The Auditory Modeling Toolbox
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EXP_ZIEGELWANGER2013 - Figures from Ziegelwanger and Majdak (2013)
Program code:
function varargout=exp_ziegelwanger2013(varargin)
%EXP_ZIEGELWANGER2013 Figures from Ziegelwanger and Majdak (2013)
% Usage: data = exp_ziegelwanger2013(flag)
%
% EXP_ZIEGELWANGER2013(flags) reproduces figures of the paper from
% Ziegelwanger and Majdak (2013).
%
% Optional fields of output data structure:
%
% The following flags can be specified:
%
% 'plot' Plot the output of the experiment. This is the default.
%
% 'noplot' Don't plot, print results in the console.
%
% 'cached' Reload previously calculated results. This is the default.
%
% 'redo' Recalculate results.
%
% 'fig1a' Reproduce Fig. 1 from ziegelwanger2013:
% Model parameters (sphere radius, sphere center) resulting
% from fitting the off-axis model to HRTFs of human
% listeners.
%
% 'fig1b' Reproduce Fig. 1 from majdak2013:
%
% Left panel:
% Normalized HRIRs of NH89 (ARI database). Sound source was
% placed 45 deg left in the horizontal plane.
% Solid line: for the left ear
% Dashed line: for the right ear
% Vertical lines: Estimated TOAs
% Arrows: Resulting ITDs from the corresponding TOAs
% Circles, Red: Minimum-Phase Cross-Correlation Method
% Triangle, Green: Time-Position of the HRIR-Maximum
% Diamonds, Blue: Centroid of the HRIR
% Squares, Magenta: Average Group Delay (1-5 kHz)
%
% Right panel:
% Estimated TOAs of NH89 (ARI database) in the horizontal
% interaural plane.
% Black: Minimum-Phase Cross-Correlation Method
% Blue: Time-Position of the HRIR-Maximum
% Green: Centroid of the HRIR
% Red: Average Group Delay (1-5 kHz)
%
% 'fig2b' Reproduce Fig. 2 from majdak2013:
%
% Right panel:
% Estimated TOAs, detected outliers and outlier adjusted set
% of TOAs for NH89 (ARI database) in the horizontal plane.
% Line: Estimated TOAs
% Triangles (down), Blue: Detected outliers for the azimuthal
% slope criterion
% Triangles (up), Blue: Detected outliers for the sagittal
% variance criterion
% Dots, Red: Outlier-adjusted set
%
% 'fig3b' Reproduce Fig. 3 from majdak2013:
%
% Left panel:
% Model parameters (sphere radius, ear position) resulting
% from fitting the on-axis model to HRTFs of a rigid sphere.
% Squares: for the left ear
% Diamonds: for the right ear
% Dashed lines: set values used in the numerical HRTF
% calculation
%
% Right panel:
% Model parameters (sphere radius) resulting from fitting the
% on-axis model to HRTFs of an off-axis placed rigid sphere.
%
%
% Requirements:
% -------------
%
% 1) SOFA API from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)
%
% 2) Optimization Toolbox for Matlab
%
% 3) Data in hrtf/ziegelwanger2013
%
% Examples:
% ---------
%
% To display Fig. 1a, use :
%
% exp_ziegelwanger2013('fig1a');
%
% To display Fig. 1b, use :
%
% exp_ziegelwanger2013('fig1b');
%
% To display Fig. 2b, use :
%
% exp_ziegelwanger2013('fig2b');
%
% To display Fig. 3b, use :
%
% exp_ziegelwanger2013('fig3b');
%
% See also: ziegelwanger2013, ziegelwanger2013_onaxis,
% ziegelwanger2013_offaxis, data_ziegelwanger2013
%
% References:
% P. Majdak and H. Ziegelwanger. Continuous-direction model of the
% broadband time-of-arrival in the head-related transfer functions. In
% ICA 2013 Montreal, volume 19, page 050016, Montreal, Canada, 2013. ASA.
%
% H. Ziegelwanger and P. Majdak. Modeling the broadband time-of-arrival
% of the head-related transfer functions for binaural audio. In
% Proceedings of the 134th Convention of the Audio Engineering Society,
% page 7, Rome, 2013.
