THE AUDITORY MODELING TOOLBOX
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EXP_BAUMGARTNER2014 - Results from Baumgartner et al. (2014)
Program code:
function varargout = exp_baumgartner2014(varargin)
%EXP_BAUMGARTNER2014 Results from Baumgartner et al. (2014)
% Usage: data = exp_baumgartner2014(flag)
%
% EXP_BAUMGARTNER2014(flag) reproduces figures of the study from
% Baumgartner et al. (2014).
%
% The following flags can be specified
%
% 'fig2' Reproduce Fig.2:
% Binaural weighting function best fitting results from
% Morimoto (2001) labeled as [1] and Macpherson and Sabin (2007)
% labeled as [2] in a least squared error sense.
%
% 'fig3' Reproduce Fig.3:
% Prediction examples. Actual responses and response predictions
% for three exemplary listeners when listening to median-plane
% targets in the baseline condition.
% Actual response angles are shown as open circles.
% Probabilistic response predictions are encoded by brightness
% according to the color bar to the right. Actual (A:) and
% predicted (P:) quadrant error rates (QE) and local polar
% RMS errors (PE) are listed above each panel.
%
% 'fig4' Reproduce Fig.4:
% Model parametrization. Partial and joint prediction residues
% as functions of the degree of selectivity and the motoric
% response scatter. Residuum functions are normalized to the
% minimum residuum obtained for the optimal parameter value.
%
% 'fig5' Reproduce Fig.5:
% Effect of band limitation and spectral warping. Actual
% responses and response predictions for listener NH12 when
% listening to broadband (BB), low-pass filtered (LP), or
% spectrally warped (W) DTFs of the median plane. Data were
% pooled within pm15^circ of lateral angle.
% All other conventions are as in Fig.3.
%
% 'fig6' Reproduce Fig.6:
% Effect of band limitation and spectral warping.
% Listeners were tested with broadband (BB), low-pass
% filtered (LP), and spectrally warped (W) DTFs.
% Actual: experimental results from Majdak et al. (2013).
% Part.: Model predictions for the actual eight participants
% based on the actually tested target positions. Pool: Model
% predictions for our listener pool based on all possible
% target positions. Symbols and whiskers show median values
% and inter-quartile ranges, respectively. Symbols were
% horizontally shifted to avoid overlaps. Dotted horizontal
% lines represent chance rate. Correlation coefficients, r,
% and prediction residues, e, specify the correspondence
% between actual and predicted listener-specific performances.
%
% 'fig7' Reproduce Fig.7:
% Effect of spectral resolution in terms of varying the number
% of spectral channels of a channel vocoder. Actual responses
% and response predictions for exemplary listener NH12.
% Results for 24, 9, and 3 channels are shown. All other
% conventions are as in Fig.3.
%
% 'fig8' Reproduce Fig.8:
% Effect of spectral resolution in terms of varying the number
% of spectral channels of a channel vocoder. Actual experimental
% results are from Goupell et al. (2010). Stimulation with broadband
% click trains (CL) represents an unlimited number of channels.
% All other conventions are as in Fig.6.
%
% 'fig9' Reproduce Fig.9:
% Effect of non-individualized HRTFs in terms of untrained
% localization with others' instead of own ears. Statistics
% summaries with open symbols represent actual experimental
% results replotted from Fig.,13 of Middlebrooks (1999),
% statistics with filled symbols represent predicted results.
% Horizontal lines represent 25th, 50th, and 75th percentiles,
% the whiskers represent 5th and 95th percentiles, and crosses
% represent minima and maxima. Circles and squares denote mean values.
% Dimensions of
%
% 'fig10' Reproduce Fig.10:
% Effect of spectral ripples. Actual experimental results (circles)
% are from Macpherson and Middlebrooks (2003). Predicted results (filled circles)
% were modeled for our listener pool (squares). Either the ripple
% depth of 40,dB (top) or the ripple density of one ripple/octave
% (bottom) was kept constant. Ordinates show the listener-specific
% difference in error rate between a test and the baseline condition.
% Baseline performances are shown in the bottom right panel.
% Symbols and whiskers show median values and inter-quartile ranges,
% respectively. Symbols were horizontally shifted to avoid overlaps.
% Diamonds with dashed lines show predictions (P) of the model
% without positive spectral gradient extraction (PSGE).
%
% 'fig11' Reproduce Fig.11:
% Effect of high-frequency attenuation in speech localization.
% Actual experimental results are from Best et al. (2005).
