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EXP_BARUMERLI2023
Experiments and results of Barumerli et al. (2023)
Program code:
function exp_barumerli2023(varargin)
%EXP_BARUMERLI2023 Experiments and results of Barumerli et al. (2023)
% Usage: [] = exp_barumerli2023(flag)
%
% EXP_BARUMERLI2023(flag) reproduces figures and results of the study
% from Barumerli et al. (2023). Note: in the paper, we refer to magnitue
% profiles (MP) and gradient profiles (GP). This new naming has been
% introduced in a later stage. The code instead uses DTF instead of MP
% and PGE instead of PG.
%
% The following flags can be specified:
%
% 'tab2' Report data in Tab.2:
% The metrics LE, PE and QE computed for each fitted model
% are compared to the actual performances of five listeners in
% Majdak et al. (2010). Three different versions are tested
% each one with a different features space.
% The first considers with only binaural cues,
% the second combines binaural cues with spectral amplitudes
% and the third relies on binaural cues and spectral gradients.
%
% 'gain_test' Report results on prior contribution:
% This experiment prints the results of the analysis which
% employs the polar gain metric as in Ege et al. (2018) to
% quantify the contribution of the prior distribution.
%
% 'fig2' Reproduce Fig.2:
% Binarual feature examples. Visual comparison
% between the interaural cues for one subject in
% the frontal and horizontal plane.
%
% 'fig3' Reproduce Fig.3:
% Monaural feature examples. Visual comparison
% between the spectral cues for one subject in
% the median plane.
%
% 'fig4' Reproduce Fig.4:
% Prediction examples. Example of the model inferring the sound
% direction. Particularly, the plot shows the prediction of
% the bayesian observer based on the posterior distribution.
% Finally, such estimate is corrupted by motor noise required
% by the listener to provide a response.
%
% 'fig5' Reproduce Fig.5:
% Example of the fitted model based on spectral gradients.
% The actual data for both lateral and polar dimensions
% are from subject NH16 from Majdak et al. (2010).
% Moreover, model predictions, black dots, are:
% (a) based only on the likelihood function (i.e. inference driven
% only by sensory evidence) as in Reijniers et al. (2014)
% (b) Bayesian inference with both prior belief and sensory evidence.
% (c) Full model (i.e. Bayesian inference and sensorimotor stage.
%
% 'fig6' Reproduce Fig.6:
% Comparison between fitted models and actual data for
% five listeners in Majdak et al (2010).
% Actual (gray) and predicted (black) are the sound-localization
% performance metrics obtained by models based on
% spectral amplitues or gradients.
% Each row reports a different metrics:
% the first is about the Lateral Error (LE) as function of the
% lateral angle, the second and the third show the
% Polar Error (PE) and Quadrant Error (QE), respectively as
% a function of the polar angle, calculated for all targets
% within the lateral interval [-30, 30]deg.
%
% 'fig7' Reproduce Fig.7: see flag exp_middlebrooks1999
%
% 'fig8' Reproduce Fig.8: see flag exp_macpherson2003
%
% 'exp_middlebrooks1999' Reproduce Fig.7:
% Predicted localization performance obtained for
% the individual (Own) and non-individual (Other) HRTFs
% with models based on two feature spaces.
% Additionally, predictions from Reijniers et al. (2014) and
% Baumgartner et al. (2014) as well as actual data from
% the original experiment Middlebrooks (1999) are shown.
%
% 'exp_macpherson2003' Reproduce Fig.8:
% Effect of the spectral ripples on sound localization
% performance by means of the polar error metric.
% Top and bottom left panels show differences to
% the reference condition in the right-most bottom panel
% which reports the polar errors obtained with
% broadband noise without spectral ripples.
% All panels show, in addition to predictions from our models,
% predictions from Reijniers et al (2014) and
% Baumgartner et al. (2014) as well as actual data
% from the original experiment Macpherson and Middlebrooks (2003).
%
% Further, cache flags (see amt_cache) and plot flags can be specified:
%
% 'plot' Plot the output of the experiment. This is the default.
%
% 'no_plot' Don't plot, only return data.
%
% 'test' Run one iteration for the experiment for testing code.
%
% 'redo' Recompute all results (it can take a while)
%
% 'redo_fast' Recumpute all results but with less iterations. Cached files are not changed.
%
% Requirements:
% -------------
%
% 1) SOFA Toolbox or higher from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)
%
% 2) Data in auxdata/barumerli2023
%
% 3) Statistics Toolbox and Computer Vision Toolbox for Matlab
%
% Examples:
%
% To display Fig.5 use :
%
% exp_barumerli2023('fig5');
%
% To display Fig.6 use :
%
% exp_barumerli2023('fig6');
%
% To display Fig.7 use :
%
% exp_barumerli2023('fig7');
%
% To display Fig.8 use :
%
% exp_barumerli2023('fig8');
%
%
% References:
% R. Barumerli, P. Majdak, M. Geronazzo, D. Meijer, F. Avanzini, and
% R. Baumgartner. A Bayesian model for human directional localization of
% broadband static sound sources. Acta Acust., 7:12, 2023.
%
% R. Baumgartner, P. Majdak, and B. Laback. Modeling sound-source
% localization in sagittal planes for human listeners. The Journal of the
% Acoustical Society of America, 136(2):791--802, 2014.
%
% P. Majdak, M. J. Goupell, and B. Laback. 3-D localization of virtual
% sound sources: Effects of visual environment, pointing method and
% training. Atten Percept Psycho, 72:454--469, 2010.
%
% J. C. Middlebrooks. Virtual localization improved by scaling
% nonindividualized external-ear transfer functions in frequency. The
% Journal of the Acoustical Society of America, 106:1493--1510, 1999.
%
% E. A. Macpherson and J. C. Middlebrooks. Vertical-plane sound
% localization probed with ripple-spectrum noise. The Journal of the
% Acoustical Society of America, 114:430--445, 2003.
%
% 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.
%
%
% See also: demo_barumerli2023 barumerli2023 barumerli2023_featureextraction barumerli2023_metrics
%
% Url: http://amtoolbox.org/amt-1.6.0/doc/experiments/exp_barumerli2023.php
% AUTHOR: Roberto Barumerli
% Information Engineering Dept., University of Padova, Italy, 2021
% Acoustics Research Institute, OeAW, Wien, Austria, 2023
%% ------ Check input options ---------------------------------------------
definput.import = {'amt_cache'};
definput.keyvals.MarkerSize = 6;
definput.keyvals.FontSize = 9;
definput.flags.type = {'missingflag', 'tab1', 'tab2', 'gain_test', 'fig2', 'fig3', 'fig4', 'fig5', 'fig6', 'fig7', 'fig8', 'fig9', 'exp_middlebrooks1999', 'exp_macpherson2003'};
definput.flags.plot = {'plot', 'no_plot'};
definput.flags.redo = {'no_redo_fast','redo_fast', 'redo'};
definput.flags.test = {'no_test','test'};
[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
%% ------ tab1 - fitted parameters
if flags.do_tab1
data_majdak = data_majdak2010('Learn_M');
data_majdak([1:5]) = [];
calibrations = amt_load('barumerli2023', 'barumerli2023_calibration.mat');
calibrations = calibrations.cache.value;
if size(calibrations.sigma, 1) ~= length(data_majdak)
warning('sigma values not enough for the provided subejcts')
end
num_calib = size(calibrations.combination,1);
num_sub = size(calibrations.sigma, 1);
for c = 1:num_calib
fprintf("CALIBRATION %s\n", calibrations.combination{c,:})
fprintf("ID & PRIOR & ILD & MON & MOTOR\n")
for s = 1:num_sub % subjects
sigma_ild = calibrations.sigma(s,c).values(2);
sigma_mon = calibrations.sigma(s,c).values(3);
sigma_motor = calibrations.sigma(s,c).values(4);
sigma_prior = calibrations.sigma(s,c).values(5);
fprintf("%s & %.2f & %.2f & %.2f & %.2f\n", data_majdak(s).id, sigma_prior, sigma_ild, sigma_mon, sigma_motor)
end
end
end
%% ------ tab2 - fitted models and predicted perfomances
if flags.do_tab2
data_majdak = data_majdak2010('Learn_M');
data_majdak([1:5]) = [];
calibrations = amt_load('barumerli2023', 'barumerli2023_calibration.mat');
calibrations = calibrations.cache.value;
if size(calibrations.sigma, 1) ~= length(data_majdak)
warning('sigma values not enough for the provided subejcts')
end
tab2 = amt_cache('get', 'barumerli2023_tab2',flags.cachemode);
num_exp = 300;
num_calib = size(calibrations.combination,1);
num_sub = size(calibrations.sigma, 1);
if flags.do_redo_fast
num_exp = 20;
tab2 = [];
end
% Preallocation
if isempty(tab2)
tab2 = repmat(struct('err', ...
struct([])),length(data_majdak), size(calibrations.combination, 1));
for s = 1:num_sub % subjects
fprintf('\n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \n SUBJECT %s\n', data_majdak(s).id)
sofa = amt_load('barumerli2023',['ARI_' upper(calibrations.name{s}) '_hrtf_M_dtf 256.sofa']);
comb = calibrations.combination;
for c = 1:num_calib% feature space
fprintf('\nCOMBINATION %i ', c)
fprintf('%s ', comb{c,:})
[template_par, target_par] = barumerli2023_featureextraction(sofa, ...
calibrations.combination{c,1});
sigma_l = calibrations.sigma(s,c).values(1);
sigma_l2 = calibrations.sigma(s,c).values(2);
sigma_mon = calibrations.sigma(s,c).values(3);
sigma_m = calibrations.sigma(s,c).values(4);
sigma_prior = calibrations.sigma(s,c).values(5);
if calibrations.sigma(s,c).values(3) == 0
sigma_mon= [];
end
m = barumerli2023('template', template_par, ...