%
%
% Url: http://amtoolbox.sourceforge.net/data/amt-test/htdocs/amt-0.10.0/doc/experiments/exp_ziegelwanger2013.php
% Copyright (C) 2009-2020 Piotr Majdak and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 0.10.0
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR: Harald Ziegelwanger, Acoustics Research Institute, Vienna,
% Austria
%% ------ Check input options --------------------------------------------
definput.flags.type = {'missingflag',...
'fig1a','fig1b','fig2b','fig3b'};
definput.flags.plot = {'plot','noplot'};
definput.import={'amt_cache'}; % get the flags of amt_cache
% Parse input options
[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;
%% Figure 1b
if flags.do_fig1b
data=data_ziegelwanger2013('NH89',flags.cachemode);
subplot(122)
%---------------------------Threshold---------------------------
[~,tmp]=ziegelwanger2013(data,1,0);
MAX=tmp.toa;
%---------------------------Centroid----------------------------
[~,tmp]=ziegelwanger2013(data,2,0);
CTD=tmp.toa;
%---------------------------Groupdelay--------------------------
[~,tmp]=ziegelwanger2013(data,3,0);
AGD=tmp.toa;
%---------------------------Minimal-Phase-----------------------
[~,tmp]=ziegelwanger2013(data,4,0);
MCM=tmp.toa;
clear tmp
plot_ziegelwanger2013(data,MCM(:,1),4,[0 0 0]/255,0,1,1,'-',1);
hold on
plot_ziegelwanger2013(data,MAX(:,1),4,[0 0 80]/255,0,1,1,'--',1);
plot_ziegelwanger2013(data,CTD(:,1),4,[50 220 50]/255,0,1,1,'-',1);
plot_ziegelwanger2013(data,AGD(:,1),4,[250 80 80]/255,0,1,1,'--',1);
xlim([-10 370])
ylim([0.65 2.05])
grid off
legend(' MCM',' MAX',' CTD',' AGD','Location','NorthWest');
legend boxoff
xlabel('Azimuth (deg) ')
ylabel('TOA (ms) ')
% set(gca,'FontSize',ticklabelsize,'LineWidth',bw,'LineWidth',bw);%,'YTick',[-0.8 -0.4 0 0.4 0.8]
title('');
subplot(121)
time=(0:data.API.N-1)/data.Data.SamplingRate*1000;
MAX=round(MAX);
CTD=round(CTD);
AGD=round(AGD);
MCM=round(MCM);
idx=ARI_FindPosition(data,85,0);
fprintf(['MAX: ITD is ' num2str(time(diff(MAX(idx,:),1,2))) ' ms\n'])
fprintf(['CTD: ITD is ' num2str(time(diff(CTD(idx,:),1,2))) ' ms\n'])
fprintf(['AGD: ITD is ' num2str(time(diff(AGD(idx,:),1,2))) ' ms\n'])
fprintf(['MCM: ITD is ' num2str(time(diff(MCM(idx,:),1,2))) ' ms\n'])
plot(time,squeeze(data.Data.IR(idx,1,:))/max(abs(data.Data.IR(idx,1,:))),'k-');
hold on
plot(time,squeeze(data.Data.IR(idx,2,:))/max(abs(data.Data.IR(idx,2,:))),'k--')
h=stem([time(MCM(idx,1)) time(MCM(idx,1))],[-2 data.Data.IR(idx,1,MCM(idx,1))/max(abs(data.Data.IR(idx,1,:)))],'r-','BaseValue',-1);
stem([time(CTD(idx,1)) time(CTD(idx,1))],[-2 data.Data.IR(idx,1,CTD(idx,1))/max(abs(data.Data.IR(idx,1,:)))],'g-','BaseValue',-1)
stem([time(MAX(idx,1)) time(MAX(idx,1))],[-2 data.Data.IR(idx,1,MAX(idx,1))/max(abs(data.Data.IR(idx,1,:)))],'b-','BaseValue',-1)
