forum',flags.cachemode);
end
if isempty(fig4_barumerli2020forum)
% load 23 DTFs from baumgartner2014
amt_disp('Loading SOFA files');
sbj_dtf = data_baumgartner2014('pool','global');
num_exp = 5;
% generate stimulus
% copyed from exp_baumgartner2014/do_fig10
density = [0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 6, 8]; % ripples/oct
depth = 10:10:40; % ripple depth (peak-to-trough) in dB
% 250-ms bursts, 20-ms raised-cosine fade in/out, flat from 0.6-16kHz
fs = sbj_dtf(1).Obj.Data.SamplingRate;
flow = 1e3; % lower corner frequency of ripple modification in Hz
fhigh = 16e3; % upper corner frequency of ripple modification in Hz
Nf = 2^10; % # Frequency bins
f = 0:fs/2/Nf:fs/2; % frequency bins
id600 = find(f<=600,1,'last'); % index of 600 Hz (lower corner frequency of stimulus energy)
idlow = find(f<=flow,1,'last'); % index of flow (ripples)
idhigh = find(f>=fhigh,1,'first'); % index of fhigh (ripples)
N600low = idlow - id600 +1; % # bins without ripple modification
Nlowhigh = idhigh - idlow +1; % # bins with ripple modification %
O = log2(f(idlow:idhigh)/1e3); % freq. trafo. to achieve equal ripple density in log. freq. scale
% Raised-cosine "(i.e., cos^2)" ramp 1/8 octave wide
fup = f(idlow)*2^(1/8); % upper corner frequency of ramp upwards
idup = find(f<=fup,1,'last');
Nup = idup-idlow+1;
rampup = cos(-pi/2:pi/2/(Nup-1):0).^2;
fdown = f(idhigh)*2^(-1/8); % lower corner frequency of ramp downwards
iddown = find(f>=fdown,1,'first');
Ndown = idhigh-iddown+1;
rampdown = cos(0:pi/2/(Ndown-1):pi/2).^2;
ramp = [rampup ones(1,Nlowhigh-Nup-Ndown) rampdown];
ramp = [-inf*ones(1,id600-1) zeros(1,N600low) ramp -inf*ones(1,Nf - idhigh)];
% Ripples of Experiment I
Sexp1 = zeros(Nf+1,length(density),2); % 3rd dim: 1:0-phase 2:pi-phase
Sexp1(idlow:idhigh,:,1) = (40/2* sin(2*pi*density'*O+ 0))'; % depth: 40dB, 0-phase
Sexp1(idlow:idhigh,:,2) = (40/2* sin(2*pi*density'*O+pi))'; % depth: 40dB, pi-phase
Sexp1 = repmat(ramp',[1,length(density),2]) .* Sexp1;
Sexp1 = [Sexp1;Sexp1(Nf:-1:2,:,:)];
Sexp1(isnan(Sexp1)) = -100;
sexp1 = ifftreal(10.^(Sexp1/20),2*Nf);
sexp1 = circshift(sexp1,Nf); % IR corresponding to ripple modification
sexp1 = squeeze(sexp1(:,:,1));
% Ripples of Experiment II
Sexp2 = zeros(Nf+1,length(depth),2); % 3rd dim: 1:0-phase 2:pi-phase
Sexp2(idlow:idhigh,:,1) = (depth(:)/2*sin(2*pi*1*O+ 0))'; % density: 1 ripple/oct, 0-phase
Sexp2(idlow:idhigh,:,2) = (depth(:)/2*sin(2*pi*1*O+pi))'; % density: 1 ripple/oct, pi-phase
Sexp2 = repmat(ramp',[1,length(depth),2]) .* Sexp2;
Sexp2 = [Sexp2;Sexp2(Nf-1:-1:2,:,:)];
Sexp2(isnan(Sexp2)) = -100;
sexp2 = ifftreal(10.^(Sexp2/20),2*Nf);
sexp2 = circshift(sexp2,Nf); % IR corresponding to ripple modification
sexp2 = squeeze(sexp2(:,:,1));
% preprocess templates for each user
amt_disp('Processing subjects HRIR');
for i = 1:length(sbj_dtf)
% extract directions
% filter targets' coordinates
% convert from spherical to horizontal-polar coordinates
%horpolar_coords = SOFAconvertCoordinates(...
% sbj_dtf(i).Obj.SourcePosition, ...