'target', target_par, ...
'num_exp', num_exp, ...
'sigma_itd', sigma_l, ...
'sigma_ild', sigma_l2, ...
'sigma_spectral', sigma_mon, ...
'sigma_motor', sigma_m,...
'MAP',...
'sigma_prior', sigma_prior);
tab2(s,c).err = barumerli2023_metrics(m,'middle_metrics');
end
end
if ~flags.do_redo_fast
amt_cache('set', 'barumerli2023_tab2',tab2);
end
end
% metrics
amt_disp(sprintf('CHECK CONDITIONs'))
metric_calib = zeros(num_calib,3);
for s = 1:num_sub
amt_disp(sprintf('\nSUBJECT %i', s))
mtx = data_majdak(s).mtx;
real = barumerli2023_metrics(mtx,'middle_metrics');
real_metric(s,1) = real.rmsL;
real_metric(s,2) = real.rmsP;
real_metric(s,3) = real.querr;
for c = 1:num_calib
% plot
amt_disp(sprintf('\tFEATURE SPACE %s ', calibrations.combination{c,:}))
le_tau = abs(tab2(s,c).err.rmsL - real.rmsL)/real.rmsL;
pe_tau = abs(tab2(s,c).err.rmsP - real.rmsP)/real.rmsP;
qe_tau_rau = abs(rau(tab2(s,c).err.querr, 1, 'PC') - rau(real.querr, 1, 'PC'))/rau(real.querr, 1, 'PC');
fprintf('le_tau %.2f pe_tau %.2f querr_tau %.2f\n', le_tau, pe_tau, qe_tau_rau);
metric_calib(c,1) = metric_calib(c,1) + tab2(s,c).err.rmsL;
metric_calib(c,2) = metric_calib(c,2) + tab2(s,c).err.rmsP;
metric_calib(c,3) = metric_calib(c,3) + tab2(s,c).err.querr;
end
end
metric_calib = metric_calib./num_sub;
% std stuff
for s = 1:num_sub
for c = 1:num_calib
metric_calib_std(c,1,s) = tab2(s,c).err.rmsL;
metric_calib_std(c,2,s) = tab2(s,c).err.rmsP;
metric_calib_std(c,3,s) = tab2(s,c).err.querr;
end
end
amt_disp(sprintf('\n############ TAB2 - AVERAGE OVER SUBJECTS'))
for c = 1:size(calibrations.combination, 1)
amt_disp(sprintf('\tFEATURE SPACE %s ', calibrations.combination{c,:}))
fprintf('le %.2f pe %.2f querr %.2f\n', metric_calib(c,:));
amt_disp(sprintf('le_std %.2f, pe_std %.2f, qe_std %.2f ',...
std(metric_calib_std(c,:,:), 0, 3)));
end
amt_disp(sprintf('\n############ TAB2 - REAL SUBJECTS'))
amt_disp(sprintf('le %.2f, pe %.2f, qe %.2f ',...
mean(squeeze(real_metric(:,1))), mean(squeeze(real_metric(:,2))), mean(squeeze(real_metric(:,3)))));
amt_disp(sprintf('le_std %.2f, pe_std %.2f, qe_std %.2f ',...
std(squeeze(real_metric(:,1))), std(squeeze(real_metric(:,2))), std(squeeze(real_metric(:,3)))));
end
%% ------ gain metric to evaluate prior contribution
if flags.do_gain_test
data_majdak = data_majdak2010('Learn_M');
data_majdak([1:5]) = [];
calibrations = amt_load('barumerli2023', 'barumerli2023_calibration.mat');
calibrations = calibrations.cache.value;
% Preallocation
gain_test = amt_cache('get', 'barumerli2023_gain_test',flags.cachemode);
% Preallocation
if isempty(gain_test)
for s = 1:length(data_majdak)
sofa = amt_load('barumerli2023',['ARI_' upper(data_majdak(s).id) '_hrtf_M_dtf 256.sofa']);
mtx = data_majdak(s).mtx;
[template, target] = barumerli2023_featureextraction(sofa, ...
'pge', ...
'targ_az', mtx(:, 1), ...
'targ_el', mtx(:, 2));
m_noprior = barumerli2023('template', template, ...
'target', target, ...
'num_exp', 5, ...
'sigma_itd',calibrations.sigma(s,2).values(1), ...
'sigma_ild', calibrations.sigma(s,2).values(2), ...
'sigma_spectral', calibrations.sigma(s,2).values(3), ...
'sigma_prior', [],...
'sigma_motor', []);
m_prior = barumerli2023('template', template, ...
'target', target, ...
'num_exp', 5, ...
'sigma_itd',calibrations.sigma(s,2).values(1), ...
'sigma_ild', calibrations.sigma(s,2).values(2), ...
'sigma_spectral', calibrations.sigma(s,2).values(3), ...
'sigma_prior', calibrations.sigma(s,2).values(5),...
'sigma_motor', []);
m_prior_motor = barumerli2023('template', template, ...
'target', target, ...
'num_exp', 5, ...
'sigma_itd',calibrations.sigma(s,2).values(1), ...
'sigma_ild', calibrations.sigma(s,2).values(2), ...
'sigma_spectral', calibrations.sigma(s,2).values(3), ...
'sigma_prior', calibrations.sigma(s,2).values(5),...
'sigma_motor', calibrations.sigma(s,2).values(4));
results = {mtx, m_noprior, m_prior, m_prior_motor};
metrics = {'gainPfront'};
for m = 1:length(results)
for sp = 1:length(metrics)
gain_test(s, m, sp) = localizationerror(results{m}, metrics{sp});
end
end
end
amt_cache('set', 'barumerli2023_gain_test', gain_test);
end
gain_test = squeeze(gain_test(:,:,1));
res = mean(gain_test, 1);
fprintf("Gains frontal plane averaged over 5 subjects\n")
fprintf("Real:\t\t%.2f\n", res(1))
fprintf("Only Likelihood:%.2f\n", res(2))
fprintf("Full Model:\t%.2f\n", res(4))
end
%% ------ figure with binaural features
if flags.do_fig2
if flags.do_redo
fig2 = [];
else
fig2 = amt_cache('get','barumerli2023_fig2',flags.cachemode);
end
if isempty(fig2)
sofa = amt_load('barumerli2023', 'ARI_NH12_hrtf_M_dtf 256.sofa');
template = barumerli2023_featureextraction(sofa);
c = template.coords.return_positions('horizontal-polar');
% select directions and values with polar angle less than 2 (eye-level)
% in the paper the line is smoother since I have been using more points
% for interpolation
pos = find(abs(c(:, 2)) < 2);
itd = template.itd(pos);
ild = template.ild(pos);
% sort them for plotting
[pos, idx] = sort(c(pos,1));
itd = -itd(idx);
ild = ild(idx);
fig2.pos = pos;
fig2.itd = itd;
fig2.ild = ild;
amt_cache('set','barumerli2023_fig2',fig2);
end
if flags.do_plot
% figure
FontSize = kv.FontSize;
figure('Units', 'points', 'Position', [200 200 245 150]);
% colororder([[55,126,184]./255-0.2; [77,175,74]./255-0.2])
if isoctave
ax = plotyy(fig2.pos, fig2.itd, fig2.pos, fig2.ild);
ylabel(ax(1),'ITD [JND]');
yticks(ax(1), sort([-15:7.5:15]))
ylabel(ax(2), 'ILD [dB]');
yticks(ax(2), ([-30:15:30]))
else
%yyaxis left
plot(fig2.pos, fig2.itd)
ylabel('ITD [JND]')
yticks(sort([-15:7.5:15]))
hold on
xticks([-90:45:90])
grid on
yyaxis right
plot(fig2.pos, fig2.ild)
ylabel('ILD [dB]')
yticks([-30:15:30])
end
xlabel('Lateral angle [deg]')
set(gca, 'FontSize', FontSize)
end
end
%% ------ figure with monaural features
if flags.do_fig3
if flags.do_redo
fig3 = [];
else
fig3 = amt_cache('get','barumerli2023_fig3',flags.cachemode);
end
if isempty(fig3)
data_majdak = data_majdak2010('Learn_M');
data_majdak([1:5]) = [];
calibrations = amt_load('barumerli2023', 'barumerli2023_calibration.mat');
calibrations = calibrations.cache.value;
sofa = amt_load('barumerli2023', ['ARI_' upper(data_majdak(1).id) '_hrtf_M_dtf 256.sofa']);
% Preprocessing source information for both directions
[dtf] = barumerli2023_featureextraction(sofa, 'template','dtf');
[pge] = barumerli2023_featureextraction(sofa, 'template','pge');
fig3.dtf = dtf;
fig3.pge = pge;
amt_cache('set','barumerli2023_fig3',fig3);
end
if flags.do_plot
fig = figure('Units', 'points', 'Position', [200 200 245 300]);
% (Nh, Nw, gap, marg_h, marg_w)
[ha, ~] = tight_subplot(2,1, [.06 0.08],[.1 .05],[.15 .15]);
hor = fig3.dtf.coords.return_positions('horizontal-polar');
med_plane_idx = find(abs(hor(:,1))<1);% & hor(:,2)<90 & hor(:,2)>=0;
t = hor(med_plane_idx,2);
[t_sort, t_idx] = sort(t);
t_sort(1) = -30;
t_sort(end) = 210;
f_dtf = fig3.dtf.fc./1e3;
sp = 1;
axes(ha(sp));
X = fig3.dtf.monaural(med_plane_idx(t_idx), 1:length(fig3.dtf.fc));
image(f_dtf,t_sort,X,'CDataMapping','scaled')
shading flat
title('Magnitude [dB]', 'FontWeight', 'normal')
view(0,90);
cdtf = colorbar('location', 'eastoutside', 'colormap', colormap(hot));
% cpge.Label.String = 'Magnitude [dB]';
% cdtf.Label.Rotation = 0;
% cdtf.Label.Position = [0.689523637294769 -23.4999957765852 0];
sp = 2;
axes(ha(sp));
X = fig3.pge.monaural(med_plane_idx(t_idx), 1:length(fig3.pge.fc));
image(f_dtf(1:end-1),t_sort,X,'CDataMapping','scaled')
shading flat
view(0,90);
title('Gradient [dB]', 'FontWeight', 'normal')
cpge = colorbar('location', 'eastoutside', 'colormap', colormap(hot));
% cpge.Label.String = 'Gradient [dB]';
% cpge.Label.Rotation = 0;
% cpge.Label.Position = [0.689523662839617 6.77143143245152 0];
%% LABELs and AXIS
set(ha, 'YDir','normal')
set(ha, 'FontSize',kv.FontSize)
set(ha, 'YLim', [-30, 210])
set(ha, 'YTick', [0:90:180])
set(ha, 'XScale', 'log')
set(ha, 'XTick', [0, 1, 5, 10])
set(ha, 'XTickLabel', {'0.1', '1', '5', '10'})
set(ha, 'XLim', [0.7, f_dtf(end-1)])
set(ha(1), 'XTick', [])
% set(ha([1, 2]), 'XTick', [])
set(ha([1]), 'CLim', [-35 -25])
set(ha([2]), 'CLim', [0 6])
ha(2).XLabel.String = 'Frequency [kHz]';
ha(1).YLabel.String = 'Polar angle [deg]';
ha(2).YLabel.String = 'Polar angle [deg]';
set(cdtf, 'Position', [0.879510204081633 0.5795 0.022 0.35]);
set(cpge, 'Position', [0.879510204081633 0.123 0.022 0.35]);
% saveas(fig, 'new_plots/monaural_features.eps', 'epsc')
% print(fig, 'fig4', '-dpng', '-r600')
end
end
%% ------ fig.4 from paper - model estimation
if flags.do_fig4
data_majdak = data_majdak2010('Learn_M');
data_majdak([1:5]) = [];
calibrations = amt_load('barumerli2023', 'barumerli2023_calibration.mat');
calibrations = calibrations.cache.value;
fig4 = amt_cache('get', 'barumerli2023_fig4',flags.cachemode);
% select one point from real data
az_target = data_majdak(5).mtx(177, 1);
el_target = data_majdak(5).mtx(177, 2);
if isempty(fig4)
s = 5;
sofa = amt_load('barumerli2023',['ARI_' upper(data_majdak(s).id) '_hrtf_M_dtf 256.sofa']);
[template, target] = barumerli2023_featureextraction(sofa, ...