stem([time(AGD(idx,1)) time(AGD(idx,1))],[-2 data.Data.IR(idx,1,AGD(idx,1))/max(abs(data.Data.IR(idx,1,:)))],'m-','BaseValue',-1)
stem([time(MCM(idx,2)) time(MCM(idx,2))],[-2 data.Data.IR(idx,2,MCM(idx,2))/max(abs(data.Data.IR(idx,2,:)))],'r-','BaseValue',-1)
stem([time(CTD(idx,2)) time(CTD(idx,2))],[-2 data.Data.IR(idx,2,CTD(idx,2))/max(abs(data.Data.IR(idx,2,:)))],'g-','BaseValue',-1)
stem([time(AGD(idx,2)) time(AGD(idx,2))],[-2 data.Data.IR(idx,2,AGD(idx,2))/max(abs(data.Data.IR(idx,2,:)))],'m-','BaseValue',-1)
stem([time(MAX(idx,2)) time(MAX(idx,2))],[-2 data.Data.IR(idx,2,MAX(idx,2))/max(abs(data.Data.IR(idx,2,:)))],'b-','BaseValue',-1)
plot(time(MAX(idx,1)),data.Data.IR(idx,1,MAX(idx,1))/max(abs(data.Data.IR(idx,1,:))),'b^','MarkerFaceColor','b')
plot(time(MCM(idx,1)),data.Data.IR(idx,1,MCM(idx,1))/max(abs(data.Data.IR(idx,1,:))),'ro','MarkerFaceColor','r')
plot(time(CTD(idx,1)),data.Data.IR(idx,1,CTD(idx,1))/max(abs(data.Data.IR(idx,1,:))),'gd','MarkerFaceColor','g')
plot(time(AGD(idx,1)),data.Data.IR(idx,1,AGD(idx,1))/max(abs(data.Data.IR(idx,1,:))),'ms','MarkerFaceColor','m')
plot(time(MAX(idx,2)),data.Data.IR(idx,2,MAX(idx,2))/max(abs(data.Data.IR(idx,2,:))),'b^','MarkerFaceColor','b')
plot(time(MCM(idx,2)),data.Data.IR(idx,2,MCM(idx,2))/max(abs(data.Data.IR(idx,2,:))),'ro','MarkerFaceColor','r')
plot(time(CTD(idx,2)),data.Data.IR(idx,2,CTD(idx,2))/max(abs(data.Data.IR(idx,2,:))),'gd','MarkerFaceColor','g')
plot(time(AGD(idx,2)),data.Data.IR(idx,2,AGD(idx,2))/max(abs(data.Data.IR(idx,2,:))),'ms','MarkerFaceColor','m')
xlim([0.4 2.1])
ylim([-1.1 1.1])
xlabel('Time (ms) ')
ylabel('Amplitude ')
% set(gca,'FontSize',ticklabelsize,'LineWidth',bw,'LineWidth',bw,'XTick',[2.5 3 3.5 4]);
set(get(h,'Baseline'),'Visible','off')
end
%% Figure 2b
if flags.do_fig2b
data=data_ziegelwanger2013('NH89',flags.cachemode);
ch=1;
p0_onaxis=[[0.0875; pi/2; 0; 0.0001] [0.0875; -pi/2; 0; 0.0001]];
p0_onaxis=transpose(p0_onaxis);
p_onaxis=zeros(size(p0_onaxis));
p0_offaxis=zeros(2,7);
p_offaxis=p0_offaxis;
indicator=zeros(data.API.M,data.API.R);
indicator_hor=indicator;
indicator_sag=indicator;
pos=zeros(data.API.M,8);
pos(:,1:2)=data.SourcePosition(:,1:2);
[pos(:,6),pos(:,7)]=sph2hor(data.SourcePosition(:,1),data.SourcePosition(:,2));
pos(:,8)=cumsum(ones(data.API.M,1));
[~,tmp]=ziegelwanger2013(data,4,0);
toaEst=tmp.toa;
% Outlier detection: smooth TOA in horizontal planes
epsilon=5;
slope=zeros(data.API.M,1);
for ele=min(pos(:,2)):epsilon:max(pos(:,2)) %calculate slope for each elevation along azimuth
idx=find(pos(:,2)>ele-epsilon/2 & pos(:,2)<=ele+epsilon/2);
if numel(idx)>1
idx(length(idx)+1)=idx(1);
slope(idx(1:end-1),1)=diff(toaEst(idx,ch))./abs(diff(pos(idx,1)));
end
end
sloperms=sqrt(sum(slope.^2)/length(slope));
if sloperms<30/(length(find(pos(:,2)==0))/2)
sloperms=30/(length(find(pos(:,2)==0))/2);
end
for ele=min(pos(:,2)):epsilon:max(pos(:,2))