% Absolute polar angle errors (top) and QE (bottom) were averaged
% across listeners. Circles and squares show actual and predicted
% results, respectively. Diamonds with dashed lines show predictions
% of the model without positive spectral gradient extraction.
%
% 'fig12' Reproduce Fig.12:
% Listener-specific likelihood statistics used to evaluate
% target-specific predictions for baseline condition. Bars
% show actual likelihoods, dots show mean expected likelihoods,
% and whiskers show tolerance intervals with 99% confidence
% level of expected likelihoods.
%
% 'fig13' Reproduce Fig.13:
% Exemplary baseline predictions. Same as Fig.3 but for listeners
% where actual likelihoods were outside the tolerance intervals.
%
% 'fig14' Reproduce Fig.14:
% Baseline performance as a function of the magnitude of the
% lateral response angle. Symbols and whiskers show median
% values and inter-quartile ranges, respectively. Open symbols
% represent actual and closed symbols predicted results. Symbols
% were horizontally shifted to avoid overlaps. Triangles with
% dashed lines show predictions (P) of the model without the
% sensomotoric mapping (SMM) stage.
%
% 'tab1' Reproduce Tab.1:
% Listener-specific sensitivity calibrated on the basis of N
% baseline targets in proximity of the median plane (+-30deg).
% Listeners are labeled as NHl. Actual and predicted quadrant
% errors (QE) and local polar RMS errors (PE) are shown pairwise
% (Actual | Predicted).
%
% 'tab2' Reproduce Tab.2:
% The effects of model configurations on the prediction residues.
% PSGE: model with or without positive spectral gradient extraction.
% MBA: model with or without manual bandwidth adjustment to the
% stimulus bandwidth. Prediction residues between actual and
% predicted PE and QE are listed for acute performance with
% the broadband (BB), low-passed (LP) and warping (W) conditions
% of the experiments from Majdak et al. (2013).
%
% 'tab3' Reproduce Tab.3:
% Performance predictions for binaural, ipsilateral, and
% contralateral listening conditions. The binaural weighting
% coefficient was varied in order to represent the three
% conditions: binaural: Phi = 13^circ;
% ipsilateral: Phi rightarrow +0^circ;
% contralateral: Phi rightarrow -0^circ.
% Prediction residues and correlation coefficients between
% actual and predicted results are shown together with predicted
% average performances.
%
%
% 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.
%
% Requirements:
% -------------
%
% 1) SOFA API v0.4.3 or higher from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)
%
% 2) Data in auxdata/baumgartner2014
%
% 3) Statistics Toolbox for Matlab (for some of the figures)
%
% Examples:
% ---------
%
% To display Fig.2 use :
%
% exp_baumgartner2014('fig2');
%
% To display Fig.3 use :
%
% exp_baumgartner2014('fig3');
%
% To display Fig.4 use :
%
% exp_baumgartner2014('fig4');
%
% To display Fig.5 use :
%
% exp_baumgartner2014('fig5');
%
% To display Fig.6 use :
%
% exp_baumgartner2014('fig6');
%
% To display Fig.7 use :
%
% exp_baumgartner2014('fig7');
%
% To display Fig.8 use :
%
% exp_baumgartner2014('fig8');
%
% To display Fig.9 use :
%
% exp_baumgartner2014('fig9');
%
% To display Fig.10 use :
%
% exp_baumgartner2014('fig10');
%
% To display Fig.11 use :
%
% exp_baumgartner2014('fig11');
%
% To display Fig.12 use :
%
% exp_baumgartner2014('fig12');
%
% To display Fig.13 use :
%
% exp_baumgartner2014('fig13');
%
% To display Fig.14 use :
%
% exp_baumgartner2014('fig14');
%
%
% See also: baumgartner2014 data_baumgartner2014
%
% References:
% 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.
%
% M. Morimoto. The contribution of two ears to the perception of vertical
% angle in sagittal planes. J. Acoust. Soc. Am., 109:1596--1603, 2001.
%
% P. Majdak, T. Walder, and B. Laback. Effect of long-term training on
% sound localization performance with spectrally warped and band-limited
% head-related transfer functions. J. Acoust. Soc. Am., 134:2148--2159,
% 2013.
%
% M. J. Goupell, P. Majdak, and B. Laback. Median-plane sound
% localization as a function of the number of spectral channels using a
% channel vocoder. J. Acoust. Soc. Am., 127:990--1001, 2010.
%
% J. C. Middlebrooks. Virtual localization improved by scaling
% nonindividualized external-ear transfer functions in frequency. J.