'pge', ...
'targ_az', az_target, ...
'targ_el', el_target); % defined in spherical coordinates
fig4.template = template;
fig4.target = target;
[fig4.m, fig4.doa, fig4.target_coords] = ...
barumerli2023('template', template, ...
'target', target, ...
'num_exp', 1, ...
'sigma_itd', calibrations.sigma(s,2).values(1), ...
'sigma_ild', calibrations.sigma(s,2).values(2), ...
'sigma_spectral', calibrations.sigma(s,2).values(3), ...
'sigma_prior', calibrations.sigma(s,2).values(5),...
'sigma_motor', calibrations.sigma(s,2).values(4));
amt_cache('set', 'barumerli2023_fig4', fig4);
end
fig = figure('Units', 'points', 'Position', [100 100 245 300]);
temp_c = fig4.template.coords.return_positions('cartesian');
% load full sphere
dirs = amt_load('barumerli2023','dirs.mat');
dirs = dirs.cache.value;
% pad posterior otherwise artifacts in the plot
posterior = [fig4.doa.posterior, zeros(1,500)];
[~, cbar] = plot_reijniers2014(dirs, max(log10(posterior), -10), 'FontSize', kv.FontSize);
set(cbar, 'colormap', colormap(flipud(gray)))
set(cbar, 'Location', 'northoutside')
ctitle = get(cbar, 'Title');
set(ctitle, 'String', "$log(p(\varphi|t))$")
set(ctitle, 'Interpreter', "latex")
set(cbar, 'Position', [0.526530612244898 0.73 0.438819197403734 0.0266666666666666])
%% real direction
[x,y,x_sign] = lambert_area_projection(deg2rad(az_target),deg2rad(el_target));
q0 = plot(x+(1-x_sign),y,'x');
% plot(x+(1-x_sign),y,'x');
q0.MarkerSize = 10;
q0.LineWidth = 1.5;
q0.MarkerEdgeColor = [228,0,28]./255;
%% subject estimate
% select one point from real data
az_sbj = deg2rad(data_majdak(5).mtx(177, 3));
el_sbj = deg2rad(data_majdak(5).mtx(177, 4));
[x,y,x_sign] = lambert_area_projection(az_sbj,el_sbj);
q1 = plot(x+(1-x_sign),y,'+');
q1.MarkerSize = 10;
q1.LineWidth = 1.5;
q1.Color = [55,126,184]./255;
%% estimated direction by bayesian observer
[~, i] = max(posterior);
[az,el]=cart2sph(temp_c(i,1),temp_c(i,2),temp_c(i,3));
[x,y,x_sign] = lambert_area_projection(az,el);
q2 = plot(x+(1-x_sign),y,'+');
q2.MarkerSize = 10;
q2.LineWidth = 1.5;
q2.Color = [77,175,74]./255;
%% final estimate corrupted by sensorymotor scatter
est = fig4.doa.estimations;
[az,el]=cart2sph(est(:,:,1),est(:,:,2),est(:,:,3));
% lambert equal area projection
[x,y,x_sign] = lambert_area_projection(az,el);
q3 = plot(x+(1-x_sign),y,'+');
q3.MarkerSize = 10;
q3.LineWidth = 1.5;
q3.Color = [255,127,0]./255; % [0 0.4470 0.7410];
l = legend(["", repmat("", 1, 29), ...
"Source direction", "Human response", "Model estimate", "Model response"], ...
'Interpreter', 'latex', ...
'Position',[0.0303890612964734 0.691896928826252 0.433163763552296 0.188103071173748]);
% saveas(fig, 'new_plots/single_trial.eps', 'epsc')
% print(fig, 'fig5', '-dpng', '-r600')
end
%% -------------- fig 5 paper - individual perforances
if flags.do_fig5
data_majdak = data_majdak2010('Learn_M');
data_majdak([1:5]) = [];
calibrations = amt_load('barumerli2023', 'barumerli2023_calibration.mat');
calibrations = calibrations.cache.value;
if size(calibrations.sigma, 1) ~= length(data_majdak)
warning('sigma values not enough for the provided subejcts')
end
fig5 = amt_cache('get', 'barumerli2023_fig5',flags.cachemode);
% Preallocation
if isempty(fig5)
for s = 1:length(data_majdak)
sofa = amt_load('barumerli2023', ['ARI_' upper(data_majdak(s).id) '_hrtf_M_dtf 256.sofa']);
mtx = data_majdak(s).mtx;
fprintf("SUBJECT %s\n", data_majdak(s).id);
%% DTF
calibs_dtf = calibrations.sigma(s,1); % select dtf
calibs.sigma = calibs_dtf.values(1,1:3);
calibs.motor_sigma = calibs_dtf.values(1,4);
calibs.prior = calibs_dtf.values(1,5);
[template, target] = barumerli2023_featureextraction(sofa, ...
'dtf', ...
'targ_az', mtx(:, 1), ...
'targ_el', mtx(:, 2));
m_motor_dtf = barumerli2023('template', template, ...
'target', target, ...
'num_exp', 300, ...
'sigma_itd', calibs.sigma(1), ...
'sigma_ild', calibs.sigma(2), ...
'sigma_spectral', calibs.sigma(3), ...
'sigma_prior', calibs.prior,...
'sigma_motor', calibs.motor_sigma);
%% PGE
calibs_pge = calibrations.sigma(s,2); % select dtf
calibs.sigma = calibs_pge.values(1,1:3);
calibs.motor_sigma = calibs_pge.values(1,4);
calibs.prior = calibs_pge.values(1,5);
[template, target] = barumerli2023_featureextraction(sofa, ...
'pge', ...
'targ_az', mtx(:, 1), ...
'targ_el', mtx(:, 2));
m_motor_pge = barumerli2023('template', template, ...
'target', target, ...
'num_exp', 300, ...
'sigma_itd', calibs.sigma(1), ...
'sigma_ild', calibs.sigma(2), ...
'sigma_spectral', calibs.sigma(3), ...
'MAP',...
'sigma_prior', calibs.prior,...