% 'spherical', 'horizontal-polar');
horpolar_coords = zeros(size(sbj_dtf(i).Obj.SourcePosition));
[horpolar_coords(:,2),horpolar_coords(:,1),horpolar_coords(:,3)]=...
cart2sph(sbj_dtf(i).Obj.SourcePosition(:,1),sbj_dtf(i).Obj.SourcePosition(:,3),-sbj_dtf(i).Obj.SourcePosition(:,2));
horpolar_coords(:,1:2)=rad2deg(horpolar_coords(:,1:2));
horpolar_coords(:,1)=-horpolar_coords(:,1);
horpolar_coords(:,2)=mod(horpolar_coords(:,2),360);
% polar in [60, 120]
% lateral = 0
idx = find(((horpolar_coords(:, 2) >= 300 ...
| horpolar_coords(:, 2) <= 60) ...
| (horpolar_coords(:, 2) >= 120 & ...
horpolar_coords(:, 2) <= 240)) ...
& (horpolar_coords(:, 1) <= 30 & horpolar_coords(:, 1) >= -30));
amt_disp(['Pre-processing subject #' num2str(i)]);
[sbj_template(i), sbj_target_flat(i)] = ...
reijniers2014_featureextraction(sbj_dtf(i).Obj, ...
'targ_az', sbj_dtf(i).Obj.SourcePosition(idx, 1), ...
'targ_el', sbj_dtf(i).Obj.SourcePosition(idx, 2));
amt_disp('Densities conditions');
for j = 1:length(density)
[~, sbj_target_exp1(i, j)] = ...
reijniers2014_featureextraction(sbj_dtf(i).Obj, ...
'source_ir', squeeze(sexp1(:, j)), ...
'targ_az', sbj_dtf(i).Obj.SourcePosition(idx, 1), ...
'targ_el', sbj_dtf(i).Obj.SourcePosition(idx, 2));
end
amt_disp('Depth conditions');
for j = 1:length(depth)
[~, sbj_target_exp2(i, j)] = ...
reijniers2014_featureextraction(sbj_dtf(i).Obj, ...
'source_ir', squeeze(sexp2(:, j)), ...
'targ_az', sbj_dtf(i).Obj.SourcePosition(idx, 1), ...
'targ_el', sbj_dtf(i).Obj.SourcePosition(idx, 2));
end
end
% preallocation for results
amt_disp('Allocating memory for results');
estimations = struct('doa', struct([]));
est_expflat = repmat(estimations, length(sbj_dtf));
est_exp1 = repmat(estimations, ...
length(sbj_dtf),length(density));
est_exp2 = repmat(estimations, ...
length(sbj_dtf),length(depth));
% simulations
for i = 1:length(sbj_dtf)
amt_disp(['Processing subject #' num2str(i)]);
% flat spectrum estimations
est_expflat(i, 1).doa = ...
reijniers2014(sbj_template(i), sbj_target_flat(i), 'num_exp', num_exp);
% rippled estimations
for j = 1:length(density)
est_exp1(i, j).doa = ...
reijniers2014(sbj_template(i), sbj_target_exp1(i, j), 'num_exp', num_exp);
end
for j =1:length(depth)
est_exp2(i, j).doa = ...
reijniers2014(sbj_template(i), sbj_target_exp2(i, j), 'num_exp', num_exp);
end
end
% metrics
% allocate memory for results
% aggregate over different lateral angles
pe_exp1 = zeros(length(sbj_dtf),length(density));
pe_exp2 = zeros(length(sbj_dtf),length(depth));
pe_flat = zeros(length(sbj_dtf), 1);
for i = 1:length(sbj_dtf)
% compute regression (see paper)
[f,r] = reijniers2014_metrics(est_expflat(i).doa,'sirpMacpherson2000');
pe_flat(i) = reijniers2014_metrics(est_expflat(i).doa,f,r,'perMacpherson2003');
for j = 1:size(est_exp1, 2)
pe_exp1(i, j) = reijniers2014_metrics(est_exp1(i, j).doa, f, r, 'perMacpherson2003');
end
for j = 1:size(est_exp2, 2)
pe_exp2(i, j) = reijniers2014_metrics(est_exp2(i, j).doa, f, r, 'perMacpherson2003');
end
end
% save cache
fig4_barumerli2020forum.pe_flat = pe_flat;
fig4_barumerli2020forum.pe_exp1 = pe_exp1;
fig4_barumerli2020forum.pe_exp2 = pe_exp2;
amt_cache('set','fig4_barumerli2020forum', fig4_barumerli2020forum);
end
varargout{1} = fig4_barumerli2020forum;
% simulations data
pe_flat = fig4_barumerli2020forum.pe_flat;
pe_exp1 = fig4_barumerli2020forum.pe_exp1;
pe_exp2 = fig4_barumerli2020forum.pe_exp2;
% Original data:
data = data_macpherson2003;
% Baumgartner2014's data
% varargout{1} = {pe_exp1,pe_exp2,pe_flat,noDCN};