% Acoust. Soc. Am., 106:1493--1510, 1999.
%
% E. A. Macpherson and J. C. Middlebrooks. Vertical-plane sound
% localization probed with ripple-spectrum noise. J. Acoust. Soc. Am.,
% 114:430--445, 2003.
%
%
% Url: http://amtoolbox.org/amt-1.5.0/doc/experiments/exp_baumgartner2014.php
% #Author: Robert Baumgartner (2014)
% 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.
%% ------ Check input options --------------------------------------------
definput.import={'amt_cache'};
definput.keyvals.FontSize = 12;
definput.keyvals.MarkerSize = 6;
definput.flags.type = {'missingflag','fig2','fig3','fig4','fig5','fig6',...
'fig7','fig8','fig9','fig10','fig11','fig12','fig13',...
'fig14','tab1','tab2','tab3'};%,...s
definput.flags.plot = {'plot','no_plot'};
[flags,kv] = ltfatarghelper({'FontSize','MarkerSize'},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;
%% General Plot Settings
TickLength = [0.02,0.04];
%% ------ FIG 2 -----------------------------------------------------------
if flags.do_fig2
% Original Data
Morimoto_left = [1,0.5,0];
Morimoto_right = 1-Morimoto_left;
Morimoto_ang = [60,0,-60];
Macpherson_left = [3.75,0.5,-4]/10+0.5;
Macpherson_right = [-3.75,-0.5,4.75]/10+0.5;
Macpherson_ang = [30,0,-30];
data = [Morimoto_left, -Macpherson_right, Macpherson_left];
lat = [Morimoto_ang , Macpherson_ang , Macpherson_ang];
% Fit Slope
bwslope = 1:0.1:90;
resid = zeros(size(bwslope));
for ii = 1:length(bwslope)
binw_left = 1./(1+exp(-lat/bwslope(ii)));
resid(ii) = rms(binw_left-data);
end
[~,idmin] = min(resid);
contralateralGain = bwslope(idmin);
amt_disp(['Phi: ' num2str(contralateralGain,'%2.0f') ' deg'],'documentation');
% Calculate specific weights to plot
lat = -90:5:90;
binw_left = 1./(1+exp(-lat/contralateralGain)); % weight of left ear signal with 0 <= binw <= 1
binw_right = 1-binw_left;
if flags.do_plot
figure;
plot(lat,binw_left)
hold on
plot(lat,binw_right,'r--')
plot(Morimoto_ang,Morimoto_left,'vk','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w');
plot(Macpherson_ang,Macpherson_left,'ok','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
plot(Morimoto_ang,Morimoto_left,'vb','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
plot(Morimoto_ang,Morimoto_right,'vr','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
plot(Macpherson_ang,Macpherson_left,'ob','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
plot(Macpherson_ang,Macpherson_right,'or','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
l = legend('\itL','\itR','[1]','[2]');
set(l,'Location','East','FontSize',kv.FontSize-1)
set(gca,'XLim',[lat(1) lat(end)],'YLim',[-0.05 1.05],'XTick',-60:30:60,'FontSize',kv.FontSize)
xlabel('\phi_k (deg)','FontSize',kv.FontSize)
ylabel('w_{\zeta}(\phi_k)','FontSize',kv.FontSize)
end
% Output
clear data
data.contralateralGain = contralateralGain;
end
%% ------ FIG 3&13 --------------------------------------------------------
if flags.do_fig3 || flags.do_fig13
latseg = [-20,0,20]; ii = 2; % centers of lateral segments
% dlat = 10; % lateral range (+-) of each segment
s = data_baumgartner2014('baseline',flags.cachemode);
if flags.do_fig3
idselect = ismember({s.id},{'NH15','NH22','NH62'});
else
idselect = ismember({s.id},{'NH12','NH39','NH18'});
end
s = s(idselect);
%% LocaMo
qe = zeros(length(s),length(latseg));
pe = zeros(length(s),length(latseg));
for ll = 1:length(s)
s(ll).sphrtfs{ii} = 0; % init
s(ll).p{ii} = 0; % init
[s(ll).sphrtfs{ii},polang] = extractsp( latseg(ii),s(ll).Obj );
[s(ll).p{ii},respangs] = baumgartner2014(...
s(ll).sphrtfs{ii},s(ll).sphrtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',latseg(ii),'polsamp',polang);
[ qe(ll,ii),pe(ll,ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , polang , respangs , s(ll).target{ii});
if flags.do_plot
if ll ==1; figure; end
subplot(1,3,ll)
Nmax = min(150,s(ll).Ntargets{ii});
idplot = round(1:s(ll).Ntargets{ii}/Nmax:s(ll).Ntargets{ii});
plot_baumgartner2014(s(ll).p{ii},polang,respangs,...
s(ll).target{ii}(idplot),s(ll).response{ii}(idplot),...