'sigma_motor', calibs.motor_sigma);
results(s,:) = {m_motor_dtf, m_motor_pge};
end
amt_cache('set', 'barumerli2023_fig5', results);
end
%% COMPUTE RMS
lat = [-90, -40, 0, 40, 90];
lat_label = [-65, -20, 20, 65];
pol = [-30 30 150 210];
pol_label = [0 90 180];
% check
rmsL_dtf = zeros(length(data_majdak), length(lat)-1);
rmsL_pge = zeros(length(data_majdak), length(lat)-1);
rmsL_real = zeros(length(data_majdak), length(lat)-1);
rmsP_dtf = zeros(1, length(pol)-1);
rmsP_pge = zeros(1, length(pol)-1);
rmsP_real = zeros(1, length(pol)-1);
querr_dtf= zeros(1, length(pol)-1);
querr_pge= zeros(1, length(pol)-1);
querr_real = zeros(1, length(pol)-1);
for s = 1:length(data_majdak)
results = fig5(s,:);
m_dtf = results{1};
m_pge = results{2};
mtx = data_majdak(s).mtx;
for i=1:length(lat)-1
% real
mtx_temp = mtx((mtx(:, 5) > lat(i) & (mtx(:, 5) < lat(i+1))),:);
rmsL_real(s,i) = localizationerror(mtx_temp, 'rmsL');
% simulation
m_temp = m_pge((m_pge(:, 5) > lat(i) & (m_pge(:, 5) < lat(i+1))),:);
rmsL_pge(s,i) = localizationerror(m_temp, 'rmsL');
m_temp = m_dtf((m_dtf(:, 5) > lat(i) & (m_dtf(:, 5) < lat(i+1))),:);
rmsL_dtf(s,i) = localizationerror(m_temp, 'rmsL');
end
mtx(abs(mtx(7,:)) <= 30,:)=[];
m_pge(abs(m_pge(7,:)) <= 30,:)=[];
m_dtf(abs(m_dtf(7,:)) <= 30,:)=[];
for i=1:length(pol)-1
m_temp = mtx((mtx(:, 6) > pol(i) & (mtx(:, 6) < pol(i+1))),:);
rmsP_real(s,i) = localizationerror(m_temp, 'rmsPmedianlocal');
querr_real(s,i) = localizationerror(m_temp, 'querrMiddlebrooks');
m_temp = m_pge((m_pge(:, 6) > pol(i) & (m_pge(:, 6) < pol(i+1))),:);
rmsP_pge(s,i) = localizationerror(m_temp, 'rmsPmedianlocal');
querr_pge(s,i) = localizationerror(m_temp, 'querrMiddlebrooks');
m_temp = m_dtf((m_dtf(:, 6) > pol(i) & (m_dtf(:, 6) < pol(i+1))),:);
rmsP_dtf(s,i) = localizationerror(m_temp, 'rmsPmedianlocal');
querr_dtf(s,i) = localizationerror(m_temp, 'querrMiddlebrooks');
end
end
%% FIGURE
FontSize = 9;
% fig = figure('Units', 'Points', 'Position', [1e3 1e3 510 200]);
fig = figure('Units', 'Points', 'Position', [1 1 510 300]);
[ha, ~] = tight_subplot(3, length(data_majdak), [.1 0.01],[.15 0.08],[.09 .01]);
xangle = 0;
sp = 1;
Size = 36;
for s = 1:length(data_majdak)
marker_dtf = 's';
marker_pge = 'v';
% lateral
axes(ha(sp))
pos=get(gca,'Position');
set(gca,'Position',[pos(1) 0.6675 pos(3:4)]);
scatter(lat_label, rmsL_real(s,:), Size, 0.8*[1 1 1], 'filled')
hold on
scatter(lat_label, rmsL_dtf(s,:), Size, 0.2*[1 1 1], marker_dtf)
scatter(lat_label, rmsL_pge(s,:), Size, 0.2*[1 1 1], marker_pge)
set(gca, 'YLim', [0 20], 'Ytick', [0,10,20], 'Xtick', lat_label, 'Xlim', [-90, 90],'FontSize',FontSize)
title({upper(data_majdak(s).id)})
grid on
% polar
axes(ha(sp+length(data_majdak)));
pos=get(gca,'Position');
set(gca,'Position', [pos(1) 0.37 pos(3:4)]);
%ha(sp+length(data_majdak)).Position(2) = 0.37;
scatter(pol_label, rmsP_real(s,:), Size, 0.8*[1 1 1], 'filled')
hold on
scatter(pol_label, rmsP_dtf(s,:), Size, 0.2*[1 1 1], marker_dtf)
scatter(pol_label, rmsP_pge(s,:), Size, 0.2*[1 1 1], marker_pge)
set(gca, 'Xtick', pol_label, 'Xlim', [-90, 270], 'Ylim', [0 60], 'Ytick', [0 30 60],'FontSize',FontSize)
xtickangle(xangle)
grid on
% querr
axes(ha(sp+2*length(data_majdak)))
pos=get(gca,'Position'); %ha(sp+2*length(data_majdak)).Position(2) = 0.12;
set(gca,'Position',[pos(1) 0.12 pos(3:4)]);
scatter(pol_label, querr_real(s,:), Size, 0.8*[1 1 1], 'filled')
hold on
scatter(pol_label, querr_dtf(s,:), Size, 0.2*[1 1 1], marker_dtf)
scatter(pol_label, querr_pge(s,:), Size, 0.2*[1 1 1], marker_pge)
set(gca, 'Xtick', pol_label, 'Xlim', [-90, 270], 'Ylim', [-5 40], 'Ytick', [0, 20,40], 'FontSize',FontSize)
xtickangle(xangle)
grid on
sp = sp + 1;
end
set(ha([2:5, 7:10, 12:15]),'YTickLabel','')
set(ha([6:10]),'XTickLabel','')
for i=6:10
x=get(ha(i),'Title'); set(x,'String','');
end
x=get(ha(3),'XLabel');
set(x,'String',' Lateral angle [deg]');
set(x,'FontSize',FontSize);
x=get(ha(13),'XLabel');
set(x,'String',' Polar angle [deg]');
set(x,'FontSize',FontSize);
for i=1:5
set(ha(i),'TitleFontWeight','normal');
end
x_pos = -9;
x=get(ha(1),'YLabel');
set(x,'String',{'Lateral';'error [deg]'});
% ha(1).YLabel.Position = ha(1).YLabel.Position + [x_pos, 0, 0];
set(x,'FontSize',FontSize);
x=get(ha(6),'YLabel');
set(x,'String',{'Polar';'error [deg]'});
% ha(6).YLabel.Position = ha(6).YLabel.Position + [x_pos, 0, 0];
set(x,'FontSize',FontSize);
x=get(ha(11),'YLabel');
set(x,'String',{'Quadrant';'error [%]'});
% ha(11).YLabel.Position = ha(11).YLabel.Position + [x_pos, 0, 0];
x=get(ha(11),'YLabel');
legend({'Actual data', 'MP variant', 'GP variant'}, ...
'Orientation','horizontal', ... %'Interpreter', 'latex', ...
'Position', [0.330346204909391 0.92194871928753 0.412033794094542 0.0652307678919574])
% saveas(fig, 'new_plots/model_estimations_sectors.eps', 'epsc')
% print(fig, 'fig6', '-dpng', '-r600')
end
%% ------ fig6 from paper - model stages
if flags.do_fig6
data_majdak = data_majdak2010('Learn_M');
data_majdak([1:5]) = [];
calibrations = amt_load('barumerli2023', 'barumerli2023_calibration.mat');
calibrations = calibrations.cache.value;
if size(calibrations.sigma, 1) ~= length(data_majdak)
warning('sigma values not enough for the provided subejcts')
end
fig6 = amt_cache('get', 'barumerli2023_fig6',flags.cachemode);
% Preallocation
if isempty(fig6)
s = 3; % select subject 3
calibrations = calibrations.sigma(s,2); % select pge
sofa = amt_load('barumerli2023',['ARI_' upper(data_majdak(s).id) '_hrtf_M_dtf 256.sofa']);
mtx = data_majdak(s).mtx;
[template, target] = barumerli2023_featureextraction(sofa, ...
'pge', ...
'targ_az', mtx(:, 1), ...
'targ_el', mtx(:, 2));
m_noprior = barumerli2023('template', template, ...
'target', target, ...
'num_exp', 1, ...
'sigma_itd',calibrations.values(1), ...
'sigma_ild', calibrations.values(2), ...
'sigma_spectral', calibrations.values(3), ...
'sigma_prior', [],...
'sigma_motor', []);
m_prior = barumerli2023('template', template, ...
'target', target, ...
'num_exp', 1, ...
'sigma_itd',calibrations.values(1), ...
'sigma_ild', calibrations.values(2), ...
'sigma_spectral', calibrations.values(3), ...
'sigma_prior', calibrations.values(5),...
'sigma_motor', []);
m_prior_motor = barumerli2023('template', template, ...
'target', target, ...
'num_exp', 1, ...
'sigma_itd',calibrations.values(1), ...
'sigma_ild', calibrations.values(2), ...
'sigma_spectral', calibrations.values(3), ...
'sigma_prior', calibrations.values(5),...