idx=find(pos(:,2)>ele-epsilon/2 & pos(:,2)<=ele+epsilon/2);
for ii=1:length(idx)-1
if abs(slope(idx(ii)))>sloperms
for jj=0:1
if ii+jj==0 || ii+jj==length(idx)
indicator_hor(idx(end),ch)=1;
else
indicator_hor(idx(mod(ii+jj,length(idx))),ch)=1;
end
end
end
end
clear idx
end
% Outlier detection: constant TOA in sagittal planes
epsilon=2;
for ii=1:20
sag_dev=zeros(data.API.M,1);
for lat=-90:epsilon:90
idx=find(pos(:,6)>lat-epsilon/2 & pos(:,6)<=lat+epsilon/2);
idx2=find(pos(:,6)>lat-epsilon/2 & pos(:,6)<=lat+epsilon/2 & indicator_hor(:,ch)==0 & indicator(:,ch)==0);
if length(idx2)>2
sag_dev(idx,1)=toaEst(idx,ch)-mean(toaEst(idx2,ch));
end
end
sag_var=sqrt(sum(sag_dev.^2)/length(sag_dev));
if sag_var<2
sag_var=2;
end
indicator(:,ch)=zeros(size(indicator,1),1);
indicator_sag(:,ch)=zeros(size(indicator_sag,1),1);
indicator_sag(abs(sag_dev)>sag_var,ch)=ones(length(find(abs(sag_dev)>sag_var)),1);
indicator(abs(sag_dev)>sag_var | indicator_hor(:,ch)==1,ch)=ones(length(find(abs(sag_dev)>sag_var | indicator_hor(:,ch)==1)),1);
end
clear sag_dev; clear sag_var;
plot_ziegelwanger2013(data,toaEst,4,'k',0,1,1,{'-'},1);
hold on
h=plot_ziegelwanger2013(data,indicator_sag.*toaEst,3,'w',0,1,1,{'^'},4);
set(h,'MarkerFaceColor','w','MarkerEdgeColor','w');
h=plot_ziegelwanger2013(data,indicator_hor.*toaEst,3,'w',0,1,1,{'v'},4);
set(h,'MarkerFaceColor','w','MarkerEdgeColor','w');
h=plot_ziegelwanger2013(data,indicator_sag.*toaEst,3,'b',0,1,1,{'^'},4);
set(h,'LineWidth',2);
h=plot_ziegelwanger2013(data,indicator_hor.*toaEst,3,'b',0,1,1,{'v'},4);
set(h,'LineWidth',2);
h=plot_ziegelwanger2013(data,(-indicator+1).*toaEst,3,'r',0,1,1,{'o'},4);
set(h,'MarkerFaceColor','r','MarkerEdgeColor','r');
ylabel('TOA (ms)')
xlabel('Azimuth (deg)')
grid off
xlim([-10 370])
ylim([0.65 1.65])
title('')
set(gca,'YTick',[0.7 1 1.3 1.6])
end
%% Figure 3b
if flags.do_fig3b
figure
sym='sdo'; %plot symbols
clr=[0,0,255; 255,0,0; 255,255,67]/255; %plot colors
meclr=[0,0,255; 255,0,0; 255,255,67]/255; %marker edge colors
data=data_ziegelwanger2013('SPHERE_ROT',flags.cachemode);
% radii
subplot(311)
var=[squeeze(data.results.p_onaxis(1,1,:))*100 squeeze(data.results.p_onaxis(1,2,:))*100 data.radius(:)/10];
for ch=1:size(data.results.p_onaxis,2)
plot(var(:,ch),sym(ch),'MarkerEdgeColor',meclr(ch,:),'MarkerFaceColor',clr(ch,:));
hold on
end
plot(var(:,3),'k--')
clear var;
ylabel('r in cm')
%phi
subplot(312)
var=[squeeze(data.results.p_onaxis(2,1,:))/pi*180 squeeze(data.results.p_onaxis(2,2,:))/pi*180 -data.phi+ones(length(data.phi),1)*90 -data.phi-ones(length(data.phi),1)*90];
for ch=1:size(data.results.p_onaxis,2)
plot(var(:,ch),sym(ch),'MarkerEdgeColor',meclr(ch,:),'MarkerFaceColor',clr(ch,:));
hold on
end
for ch=1:size(data.results.p_onaxis,2)
plot(1:9,var(1:9,2+ch),'k--')
plot(10:14,var(10:14,2+ch),'k--')
plot(15:23,var(15:23,2+ch),'k--')