'MarkerSize',kv.MarkerSize,'cmax',0.05,'no_colorbar');
title({['A: PE = ' num2str(s(ll).pe_exp_lat(ii),2) '\circ, QE = ' num2str(s(ll).qe_exp_lat(ii),2) '%'];['P: PE = ' num2str(pe(ll,ii),2) '\circ, QE = ' num2str(qe(ll,ii),2) '%']},...
'FontSize',kv.FontSize-1)
text(90,240,s(ll).id,'FontSize',kv.FontSize,...
'Color','w','HorizontalAlignment','center')
xlabel('Target Angle (deg)','FontSize',kv.FontSize)
ylabel('Response Angle (deg)','FontSize',kv.FontSize)
set(gca,'FontSize',kv.FontSize-1)
set(gca,'XTickLabel',{[];[];0;[];60;[];120;[];180;[];[]})
set(gca,'YTickLabel',{-60;[];0;[];60;[];120;[];180;[];240})
end
end
s = rmfield(s,{'Obj','itemlist','sphrtfs'}); % reduce file size
varargout{1} = s;
end
%% ------ FIG 4 -----------------------------------------------------------
if flags.do_fig4
[perr,perr_exp,qerr,qerr_exp,gamma,mrs,Ntargets] = amt_cache('get','parametrization',flags.cachemode);
if isempty(perr)
amt_disp('Note that this procedure may last one or two hours!');
gamma = [1,3,3,3,4,4,4,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,7,7,7,8,8,8,9,9,9,...
10,10,10,12,12,12,16,16,16,30,30,30];%,100,100,100];
mrs = [0,17,18,19,19,20,21,19,20,21, 0,10,100,16,17,18,19,20,21,23,30, 5,...
19,20,21,19,20,21,19,20,21,19,20,21,19,20,21,19,20,21,19,20,21];%,19,20,21];
latseg = -60:20:60; % centers of lateral segments
dlat = 10; % lateral range (+-) of each segment
for g = 1:length(gamma)
cname = ['result_baseline_g' num2str(gamma(g),'%u') '_mrs' num2str(mrs(g),'%u')];
[s,qe, pe] = amt_cache('get',cname,flags.cachemode);
if isempty(s)
s = data_baumgartner2014('baseline','gamma',gamma(g),'mrsmsp',mrs(g),flags.cachemode);
qe_exp = zeros(length(s),length(latseg));
pe_exp = zeros(length(s),length(latseg));
for ll = 1:length(s)
s(ll).target = [];
s(ll).response = [];
s(ll).Nt = [];
for ii = 1:length(latseg)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latseg(ii)+dlat & latresp > latseg(ii)-dlat;
s(ll).mm2 = s(ll).itemlist(idlat,:);
s(ll).mm2(:,7) = 0; % set lateral angle to 0deg such that localizationerror works also outside +-30deg
pe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'rmsPmedianlocal'));
qe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'querrMiddlebrooks'));
s(ll).target{ii} = real(s(ll).mm2(:,6)); % polar angle of target
s(ll).response{ii} = real(s(ll).mm2(:,8)); % polar angle of response
s(ll).Nt{ii} = length(s(ll).target{ii});
end
end
%% LocaMo
qe = zeros(length(s),length(latseg));
pe = zeros(length(s),length(latseg));
for ll = 1:length(s)
for ii = 1:length(latseg)
s(ll).sphrtfs{ii} = 0; % init
s(ll).p{ii} = 0; % init
[s(ll).sphrtfs{ii},polang] = extractsp( latseg(ii),s(ll).Obj );
[s(ll).p{ii},respangs] = baumgartner2014(...
s(ll).sphrtfs{ii},s(ll).sphrtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',latseg(ii),'polsamp',polang,...