'sigma_motor', calibrations.values(4));
fig6 = {m_noprior, m_prior, m_prior_motor, mtx};
amt_cache('set', 'barumerli2023_fig6', fig6);
end
m_noprior = fig6{1,1};
m_prior = fig6{1,2};
m_prior_motor = fig6{1,3};
mtx = fig6{1,4};
%% FIGURE
Size = 10;
fig = figure('Units', 'points', 'Position', [10 10 245 400]);
[ha, ~] = tight_subplot(3, 2, [.07 0.1], [.07 .07],[.13 .04]);
%% SUBPLOTs
sp = 1;
axes(ha(sp));
plot([-100 100], [-100 100], '-', 'Color', [1 1 1]*0.5)
hold on
p1=scatter(mtx(:, 5), mtx(:, 7), Size, 0.6*[1 1 1], 'filled');
p2=scatter(m_noprior(:, 5), m_noprior(:, 7), Size, 0.2*[1 1 1]);
sp = sp + 1;
axes(ha(sp));
plot([-90 270], [-90 270], '-', 'Color', [1 1 1]*0.5)
hold on
plot([-90 270], [-90 270]+90, '--', 'Color', [0 0 1]*0.5)
plot([-90 270], [-90 270]-90, '--', 'Color', [0 0 1]*0.5)
scatter(mtx(:, 6), mtx(:, 8), Size, 0.6*[1 1 1], 'filled');
m_noprior(~((m_noprior(:, 6) > -30) & (m_noprior(:, 6) < 210)),:)=[];
scatter(m_noprior(:, 6), m_noprior(:, 8), Size, 0.2*[1 1 1]);
% grid on
%% PRIOR
sp = sp + 1;
axes(ha(sp));
plot([-100 100], [-100 100], '-', 'Color', [1 1 1]*0.5)
hold on
scatter(mtx(:, 5), mtx(:, 7), Size, 0.6*[1 1 1], 'filled');
scatter(m_prior(:, 5), m_prior(:, 7), Size, 0.2*[1 1 1]);
% grid on
sp = sp + 1;
axes(ha(sp));
plot([-90 270], [-90 270], '-', 'Color', [1 1 1]*0.5)
hold on
plot([-90 270], [-90 270]+90, '--', 'Color', [0 0 1]*0.5)
plot([-90 270], [-90 270]-90, '--', 'Color', [0 0 1]*0.5)
scatter(mtx(:, 6), mtx(:, 8), Size, 0.6*[1 1 1], 'filled');
hold on
m_prior(~((m_prior(:, 6) > -30) & (m_prior(:, 6) < 210)),:)=[];
scatter(m_prior(:, 6), m_prior(:, 8), Size, 0.2*[1 1 1]);
% grid on
%% FULL MODEL
sp = sp + 1;
axes(ha(sp));
plot([-100 100], [-100 100], '-', 'Color', [1 1 1]*0.5)
hold on
scatter(mtx(:, 5), mtx(:, 7), Size, 0.6*[1 1 1], 'filled');
scatter(m_prior_motor(:, 5), m_prior_motor(:, 7), Size, 0.2*[1 1 1]);
% grid on
sp = sp + 1;
axes(ha(sp));
plot([-90 270], [-90 270], '-', 'Color', [1 1 1]*0.5)
hold on
plot([-90 270], [-90 270]+90, '--', 'Color', [0 0 1]*0.5)
plot([-90 270], [-90 270]-90, '--', 'Color', [0 0 1]*0.5)
scatter(mtx(:, 6), mtx(:, 8), Size, 0.6*[1 1 1], 'filled');
hold on
m_prior_motor(~((m_prior_motor(:, 6) > -30) & (m_prior_motor(:, 6) < 210)),:)=[];
scatter(m_prior_motor(:, 6), m_prior_motor(:, 8), Size, 0.2*[1 1 1]);
% grid on
%% FRONTAL POSITION
for i=1:6
axes(ha(i));
plot(0,0,'r+')
end
%% LABELs and AXIS
for i=1:2:5
axis(ha(i), 'equal')
set(ha(i), 'YTick',[-90, 0, 90], 'XTick',[-90, 0, 90],...
'XLim', [-100, 100], 'YLim', [-100, 100])
end
for i=2:2:6
axis(ha(i), 'equal')
set(ha(i), 'YTick',[-90, 0, 90, 180, 270],...
'XTick',[-90, 0, 90, 180, 270], 'XLim', [-90, 270], 'YLim', [-90, 270])
xtickangle(ha(i),0)
end
set(ha([1:4]),'XTickLabel','')
set(ha,'FontSize',kv.FontSize-1)
x=get(ha(1),'Title'); set(x,'String','Lateral');
x=get(ha(2),'Title'); set(x,'String','Polar');
set(ha(1),'TitleFontWeight','normal');
set(ha(2),'TitleFontWeight','normal');
set(ha(1),'TitleFontSizeMultiplier',1);
set(ha(2),'TitleFontSizeMultiplier',1);
x=get(ha(5),'XLabel'); set(x,'String',' Target angle [deg]');
x=get(ha(6),'XLabel'); set(x,'String',' Target angle [deg]');
x=get(ha(1),'YLabel'); set(x,'String','Response angle [deg]');
x=get(ha(3),'YLabel'); set(x,'String','Response angle [deg]');
x=get(ha(5),'YLabel'); set(x,'String','Response angle [deg]');
titles = {'a) Sensory evidence only', ...
'b) including prior beliefs', ...
'c) including response noise'};
yt = [0.95, 0.63, 0.316];
for i=1:3
annotation(fig,'textbox',...
[0.1 yt(i) 0.8 0.05],...
'String',titles(i),...
'LineStyle','none', ...
'FontWeight', 'normal', ...
'FontSize', kv.FontSize + 1, ...
'HorizontalAlignment', 'left');
end
legend([p1 p2], {'Actual', 'Simulated'}, ...
'Orientation','vertical', ... %'Interpreter', 'latex', ...
'Position',[0.652990351141813 0.62463162317031 0.305810393087726 0.0533707851774237]);
% saveas(fig, 'new_plots/model_stages.eps', 'epsc')
% print(fig, 'fig7', '-dpng', '-r300')
end
%% ------ middlebrooks -------------------------------------
if flags.do_fig7 || flags.do_exp_middlebrooks1999
data_majdak = data_majdak2010('Learn_M');
data_majdak([1:5]) = [];
exp_middlebrooks = [];
if ~flags.do_redo
exp_middlebrooks = amt_cache('get', ...
'exp_middlebrooks1999',flags.cachemode);
end
calibrations = amt_load('barumerli2023', 'barumerli2023_calibration.mat');
calibrations = calibrations.cache.value;
if size(calibrations.sigma, 1) ~= length(calibrations.name)
error('sigma values not enough for the provided subejcts')
end
% remove feature spaces without monaural cues
monaural_none_idx = find(strcmp(calibrations.combination, 'monaural_none'));
if monaural_none_idx > 0
calibrations.combination(monaural_none_idx) = [];
calibrations.sigma(:,monaural_none_idx) = [];
end
% setting
sbj_num = length(calibrations.name);
cal_num = size(calibrations.combination, 1);
num_exp = 50;
if flags.do_redo_fast
num_exp = 5;
exp_middlebrooks = [];
end
if flags.do_test
num_exp = 1;
cal_num = 1;
sbj_num = 1;
exp_middlebrooks = [];
end
if isempty(exp_middlebrooks)
% preprocess templates for each user
amt_disp('Processing subjects'' templates');
for s = 1:sbj_num
amt_disp(['Pre-processing subject #' num2str(s)]);
sofa = amt_load('barumerli2023',['ARI_' upper(calibrations.name{s}) '_hrtf_M_dtf 256.sofa']);
for c = 1:cal_num
[template(c,s), target(c,s)] = ...
barumerli2023_featureextraction(sofa, ...
calibrations.combination{c,1});
end
end
% preallocation for results
amt_disp('Allocating memory for results');
estimations = struct('m', []);
estimations = repmat(estimations, cal_num, ...
sbj_num, sbj_num); % all vs all
for c = 1:cal_num
amt_disp(sprintf('Combination #%i', c));
for s = 1:sbj_num
amt_disp(sprintf('\tSubject #%i', s));
assert(length(calibrations.sigma(s,c).values) == 5, ...
'something is wrong with the calibration file')
sigma_l = calibrations.sigma(s,c).values(1);
sigma_l2 = calibrations.sigma(s,c).values(2);
sigma_mon = calibrations.sigma(s,c).values(3);
sigma_m = calibrations.sigma(s,c).values(4);
sigma_prior = calibrations.sigma(s,c).values(5);
for j = 1:sbj_num
amt_disp(num2str(j));
estimations(c, s, j).m = ...
barumerli2023('template', template(c, s),...
'target', target(c, j), ...
'num_exp', num_exp, ...
'sigma_itd', sigma_l, ...
'sigma_ild', sigma_l2, ...
'sigma_spectral', sigma_mon,...
'sigma_motor', sigma_m, ...