data_baum_temp = exp_baumgartner2014('fig10', 'no_plot');
data_baum.pe_exp1 = data_baum_temp{1,1};
data_baum.pe_exp2 = data_baum_temp{1,2};
data_baum.pe_flat = data_baum_temp{1,3};
% Phase condition handling
% average across the phase condition
% real data
data.pe_exp1 = mean(data.pe_exp1,3);
data.pe_exp2 = mean(data.pe_exp2,3);
% baumgartner data
data_baum.pe_exp1 = mean(data_baum.pe_exp1,3);
data_baum.pe_exp2 = mean(data_baum.pe_exp2,3);
idphase = 1;
% Increase
% simulations
%pe_exp1 = pe_exp1 - pe_flat(:);
pe_exp1 = pe_exp1 - repmat(pe_flat(:), 1, size(pe_exp1, 2));
%pe_exp2 = pe_exp2 - pe_flat(:);
pe_exp2 = pe_exp2 - repmat(pe_flat(:), 1, size(pe_exp2, 2));
% baumgartner data
data_baum.pe_exp1 = data_baum.pe_exp1 - repmat(data_baum.pe_flat(:),1,size(data_baum.pe_exp1,2));
data_baum.pe_exp2 = data_baum.pe_exp2 - repmat(data_baum.pe_flat(:),1,size(data_baum.pe_exp2,2));
% Statistics
% simulations
quart_pe_flat = quantile(pe_flat,[.25 .50 .75]);
quart_pe_exp1 = quantile(pe_exp1,[.25 .50 .75]);
quart_pe_exp2 = quantile(pe_exp2,[.25 .50 .75]);
% real data
data.quart_pe_flat = quantile(data.pe_flat,[.25 .50 .75]);
data.quart_pe_exp1 = quantile(data.pe_exp1,[.25 .50 .75]);
data.quart_pe_exp2 = quantile(data.pe_exp2,[.25 .50 .75]);
% baumgartner data
data_baum.quart_pe_flat = quantile(data_baum.pe_flat,[.25 .50 .75]);
data_baum.quart_pe_exp1 = quantile(data_baum.pe_exp1,[.25 .50 .75]);
data_baum.quart_pe_exp2 = quantile(data_baum.pe_exp2,[.25 .50 .75]);
% plot
if flags.do_plot
dx = 1.05;
FontSize = kv.FontSize;
MarkerSize = kv.MarkerSize;
LineColor = [0 0.4470 0.7410];
data.Marker = 'ko-';
data.LineColor = [1 1 1]*0.3;
data_baum.Marker = 'd-';
data_baum.LineColor = [0.8500 0.3250 0.0980];
% Exp1
mFig = figure;
mFig.Units = 'centimeters';
mFig.Position = [5,5,13.5,12];
subplot(2,8,1:8)
mach = errorbar(data.density,data.quart_pe_exp1(2,:,idphase),...
data.quart_pe_exp1(2,:,idphase) - data.quart_pe_exp1(1,:,idphase),...
data.quart_pe_exp1(3,:,idphase) - data.quart_pe_exp1(2,:,idphase),...
'o-','MarkerSize',MarkerSize, 'Color', data.LineColor, ...
'MarkerFaceColor', data.LineColor);
hold on
reij = errorbar(data.density/dx,quart_pe_exp1(2,:,idphase),...
quart_pe_exp1(2,:,idphase) - quart_pe_exp1(1,:,idphase),...
quart_pe_exp1(3,:,idphase) - quart_pe_exp1(2,:,idphase),...
's-','MarkerSize',MarkerSize, 'Color', LineColor,'MarkerFaceColor',LineColor);
hold on
baum = errorbar(data.density*dx,data_baum.quart_pe_exp1(2,:,idphase),...
data_baum.quart_pe_exp1(2,:,idphase) - data_baum.quart_pe_exp1(1,:,idphase),...
data_baum.quart_pe_exp1(3,:,idphase) - data_baum.quart_pe_exp1(2,:,idphase),...
'd--','MarkerSize',MarkerSize, 'Color', data_baum.LineColor,'MarkerFaceColor',data_baum.LineColor);
set(gca,'XScale','log','YMinorTick','on')
set(gca,'XLim',[0.25/1.2 8*1.2],'XTick',data.density,'YLim',[-16 59],'FontSize',FontSize)
xlabel('Ripple Density (ripples/octave)','FontSize',FontSize)
ylabel({'Increase in';'Polar Error Rate (%)'},'FontSize',FontSize)
% Exp2
subplot(2,8,9:13)
errorbar(data.depth,data.quart_pe_exp2(2,:,idphase),...
data.quart_pe_exp2(2,:,idphase) - data.quart_pe_exp2(1,:,idphase),...
data.quart_pe_exp2(3,:,idphase) - data.quart_pe_exp2(2,:,idphase),...
'o-','MarkerSize',MarkerSize, 'Color', data.LineColor, ...
'MarkerFaceColor', data.LineColor);
hold on
errorbar(data.depth-1,quart_pe_exp2(2,:,idphase),...
quart_pe_exp2(2,:,idphase) - quart_pe_exp2(1,:,idphase),...
quart_pe_exp2(3,:,idphase) - quart_pe_exp2(2,:,idphase),...