'gamma',gamma(g),'mrsmsp',mrs(g));
if s(ll).Nt{ii} > 0
[ qe(ll,ii),pe(ll,ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , polang , respangs , s(ll).target{ii});
else
qe(ll,ii) = NaN;
pe(ll,ii) = NaN;
end
end
end
s = rmfield(s,{'Obj','itemlist','mm2','sphrtfs'}); % reduce file size
amt_cache('set',cname,s,qe, pe, qe_exp, pe_exp);
end
end
%% Combine results to single mat file
perr = zeros(length(s),length(latseg),length(gamma));
qerr = perr;
for g = 1:length(gamma)
fn = ['result_baseline_g' num2str(gamma(g),'%u') '_mrs' num2str(mrs(g),'%u')];
[s,qerr(:,:,g), perr(:,:,g),qerr_exp,perr_exp] = amt_cache('get',fn,'cached');
end
% Number of targets for each listener and lateral segment
Ntargets = zeros(length(s),7);
for jj = 1:length(s)
Ntargets(jj,:) = [s(jj).Nt{:}];
end
amt_cache('set','parametrization',perr,perr_exp,qerr,qerr_exp,gamma,mrs,Ntargets);
end
[qerr0,perr0] = baumgartner2014_pmv2ppp(ones(72,44)); % chance performances
% extract all different gammas
g = gamma;
gamma = unique(gamma);
Nset = size(perr,3);
idnum = Ntargets ~= 0;
relNt = Ntargets/sum(Ntargets(:));
% Compute all residues
resid.perr = zeros(length(gamma),1);
resid.qerr = resid.perr;
for ii = 1:Nset
dperr = perr_exp - perr(:,:,ii);
dqerr = qerr_exp - qerr(:,:,ii);
resid.perr(ii) = sqrt( relNt(idnum)' * (dperr(idnum)).^2 );
resid.qerr(ii) = sqrt( relNt(idnum)' * (dqerr(idnum)).^2 );
end
resid.total = resid.perr/perr0 + resid.qerr/qerr0;
% Select optimal residues for various gamma
id_g = zeros(length(gamma),1);
etotal_g = zeros(length(gamma),1);
for ii = 1:length(gamma)
idgamma = find(g == gamma(ii));
[etotal_g(ii),id] = min(resid.total(idgamma));
id_g(ii) = idgamma(id);
end
eperr_g = resid.perr(id_g);
eqerr_g = resid.qerr(id_g);
[tmp,idopt] = min(etotal_g);
etotal_g = etotal_g / etotal_g(idopt);
eperr_g = eperr_g / eperr_g(idopt);
eqerr_g = eqerr_g / eqerr_g(idopt);
amt_disp(['Optimal Gamma: ' num2str(gamma(idopt),'%u') ' dB^-1'],'documentation');
% Select residues for optimal gamma and various mrs
idgammaopt = find(g == gamma(idopt));
mrs_gopt = mrs(idgammaopt);
[mrssort,idsort] = sort(mrs_gopt);
idmrs = idgammaopt(idsort);
[tmp,idopt_mrs] = min(resid.total(idmrs));
idnorm = idmrs(idopt_mrs);
etotal_gopt = resid.total(idmrs) / resid.total(idnorm);
eperr_gopt = resid.perr(idmrs) / resid.perr(idnorm);
eqerr_gopt = resid.qerr(idmrs) / resid.qerr(idnorm);
amt_disp(['Optimal MRS: ' num2str(mrssort(idopt_mrs),'%u') ' deg'],'documentation');
if flags.do_plot
%% Plot residues for various gamma
% Interpolate data
gamma_int = logspace(0,2.1,1000);
inttype = 'pchip';
dperr_int = interp1(log10(gamma),eperr_g,log10(gamma_int),inttype);
dqerr_int = interp1(log10(gamma),eqerr_g,log10(gamma_int),inttype);
dtot_int = interp1(log10(gamma),etotal_g,log10(gamma_int),inttype);
% Plot
figure;
subplot(1,2,1)
semilogx(gamma_int,dperr_int,'k: ')
hold on
semilogx(gamma_int,dqerr_int,'k--')
semilogx(gamma_int,dtot_int,'k-')
semilogx(gamma(idopt),0.95,'vk','MarkerFaceColor','k','MarkerSize',kv.MarkerSize+1)
leg = legend('PE','QE','PE&QE','\{\epsilon,\Gamma\}_{opt}');
set(leg,'Location','northeast','FontSize',kv.FontSize-1)
ylabel('e(\Gamma) / e(\Gamma_{opt})','FontSize',kv.FontSize)
xlabel('\Gamma (dB^{-1})','FontSize',kv.FontSize)
set(gca,'XLim',[gamma(1)-0.1 gamma(end)+20],'YLim',[0.91 1.7],'XMinorTick','on',...