'sigma_prior', sigma_prior);
end
end
end
% compute metrics
for c = 1:size(estimations, 1)
for i = 1:size(estimations, 2)
for j = 1:size(estimations, 3)
metrics(c, i, j) = barumerli2023_metrics(estimations(c, i, j).m, 'middle_metrics');
end
end
end
exp_middlebrooks = metrics;
if ~flags.do_redo_fast && ~flags.do_test
amt_cache('set','exp_middlebrooks1999',exp_middlebrooks);
end
end
metrics_all = exp_middlebrooks;
metrics_all(strcmp(calibrations.combination(:,1), 'monaural_none'), :, :) = [];
num_calib = size(metrics_all, 1);
quants = [0,0.05,0.25,0.5,0.75,0.95,1];
% iterate over calibrations
for c=1:num_calib
metrics = squeeze(metrics_all(c,:,:));
% aggregate metrics
ns = size(metrics,1);
own = logical(eye(ns));
other = not(own);
% code similar to baumgartner2014 - fig9
le_own(c,1).quantiles = quantile([metrics(own).rmsL], quants);
lb_own(c,1).quantiles = quantile([metrics(own).accL], quants);
qe_own(c,1).quantiles = quantile([metrics(own).querr], quants);
pe_own(c,1).quantiles = quantile([metrics(own).rmsP], quants);
pb_own(c,1).quantiles = quantile([metrics(own).accP], quants);
le_own(c,1).mean = mean([metrics(own).rmsL]);
lb_own(c,1).mean = mean([metrics(own).accL]);
qe_own(c,1).mean = mean([metrics(own).querr]);
pe_own(c,1).mean = mean([metrics(own).rmsP]);
pb_own(c,1).mean = mean([metrics(own).accP]);
le_other(c,1).quantiles = quantile([metrics(other).rmsL], quants);
lb_other(c,1).quantiles = quantile([metrics(other).accL], quants);
qe_other(c,1).quantiles = quantile([metrics(other).querr], quants);
pe_other(c,1).quantiles = quantile([metrics(other).rmsP], quants);
pb_other(c,1).quantiles = quantile([metrics(other).accP], quants);
le_other(c,1).mean = mean([metrics(other).rmsL]);
lb_other(c,1).mean = mean([metrics(other).accL]);
qe_other(c,1).mean = mean([metrics(other).querr]);
pe_other(c,1).mean = mean([metrics(other).rmsP]);
pb_other(c,1).mean = mean([metrics(other).accP]);
end
% load reference data
data_middle = 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),quants);
data_baum.pe_own.quantiles = quantile(data_baum.pe_pool(own),quants);
data_baum.pb_own.quantiles = quantile(data_baum.pb_pool(own),quants);
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),quants);
data_baum.pe_other.quantiles = quantile(data_baum.pe_pool(other),quants);
data_baum.pb_other.quantiles = quantile(data_baum.pb_pool(other),quants);
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));
% reijniers
data_reij_temp = exp_reijniers2014('fig2_barumerli2020forum', 'no_plot');
ns = size(data_reij_temp,1);
own = logical(eye(ns));
other = not(own);
data_reij.le_own.quantiles = quantile([data_reij_temp(own).rmsL], quants);
data_reij.lb_own.quantiles = quantile([data_reij_temp(own).accL], quants);
data_reij.qe_own.quantiles = quantile([data_reij_temp(own).querr], quants);
data_reij.pe_own.quantiles = quantile([data_reij_temp(own).rmsP], quants);
data_reij.pb_own.quantiles = quantile([data_reij_temp(own).accP], quants);
data_reij.le_own.mean = mean([data_reij_temp(own).rmsL]);
data_reij.lb_own.mean = mean([data_reij_temp(own).accL]);
data_reij.qe_own.mean = mean([data_reij_temp(own).querr]);
data_reij.pe_own.mean = mean([data_reij_temp(own).rmsP]);
data_reij.pb_own.mean = mean([data_reij_temp(own).accP]);
data_reij.le_other.quantiles = quantile([data_reij_temp(other).rmsL], quants);
data_reij.lb_other.quantiles = quantile([data_reij_temp(other).accL], quants);
data_reij.qe_other.quantiles = quantile([data_reij_temp(other).querr], quants);
data_reij.pe_other.quantiles = quantile([data_reij_temp(other).rmsP], quants);
data_reij.pb_other.quantiles = quantile([data_reij_temp(other).accP], quants);
data_reij.le_other.mean = mean([data_reij_temp(other).rmsL]);
data_reij.lb_other.mean = mean([data_reij_temp(other).accL]);
data_reij.qe_other.mean = mean([data_reij_temp(other).querr]);
data_reij.pe_other.mean = mean([data_reij_temp(other).rmsP]);
data_reij.pb_other.mean = mean([data_reij_temp(other).accP]);
% plot
if flags.do_plot
% calib_plot_order = [3,1,2]; %[lat, dtf, pge]
calib_plot_order = [1,2]; %[dtf, pge]
% spacing
dx = 0.11;
% multiplier for horizontal shift
middle_off = 2;
cdist_init = -1;
reij_off = -3+1;
baum_off = -2+1;
Marker = 's-';
LineColor = [[0.9290, 0.6940, 0.1250]; ...
[0.4940, 0.1840, 0.5560]; ...
[0.4660, 0.6740, 0.1880]; ...
[0.3010, 0.7450, 0.9330]; ...
[0.6350, 0.0780, 0.1840]];
data_middle.Marker = 'ko-';
data_middle.LineColor = 'k';%[1 1 1]*0.3;
% data_majdak.Marker = 'b^-';
% data_majdak.LineColor = 'b';%[0 0 1]*0.3;
data_baum.Marker = 'd-';
data_baum.LineColor = [0.8500 0.3250 0.0980];
data_reij.Marker = 'v-';
data_reij.LineColor = [0 0.4470 0.7410];
mFig = figure;
set(mFig,'Units','points');
set(mFig,'Position',[0, 0, 510, 300]);
tile_left = 0.02;%[left bottom width height]
tile_width = 0.32;
%% SUBPLOT 1
sp = 1;
subplot(1, 3, sp);
pos=get(gca,'OuterPosition');
set(gca,'OuterPosition', [tile_left pos(2) tile_width pos(4)]);
pos=get(gca,'Position');
set(gca,'Position', [pos(1:2) 0.26 pos(4)]);
% reference
local_middlebroxplot(gca, 1-middle_off*dx,data_middle.le_own, data_middle.Marker, kv.MarkerSize, data_middle.LineColor, data_middle.LineColor);
local_middlebroxplot(gca, 2-middle_off*dx,data_middle.le_other, data_middle.Marker, kv.MarkerSize, data_middle.LineColor, data_middle.LineColor);
% baseline
% local_middlebroxplot(ax, 1-majdak_off*dx,data_majdak.le, data_majdak.Marker, kv.MarkerSize, data_majdak.LineColor, data_majdak.LineColor);
% simulation
cdist = cdist_init;
for c=calib_plot_order
local_middlebroxplot(gca, 1+cdist*dx,le_own(c,1), Marker, kv.MarkerSize, LineColor(c,:), 'w');
local_middlebroxplot(gca, 2+cdist*dx,le_other(c,1), Marker, kv.MarkerSize, LineColor(c,:), 'w');
cdist = cdist+1;
end
% reijniers2014
local_middlebroxplot(gca, 1-reij_off*dx,data_reij.le_own, data_reij.Marker, kv.MarkerSize, data_reij.LineColor, 'w');
local_middlebroxplot(gca, 2-reij_off*dx,data_reij.le_other, data_reij.Marker, kv.MarkerSize, data_reij.LineColor, 'w');
%
ylabel('Lateral error [deg]','FontSize',kv.FontSize)
set(gca,'YLim',[0 45],'YTick', 0:10:40,'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
%% SUBPLOT 2
sp = sp +1;
subplot(1, 3, sp);
pos=get(gca,'OuterPosition');
set(gca,'OuterPosition', [tile_left*2 + tile_width pos(2) tile_width pos(4)]);
pos=get(gca,'Position');
set(gca,'Position', [pos(1:2) 0.26 pos(4)]);
% reference
local_middlebroxplot(gca, 1-middle_off*dx, data_middle.pe_own, data_middle.Marker, kv.MarkerSize, data_middle.LineColor, data_middle.LineColor);
local_middlebroxplot(gca, 2-middle_off*dx, data_middle.pe_other, data_middle.Marker, kv.MarkerSize, data_middle.LineColor, data_middle.LineColor);
% baseline
% local_middlebroxplot(ax, 1-majdak_off*dx, data_majdak.pe, data_majdak.Marker, kv.MarkerSize, data_majdak.LineColor, data_majdak.LineColor);
% simulations
cdist = cdist_init;
for c=calib_plot_order
local_middlebroxplot(gca, 1+cdist*dx, pe_own(c,1), Marker, kv.MarkerSize, LineColor(c,:),'w');
local_middlebroxplot(gca, 2+cdist*dx, pe_other(c,1), Marker, kv.MarkerSize, LineColor(c,:),'w');
cdist = cdist + 1;
end
% reijniers2014
local_middlebroxplot(gca, 1-reij_off*dx, data_reij.pe_own,data_reij.Marker, kv.MarkerSize, data_reij.LineColor,'w');
local_middlebroxplot(gca, 2-reij_off*dx, data_reij.pe_other,data_reij.Marker, kv.MarkerSize, data_reij.LineColor,'w');
% baumgartner2014
local_middlebroxplot(gca, 1-baum_off*dx, data_baum.pe_own,data_baum.Marker, kv.MarkerSize, data_baum.LineColor,'w');
local_middlebroxplot(gca, 2-baum_off*dx, data_baum.pe_other,data_baum.Marker, kv.MarkerSize, data_baum.LineColor,'w');
ylabel('Polar error [deg]','FontSize',kv.FontSize)
set(gca,'YLim',[0 65],'YTick', 0:10:60,'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
%% SUBPLOT 3
sp = sp +1;
sp_ref = subplot(1, 3, sp);
pos=get(gca,'OuterPosition');
set(gca,'OuterPosition', [tile_left*3 + tile_width*2 pos(2) tile_width pos(4)]);
pos=get(gca,'Position');
set(gca,'Position', [pos(1:2) 0.26 pos(4)]);
% reference
middle = local_middlebroxplot(gca, 1-middle_off*dx,data_middle.qe_own, data_middle.Marker, kv.MarkerSize, data_middle.LineColor, data_middle.LineColor);
local_middlebroxplot(gca, 2-middle_off*dx,data_middle.qe_other, data_middle.Marker, kv.MarkerSize, data_middle.LineColor, data_middle.LineColor);
% baseline
% baseline = local_middlebroxplot(ax, 1-majdak_off*dx,data_majdak.qe, data_majdak.Marker, kv.MarkerSize, data_majdak.LineColor, data_majdak.LineColor);
% simulations
cdist = cdist_init;
for c=calib_plot_order
baru(1,c) = local_middlebroxplot(gca, 1+cdist*dx,qe_own(c,1), Marker, kv.MarkerSize, LineColor(c,:),'w');
local_middlebroxplot(gca, 2+cdist*dx,qe_other(c,1), Marker, kv.MarkerSize, LineColor(c,:),'w');
cdist = cdist + 1;
end
% reijniers2014
reij = local_middlebroxplot(gca, 1-reij_off*dx,data_reij.qe_own, data_reij.Marker,kv.MarkerSize, data_reij.LineColor,'w');
local_middlebroxplot(gca, 2-reij_off*dx,data_reij.qe_other, data_reij.Marker,kv.MarkerSize, data_reij.LineColor,'w');
% baumgartner2014
baum = local_middlebroxplot(gca, 1-baum_off*dx,data_baum.qe_own, data_baum.Marker,kv.MarkerSize, data_baum.LineColor,'w');
local_middlebroxplot(gca, 2-baum_off*dx,data_baum.qe_other, data_baum.Marker,kv.MarkerSize, data_baum.LineColor,'w');
ylabel('Quadrant error [%]','FontSize',kv.FontSize)
set(gca,'YLim',[-5 55],'YTick', 0:10:50,'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
for c=calib_plot_order
switch lower(calibrations.combination{c,1})
case 'dtf'
labels{1,c} = 'MP variant';
case 'pge'
labels{1,c} = 'GP variant';
end
end
leg = legend(sp_ref, [middle, baru(calib_plot_order), baum, reij], horzcat({'Actual data'},labels, {'baumgartner2014'}, {'reijniers2014'}));
set(leg,'FontSize', kv.FontSize - 2, 'Units','centimeters', ... %leg.Interpreter = 'latex';
'Units', 'normalized', 'Position', ...