plot(24:28,var(24:28,2+ch),'k--')
plot(29:37,var(29:37,2+ch),'k--')
plot(38:42,var(38:42,2+ch),'k--')
end
clear var;
set(gca,'YTick',[-90 90])
ylabel('\phi_e in deg')
%theta
subplot(313)
var=[squeeze(data.results.p_onaxis(3,1,:))/pi*180 squeeze(data.results.p_onaxis(3,2,:))/pi*180 data.theta -data.theta];
for ch=1:size(data.results.p_onaxis,2)
plot(var(:,ch),sym(ch),'MarkerEdgeColor',meclr(ch,:),'MarkerFaceColor',clr(ch,:));
hold on
end
for ch=1:size(data.results.p_onaxis,2)
plot(1:9,var(1:9,2+ch),'k--')
plot(10:14,var(10:14,2+ch),'k--')
plot(15:23,var(15:23,2+ch),'k--')
plot(24:28,var(24:28,2+ch),'k--')
plot(29:37,var(29:37,2+ch),'k--')
plot(38:42,var(38:42,2+ch),'k--')
end
clear var;
ylabel('\theta_e in deg')
xlabel('Condition')
figure
sym='sdo'; %plot symbols
clr=[0,0,255; 255,0,0; 255,255,67]/255; %plot colors
meclr=[0,0,255; 255,0,0; 255,255,67]/255; %marker edge colors
data=data_ziegelwanger2013('SPHERE_DIS',flags.cachemode);
p1=data.results.p_onaxis(:,:,[1:3 length(data.xM)/3+1:length(data.xM)/3+3 length(data.xM)/3*2+1:length(data.xM)/3*2+3]);
r1=data.radius([1:3 length(data.xM)/3+1:length(data.xM)/3+3 length(data.xM)/3*2+1:length(data.xM)/3*2+3]);
yM1=data.yM([1:3 length(data.xM)/3+1:length(data.xM)/3+3 length(data.xM)/3*2+1:length(data.xM)/3*2+3]);
%radii
subplot(211)
% ymax2=round(max(max(squeeze(p1(1,:,:)*100))))+1;
var=[squeeze(p1(1,1,:))*100 squeeze(p1(1,2,:))*100 r1/10];
for ch=1:size(p1,2)
plot(var(:,ch),sym(ch),'MarkerEdgeColor',meclr(ch,:),'MarkerFaceColor',clr(ch,:));
hold on
end
plot(r1/10,'k--')
clear var;
ylabel('r in cm')
%yM
subplot(212)
plot(-yM1*100,'k--')
clear var;
xlabel('Condition')
ylabel('y_M in cm ')
end
%% Figure 1a
if flags.do_fig1a
hrtf={'ARI','CIPIC','LISTEN'};
sym='ods'; %plot symbols
%-------------------------------Load Data----------------------------------
for kk=1:length(hrtf)
data=data_ziegelwanger2013(hrtf{kk},flags.cachemode);
if kk==3
data.results=data.results([1:27 29:end]);
end
temp1=zeros(size(data.results(1).meta.p_onaxis,1),size(data.results(1).meta.p_onaxis,2),length(data.results));
temp3=zeros(size(data.results(1).meta.p_offaxis,1),size(data.results(1).meta.p_offaxis,2),length(data.results));
for ii=1:length(data.results)
temp1(:,:,ii)=data.results(ii).meta.p_onaxis;
temp3(:,1:size(data.results(ii).meta.p_offaxis,2),ii)=data.results(ii).meta.p_offaxis;
end
p_onaxis{kk}=temp1;
p_offaxis{kk}=temp3;
end
%radii
temp=1;
for kk=1:length(hrtf)
var(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
[squeeze(mean(p_offaxis{kk}(1,:,:)*100,2)) kk* ...
ones(size(p_onaxis{kk},3),1) transpose(1: ...
size(p_onaxis{kk},3))];
varl(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
[squeeze(p_offaxis{kk}(1,1,:)*100) kk* ...
ones(size(p_onaxis{kk},3),1) transpose(1: ...
size(p_onaxis{kk},3))];
varr(temp:temp+size(p_onaxis{kk},3)-1,:)= ...