's-','MarkerSize',MarkerSize, 'Color', LineColor,'MarkerFaceColor',LineColor);
hold on
errorbar(data.depth+1,data_baum.quart_pe_exp2(2,:,idphase),...
data_baum.quart_pe_exp2(2,:,idphase) - data_baum.quart_pe_exp2(1,:,idphase),...
data_baum.quart_pe_exp2(3,:,idphase) - data_baum.quart_pe_exp2(2,:,idphase),...
'd--','MarkerSize',MarkerSize, 'Color', data_baum.LineColor,'MarkerFaceColor',data_baum.LineColor);
set(gca,'XLim',[data.depth(1)-5 data.depth(end)+5],'XTick',data.depth,...
'YLim',[-16 59],'YMinorTick','on','FontSize',FontSize)
xlabel('Ripple Depth (dB)','FontSize',FontSize)
ylabel({'Increase in';'Polar Error Rate (%)'},'FontSize',FontSize)
ytick = get(gca,'YTick');
ticklength = get(gca,'TickLength');
% Baseline
subplot(2,8,14:15)
errorbar(-0.5,data.quart_pe_flat(2),...
data.quart_pe_flat(2) - data.quart_pe_flat(1),...
data.quart_pe_flat(3) - data.quart_pe_flat(2),...
'o-','MarkerSize',MarkerSize, 'Color', data.LineColor, ...
'MarkerFaceColor', data.LineColor);
hold on
errorbar(0,quart_pe_flat(2),...
quart_pe_flat(2) - quart_pe_flat(1),...
quart_pe_flat(3) - quart_pe_flat(2),...
's-','MarkerSize',MarkerSize, 'Color', LineColor,'MarkerFaceColor',LineColor);
hold on
errorbar(0.5,data_baum.quart_pe_flat(2),...
data_baum.quart_pe_flat(2) - data_baum.quart_pe_flat(1),...
data_baum.quart_pe_flat(3) - data_baum.quart_pe_flat(2),...
'd--','MarkerSize',MarkerSize, 'Color', data_baum.LineColor,'MarkerFaceColor',data_baum.LineColor);
set(gca,'XLim',[-3 3],'XTick',0,'XTickLabel',{'Baseline'},...
'YLim',[-15 59],'YTick',ytick,'TickLength',3*ticklength,...
'FontSize',FontSize,'YAxisLocation','right')
xlabel(' ','FontSize',FontSize)
ylabel({'Polar Error Rate (%)'},'FontSize',FontSize)
%legend
leg = legend([mach, reij, baum], {'Actual', 'SA', 'SP'});
leg.FontSize = FontSize - 1;
leg.Units = 'centimeters';
leg.Position = [9.281,10,3.466,1.561];
% Overall correlation between actual and predicted median values
m_pe_pred = [quart_pe_exp1(2,:,idphase) quart_pe_exp2(2,:,idphase)];
m_pe_actual = [data.quart_pe_exp1(2,:,idphase) data.quart_pe_exp2(2,:,idphase)];
r = corrcoef(m_pe_pred,m_pe_actual);
r_sqr = r(2);
amt_disp('Correlation between actual and predicted median values (15 conditions):','documentation');
amt_disp(['w/ PSGE: r = ' num2str(r_sqr,'%0.2f')],'documentation');
end
end
function middlebroxplot(x,quantiles,MarkerSize,LineColor)
lilen = 0.1; % length of horizontal lines
% Symbols
plot(x,quantiles(1),'x','MarkerSize',MarkerSize, 'MarkerEdgeColor', LineColor) % min
hold on
plot(x,quantiles(7),'x','MarkerSize',MarkerSize, 'MarkerEdgeColor', LineColor) % max
% Horizontal lines
line(x+0.5*[-lilen,lilen],repmat(quantiles(2),2),'Color',LineColor) % lower whisker
line(x+[-lilen,lilen],repmat(quantiles(3),2),'Color',LineColor) % 25% Quartile
line(x+[-lilen,lilen],repmat(quantiles(4),2),'Color',LineColor) % Median
line(x+[-lilen,lilen],repmat(quantiles(5),2),'Color',LineColor) % 75% Quartile
line(x+0.5*[-lilen,lilen],repmat(quantiles(6),2),'Color',LineColor) % upper whisker
% Vertical lines
line([x,x],quantiles(2:3),'Color',LineColor) % connector lower whisker
line([x,x],quantiles(5:6),'Color',LineColor) % connector upper whisker
line([x,x]-lilen,quantiles([3,5]),'Color',LineColor) % left box edge
line([x,x]+lilen,quantiles([3,5]),'Color',LineColor) % left box edge
Build with Bootstrap