[0.0854369511609941 0.711535203689433 0.211155508622859 0.187531802247802]);
% saveas(mFig, 'new_plots/middlebrooks.eps', 'epsc')
% print(mFig, 'fig8', '-dpng', '-r600')
end
end
%% ------ exp_macpherson -------------------------------------
if flags.do_fig8 || flags.do_exp_macpherson2003
exp_macpherson = [];
if ~flags.do_redo
exp_macpherson = amt_cache('get', ...
'exp_macpherson2003',flags.cachemode);
end
calibrations = amt_load('barumerli2023', 'barumerli2023_calibration.mat');
calibrations = calibrations.cache.value;
if size(calibrations.sigma, 1) ~= length(calibrations.name)
error('sigma values not enough for the provided subejcts')
end
% remove features spaces without monaural features
monaural_none_idx = find(strcmp(calibrations.combination, 'monaural_none'));
if monaural_none_idx > 0
calibrations.combination(monaural_none_idx) = [];
calibrations.sigma(:,monaural_none_idx) = [];
end
% Settings
num_exp = 50;
num_sbj = length(calibrations.name);
num_calib = size(calibrations.combination, 1);
if flags.do_redo_fast
exp_macpherson = [];
num_exp = 2;
end
if flags.do_test
exp_macpherson = [];
num_exp = 1;
num_calib = 1;
num_sbj = 1;
end
if isempty(exp_macpherson)
sofa = amt_load('barumerli2023',['ARI_' upper(calibrations.name{1}) '_hrtf_M_dtf 256.sofa']);
% 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 = sofa.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));
if flags.do_test
density(2:end) = []; % ripples/oct
depth(2:end) = [];
end
% preprocess templates for each user
for i = 1:num_sbj
amt_disp(['Processing subject ', num2str(i)]);
for c = 1:num_calib
amt_disp(['Pre-processing calibration #' num2str(c)]);
sofa = amt_load('barumerli2023',['ARI_' upper(calibrations.name{i}) '_hrtf_M_dtf 256.sofa']);
% extract directions
% filter targets' coordinates
% convert from spherical to horizontal-polar coordinates
horpolar_coords = barumerli2023_coordinates(sofa).return_positions('horizontal-polar');
% polar in [60, 120]
% lateral = 0
idx = find(((horpolar_coords(:, 2) >= -60 ...
& 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)]);
[template(c, i), target_flat(c, i)] = ...
barumerli2023_featureextraction(sofa, ...
'targ_az', sofa.SourcePosition(idx, 1), ...
'targ_el', sofa.SourcePosition(idx, 2), ...
calibrations.combination{c,1});
amt_disp('Densities conditions');
for j = 1:length(density)
target_exp1(c, i, j) = ...
barumerli2023_featureextraction(sofa, 'target', 'source', ...
'source_ir', squeeze(sexp1(:, j)), 'source_fs', fs, ...
'targ_az', sofa.SourcePosition(idx, 1), ...
'targ_el', sofa.SourcePosition(idx, 2), ...
calibrations.combination{c,1});
end
amt_disp('Depth conditions');
for j = 1:length(depth)
target_exp2(c, i, j) = ...
barumerli2023_featureextraction(sofa, 'target', 'source', ...
'source_ir', squeeze(sexp2(:, j)), 'source_fs', fs, ...
'targ_az', sofa.SourcePosition(idx, 1), ...
'targ_el', sofa.SourcePosition(idx, 2), ...
calibrations.combination{c,1});
end
end
end
% preallocation for results
amt_disp('Allocating memory for results');
estimations = struct('m',[]);
est_expflat = repmat(estimations, num_calib, num_sbj);
est_exp1 = repmat(estimations, num_calib, ...
num_sbj,length(density));
est_exp2 = repmat(estimations, num_calib, ...
num_sbj,length(depth));
% data for prior computation
data_majdak = data_majdak2010('Learn_M');
data_majdak([1:5]) = [];
% simulations
for i = 1:num_sbj
for c = 1:num_calib
amt_disp(sprintf('\tCalibration #%i', c));
assert(length(calibrations.sigma(i,c).values) == 5, 'something is wrong with the calibration file')
sigma_l = calibrations.sigma(i,c).values(1);
sigma_l2 = calibrations.sigma(i,c).values(2);
sigma_mon = calibrations.sigma(i,c).values(3);
sigma_m = calibrations.sigma(i,c).values(4);
sigma_prior = calibrations.sigma(i,c).values(5);
amt_disp(sprintf('\tSubject #%i', i));
% flat spectrum estimations
est_expflat(c, i, 1).m = barumerli2023('template', template(c, i), 'target', target_flat(c, i), ...
'num_exp', num_exp, ...
'sigma_itd', sigma_l, ...
'sigma_ild', sigma_l2, ...
'sigma_spectral', sigma_mon,...
'sigma_motor', sigma_m, ...
'sigma_prior', sigma_prior);
% rippled estimations
for j = 1:length(density)
est_exp1(c, i, j).m = barumerli2023('template', template(c, i), ...
'target', target_exp1(c, i, j), ...
'num_exp', num_exp, ...
'sigma_itd', sigma_l, ...
'sigma_ild', sigma_l2, ...
'sigma_spectral', sigma_mon,...
'sigma_motor', sigma_m, ...
'sigma_prior', sigma_prior);
end
for j =1:length(depth)
est_exp2(c, i, j).m = barumerli2023('template', template(c, i), ...
'target', target_exp2(c, i, j), ...
'num_exp', num_exp, ...
'sigma_itd', sigma_l, ...
'sigma_ild', sigma_l2, ...
'sigma_spectral', sigma_mon,...
'sigma_motor', sigma_m, ...
'sigma_prior', sigma_prior);
end
end
end
% metrics
% allocate memory for results
% aggregate over different lateral angles
pe_exp1 = zeros(num_calib, num_sbj, length(density));
pe_exp2 = zeros(num_calib, num_sbj, length(depth));
pe_flat = zeros(num_calib, num_sbj, 1);
for c = 1:num_calib
for i = 1:num_sbj
% compute iterative regression (see Macpherson paper and localizationerror.m)
[f,r] = localizationerror(est_expflat(c,i).m, 'sirpMacpherson2000');
pe_flat(c,i) = localizationerror(est_expflat(c,i).m, f, r, 'perMacpherson2003');
for j = 1:length(density)
pe_exp1(c, i, j) = localizationerror(est_exp1(c, i, j).m, f, r, 'perMacpherson2003');
end
for j = 1:length(depth)
pe_exp2(c, i, j) = localizationerror(est_exp2(c ,i, j).m, f, r, 'perMacpherson2003');
end
end
end
% save cache
exp_macpherson.pe_flat = pe_flat;
exp_macpherson.pe_exp1 = pe_exp1;
exp_macpherson.pe_exp2 = pe_exp2;
if ~flags.do_redo_fast && ~flags.do_test
amt_cache('set','exp_macpherson2003', exp_macpherson);
end
end
% Original data:
data = data_macpherson2003;
% Reijniers2014's data
data_reij = exp_reijniers2014('fig4_barumerli2020forum', 'no_plot');
% 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
% reijniers2014
data_reij.pe_exp1 = data_reij.pe_exp1 - repmat(data_reij.pe_flat(:), 1, size(data_reij.pe_exp1, 2));
data_reij.pe_exp2 = data_reij.pe_exp2 - repmat(data_reij.pe_flat(:), 1, size(data_reij.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
% 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]);
% reijniers data
data_reij.quart_pe_flat = quantile(data_reij.pe_flat,[.25 .50 .75]);
data_reij.quart_pe_exp1 = quantile(data_reij.pe_exp1,[.25 .50 .75]);
data_reij.quart_pe_exp2 = quantile(data_reij.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]);
for c = 1:num_calib
% simulations data
pe_flat = exp_macpherson.pe_flat(c,:);
pe_exp1 = squeeze(exp_macpherson.pe_exp1(c,:,:));
pe_exp2 = squeeze(exp_macpherson.pe_exp2(c,:,:));
% simulations
pe_exp1 = pe_exp1 - repmat(pe_flat(:), 1, size(pe_exp1, 2) );
pe_exp2 = pe_exp2 - repmat(pe_flat(:), 1, size(pe_exp2, 2) );
% simulations
quart_pe_flat(c,:) = quantile(pe_flat,[.25 .50 .75]);
quart_pe_exp1(c,:,:) = quantile(pe_exp1,[.25 .50 .75]);
quart_pe_exp2(c,:,:) = quantile(pe_exp2,[.25 .50 .75]);
end
% plot
if flags.do_plot
calib_plot_order = [1,2]; % lat, dtf, pge
dx = 1.05;
FontSize = kv.FontSize;
MarkerSize = kv.MarkerSize;
LineColor = [[0.9290, 0.6940, 0.1250]; ...
[0.4940, 0.1840, 0.5560]; ...
[0.4660, 0.6740, 0.1880]; ...
[0.3010, 0.7450, 0.9330]; ...
[0.6350, 0.0780, 0.1840]];
data.Marker = 'ko-';
data.LineColor = [1 1 1]*0;
data_reij.Marker = 'v-';
data_reij.LineColor = [0 0.4470 0.7410];
data_baum.Marker = 'd-';
data_baum.LineColor = [0.8500 0.3250 0.0980];
% Exp1
mFig = figure;
set(mFig,'Units','points', 'Position',[0 0 500 265]);
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-');
set(mach,'MarkerSize',MarkerSize, 'Color', data.LineColor, ...