[squeeze(p_offaxis{kk}(1,2,:)*100) kk* ...
ones(size(p_onaxis{kk},3),1) transpose(1: ...
size(p_onaxis{kk},3))];
temp=size(var,1)+1;
end
[~,idx]=sort(var(:,1));
var=var(idx,:);
varl=varl(idx,:);
varr=varr(idx,:);
var(:,3)=transpose(1:size(var,1));
varl(:,3)=transpose(1:size(var,1));
varr(:,3)=transpose(1:size(var,1));
subplot(411)
for kk=1:length(hrtf)
h{kk}=plot(var(var(:,2)==kk,3),var(var(:,2)==kk,1),sym(kk),'MarkerEdgeColor','b','MarkerFaceColor','b');
hold on
end
ylabel('Average r (cm)')
xlim([-1.5 temp+1.5])
ylim([7.5 10.57])
% legend([h{1},h{2},h{3}],'ARI','CIPIC','LISTEN','Location','NorthWest');
legend boxoff
subplot(412)
for kk=1:length(hrtf)
plot(var(var(:,2)==kk,3),abs(varl(varl(:,2)==kk,1)-varr(varr(:,2)==kk,1)),sym(kk),'MarkerEdgeColor','b','MarkerFaceColor','b');
hold on
end
ylabel('Binaural difference of r (cm)')
xlim([-1.5 temp+1.5])
ylim([-0.5 3.5])
clear var varl varr;
% yM
temp=1;
for kk=1:length(hrtf)
var(temp:temp+size(p_onaxis{kk},3)-1,:)=[squeeze(mean(p_offaxis{kk}(3,:,:)*100,2)) kk*ones(size(p_onaxis{kk},3),1) transpose(1:size(p_onaxis{kk},3))];
varl(temp:temp+size(p_onaxis{kk},3)-1,:)=[squeeze(p_offaxis{kk}(3,1,:)*100) kk*ones(size(p_onaxis{kk},3),1) transpose(1:size(p_onaxis{kk},3))];
varr(temp:temp+size(p_onaxis{kk},3)-1,:)=[squeeze(p_offaxis{kk}(3,2,:)*100) kk*ones(size(p_onaxis{kk},3),1) transpose(1:size(p_onaxis{kk},3))];
temp=size(var,1)+1;
end
var=var(idx,:);
varl=varl(idx,:);
varr=varr(idx,:);
var(:,3)=transpose(1:size(var,1));
varl(:,3)=transpose(1:size(var,1));
varr(:,3)=transpose(1:size(var,1));
subplot(413)
for kk=1:length(hrtf)
plot(var(var(:,2)==kk,3),var(var(:,2)==kk,1),sym(kk),'MarkerEdgeColor','b','MarkerFaceColor','b');
hold on
end
ylabel('y_M in cm')
xlim([-1.5 temp+1.5])
ylim([-3 4])
subplot(414)
for kk=1:length(hrtf)
plot(var(var(:,2)==kk,3),abs(varl(varl(:,2)==kk,1)-varr(varr(:,2)==kk,1)),sym(kk),'MarkerEdgeColor','b','MarkerFaceColor','b');
hold on
end
ylabel('Binaural difference of r (cm)')
xlim([-1.5 temp+1.5])
ylim([-0.5 3.5])
clear var varl varr;
xlabel('Subjects')
end
end
function idx=ARI_FindPosition(data,azimuth,elevation)
psi=sin(elevation/180*pi).*sin(data.SourcePosition(:,2)/180*pi) + ...
cos(elevation/180*pi).*cos(data.SourcePosition(:,2)/180*pi).*...
cos(azimuth/180*pi-data.SourcePosition(:,1)/180*pi);
[~,idx]=min(acos(psi));
end
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