'MarkerFaceColor', data.LineColor);
hold on
for c = calib_plot_order
baru(1,c) = errorbar(data.density/dx,squeeze(quart_pe_exp1(c, 2,:)),...
squeeze(quart_pe_exp1(c,2,:) - quart_pe_exp1(c,1,:)),...
squeeze(quart_pe_exp1(c,3,:) - quart_pe_exp1(c,2,:)),...
's-');
set(baru(1,c),'MarkerSize',MarkerSize, 'Color', LineColor(c,:),'MarkerFaceColor','w');
end
hold on
reij = errorbar(data.density*dx,data_reij.quart_pe_exp1(2,:),...
data_reij.quart_pe_exp1(2,:) - data_reij.quart_pe_exp1(1,:),...
data_reij.quart_pe_exp1(3,:) - data_reij.quart_pe_exp1(2,:),...
'v--');
set(reij,'MarkerSize',MarkerSize, 'Color', data_reij.LineColor,'MarkerFaceColor','w');
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--');
set(baum,'MarkerSize',MarkerSize, 'Color', data_baum.LineColor,'MarkerFaceColor','w');
set(gca,'XScale','log','YMinorTick','on')
set(gca,'XLim',[0.25/1.2 8*1.2],'XTick',data.density,'YLim',[-16 70],'FontSize',FontSize)
xlabel('Ripple Density [ripples/octave]','FontSize',FontSize)
ylabel({'Increase in';'Polar Error Rate [%]'},'FontSize',FontSize)
% Exp2
sp_ref = subplot(2,8,9:13);
x=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-');
set(x,'MarkerSize',MarkerSize, 'Color', data.LineColor, ...
'MarkerFaceColor', data.LineColor);
hold on
for c = calib_plot_order
x=errorbar(data.depth-0.5,squeeze(quart_pe_exp2(c, 2,:)),...
squeeze(quart_pe_exp2(c,2,:) - quart_pe_exp2(c,1,:)),...
squeeze(quart_pe_exp2(c,3,:) - quart_pe_exp2(c,2,:)),...
's-');
set(x,'MarkerSize',MarkerSize, 'Color', LineColor(c,:),'MarkerFaceColor','w');
end
hold on
x=errorbar(data.depth+1,data_reij.quart_pe_exp2(2,:),...
data_reij.quart_pe_exp2(2,:) - data_reij.quart_pe_exp2(1,:),...
data_reij.quart_pe_exp2(3,:) - data_reij.quart_pe_exp2(2,:),...
'v--');
set(x,'MarkerSize',MarkerSize, 'Color', data_reij.LineColor,'MarkerFaceColor','w');
x=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--');
set(x,'MarkerSize',MarkerSize, 'Color', data_baum.LineColor,'MarkerFaceColor','w');
set(gca,'XLim',[data.depth(1)-5 data.depth(end)+5],'XTick',data.depth,...
'YLim',[-16 70],'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(2,8,14:16)
x=errorbar(-1,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-');
set(x,'MarkerSize',MarkerSize, 'Color', data.LineColor, ...
'MarkerFaceColor', data.LineColor);
hold on
for c = calib_plot_order
x=errorbar(-0.5 + (c-1)*0.5,quart_pe_flat(c,2),...
quart_pe_flat(c,2) - quart_pe_flat(c,1),...
quart_pe_flat(c,3) - quart_pe_flat(c,2),...
's-');
set(x,'MarkerSize',MarkerSize, 'Color', LineColor(c,:),'MarkerFaceColor','w');
end
hold on
x=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--');
set(x,'MarkerSize',MarkerSize, 'Color', data_baum.LineColor,'MarkerFaceColor','w');
x=errorbar(1,data_reij.quart_pe_flat(2),...
data_reij.quart_pe_flat(2) - data_reij.quart_pe_flat(1),...
data_reij.quart_pe_flat(3) - data_reij.quart_pe_flat(2),...
'd--');
set(x,'MarkerSize',MarkerSize, 'Color', data_reij.LineColor,'MarkerFaceColor','w');
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
for c = calib_plot_order
switch lower(calibrations.combination{c,1})
case 'dtf'
labels{1,c} = 'MP variant';
case 'pge'
labels{1,c} = 'GP variant';
end
end
leg = legend(sp_ref, [mach, baru(calib_plot_order), baum, reij], horzcat({'Actual data'}, labels(calib_plot_order), {'baumgartner2014'}, {'reijniers2014'}));
set(leg,'FontSize',FontSize - 2, 'Units','normalized', ... % leg.Interpreter = 'latex';
'Orientation','horizontal', 'Position',...
[0.147112074200069 0.944044062817491 0.739274312186318 0.0430594892744974]);
% Overall correlation between actual and predicted median values
for c=calib_plot_order
m_pe_pred = [squeeze(quart_pe_exp1(c,2,:))' squeeze(quart_pe_exp2(c,2,:))'];
m_pe_actual = [data.quart_pe_exp1(2,:) data.quart_pe_exp2(2,:)];
r = corrcoef(m_pe_pred,m_pe_actual);
r_sqr = r(2);
amt_disp('Correlation between actual and predicted median values (15 conditions):')
amt_disp(sprintf('%s: r = %0.2f', labels{1,c}, r_sqr))
end
% saveas(mFig, 'new_plots/macpherson.eps', 'epsc')
% print(mFig, 'fig9', '-dpng', '-r600')
end
end
function hg = local_middlebroxplot(ax, x, data, Marker, MarkerSize, LineColor, FaceColor)
lilen = 0.05; % length of horizontal lines
hb=[];
% Symbols
i=1; hb(i) = plot(ax, x, data.quantiles(1),'x','MarkerSize',MarkerSize, 'MarkerEdgeColor', LineColor, 'MarkerFaceColor', LineColor); % min
hold on
i=i+1; hb(i) = plot(ax, x,data.quantiles(7),'x','MarkerSize',MarkerSize, 'MarkerEdgeColor', LineColor, 'MarkerFaceColor', LineColor); % max
% Horizontal lines
i=i+1; hb(i:(i+1)) = line(ax, x+0.5*[-lilen,lilen],repmat(data.quantiles(2),2),'Color',LineColor); % lower whisker
i=i+2; hb(i:(i+1)) = line(ax, x+[-lilen,lilen],repmat(data.quantiles(3),2),'Color',LineColor); % 25% Quartile
i=i+2; hb(i:(i+1)) = line(ax, x+[-lilen,lilen],repmat(data.quantiles(4),2),'Color',LineColor); % Median
i=i+2; hb(i:(i+1)) = line(ax, x+[-lilen,lilen],repmat(data.quantiles(5),2),'Color',LineColor); % 75% Quartile
i=i+2; hb(i:(i+1)) = line(ax, x+0.5*[-lilen,lilen],repmat(data.quantiles(6),2),'Color',LineColor); % upper whisker
% Vertical lines
i=i+2; hb(i:(i+1)) = line(ax, [x,x],data.quantiles(2:3),'Color',LineColor); % connector lower whisker
i=i+2; hb(i:(i+1)) = line(ax, [x,x],data.quantiles(5:6),'Color',LineColor); % connector upper whisker
i=i+2; hb(i:(i+1)) = line(ax, [x,x]-lilen,data.quantiles([3,5]),'Color',LineColor); % left box edge
i=i+2; hb(i:(i+1)) = line(ax, [x,x]+lilen,data.quantiles([3,5]),'Color',LineColor); % left box edge
% middle value
i=i+1; hb(i) = plot(x,data.mean, Marker,'MarkerSize', MarkerSize, 'MarkerFaceColor', FaceColor, 'MarkerEdgeColor',LineColor);
% create a group to avoid issues with the legend
% https://stackoverflow.com/questions/12894652/matlab-how-to-make-a-custom-legend
hg = hggroup;
set(hb,'Parent',hg);
%set(get(get(hg,'Annotation'),'LegendInformation'),...
% 'IconDisplayStyle','off');
function [x,y,x_sign] = lambert_area_projection(az, el)
% az and el has to be in radiants!!!!
% lambert equal area projection
x_sign = 1;
if abs(az) > pi/2; az = az - pi; x_sign = -1; end
k = sqrt(2 ./ (eps + 1 + (cos(el) .* cos(az))));
x = x_sign * k * 1 .* cos(el) .* sin(az) ./ sqrt(2); % ./sqrt(2) normalizing
y = k * 1 .* sin(el) ./ sqrt(2);
function rau=rau(X,N,opt)
% RAU rationalized arcsine transform
% RAU(X,N) transforms the number of correct responses X to the
% rationalized arcsine (rau). N gives the number of repetitions.
%
% This function allows to use ANOVA statistics with percent correct scores
% because: 1) RAUs are normally distributed; 2) mean and variance of RAUs
% are not correlated with eachother; and 3) likelihood that a score will
% increase/decrease will remain constant over the range.
%
% RAU=RAU(X,N,opt) defines one of the following options:
% 'Pc' ... X is given in percent correct scores (0..100%)
% 'X' ... X is given in the number of correct responses (default)
%
% The formula are based on Sherbecoe and Studebaker,
% Int. J. of Audiology 2004; 43; 442-448
%
% See also IRAU.
% 30.8.2007, Piotr Majdak
%
if exist('opt','var')
if strcmp(upper(opt),'PC')
X=X/100*N;
elseif strcmp(upper(opt),'X')
else
error('OPT must be Pc (=percent correct) or X (=number of correct responses)');
end
end
th=asin(sqrt(X/(N+1)))+asin(sqrt((X+1)/(N+1)));
rau=146/pi*(th)-23;
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