'FontSize',kv.FontSize-1)
set(gca,'XTick',[1:10,20:10:100],'XTickLabel',{1,2,3,'',5,'','','','',10,20,30,'',50,'','','','',100})
set(gca,'TickLength',TickLength)
%% Plot residues for optimal gamma and various mrs
% Interpolation
mrs_int = 0:0.1:45;
inttype = 'pchip';
dperr_int = interp1(mrssort,eperr_gopt,mrs_int,inttype);
dqerr_int = interp1(mrssort,eqerr_gopt,mrs_int,inttype);
dtot_int = interp1(mrssort,etotal_gopt,mrs_int,inttype);
% Plot
subplot(1,2,2)
plot(mrs_int,dperr_int,'k:')
hold on
plot(mrs_int,dqerr_int,'k--')
plot(mrs_int,dtot_int,'k-')
plot(mrssort(idopt_mrs),0.95,'vk','MarkerFaceColor','k','MarkerSize',kv.MarkerSize+1)
ylabel('e(\epsilon) / e(\epsilon_{opt})','FontSize',kv.FontSize)
xlabel('\epsilon (deg)','FontSize',kv.FontSize)
set(gca,'XLim',[mrssort(1) 32],'YLim',[0.91 1.7],'XMinorTick','on',...
'FontSize',kv.FontSize-1)
set(gca,'TickLength',TickLength)
end
end
%% ------ FIG 12 & TAB 1 -------------------------------------------------
if flags.do_fig12 || flags.do_tab1
[s,qe,pe,qe_exp,pe_exp,latseg] = amt_cache('get','baseline',flags.cachemode);
if isempty(s)
latseg = -60:20:60; % centers of lateral segments
dlat = 10; % lateral range (+-) of each segment
s = data_baumgartner2014('baseline',flags.cachemode);
qe_exp = zeros(length(s),length(latseg));
pe_exp = zeros(length(s),length(latseg));
for ll = 1:length(s)
s(ll).target = [];
s(ll).response = [];
s(ll).Nt = [];
for ii = 1:length(latseg)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latseg(ii)+dlat & latresp > latseg(ii)-dlat;
s(ll).mm2 = s(ll).itemlist(idlat,:);
s(ll).mm2(:,7) = 0; % set lateral angle to 0deg such that localizationerror works outside +-30deg
pe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'rmsPmedianlocal'));
qe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'querrMiddlebrooks'));
s(ll).target{ii} = real(s(ll).mm2(:,6)); % polar angle of target
s(ll).response{ii} = real(s(ll).mm2(:,8)); % polar angle of response
s(ll).Nt{ii} = length(s(ll).target{ii});
end
end
%% LocaMo
qe = zeros(length(s),length(latseg));
pe = zeros(length(s),length(latseg));
for ll = 1:length(s)
for ii = 1:length(latseg)
s(ll).sphrtfs{ii} = 0; % init
s(ll).p{ii} = 0; % init
[s(ll).sphrtfs{ii},polang{ii}] = extractsp( latseg(ii),s(ll).Obj );
[s(ll).p{ii},respangs{ii}] = baumgartner2014(...
s(ll).sphrtfs{ii},s(ll).sphrtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',latseg(ii),'polsamp',polang{ii});
if s(ll).Nt{ii} > 0
[ qe(ll,ii),pe(ll,ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , polang{ii} , respangs{ii} , s(ll).target{ii});
else
qe(ll,ii) = NaN;
pe(ll,ii) = NaN;
end
end
[s(ll).la,s(ll).le,s(ll).ci,s(ll).lr] = baumgartner2014_likelistat(s(ll).p,polang,respangs,s(ll).target,s(ll).response);
end
% sum( ci(:,1)-la' <=0 & ci(:,2)-la' >=0 )
s = rmfield(s,{'Obj','itemlist','mm2','sphrtfs'}); % reduce file size
amt_cache('set','baseline',s,qe,pe,qe_exp,pe_exp,latseg)
end
varargout{1} = struct('s',s, 'qe',qe, 'pe',pe, 'qe_exp',qe_exp, 'pe_exp',pe_exp,...
'latseg',latseg);
flags.do_pm30deglat = true; % consider lateral range of +-30 deg
Ns = length(s);
relfreq = zeros(Ns,length(latseg));
Ntall = nan(1,Ns);
for jj = 1:Ns
Ntlat = [s(jj).Nt{:}];
Ntall(jj) = sum(Ntlat);
relfreq(jj,:) = Ntlat/Ntall(jj);
end
if flags.do_pm30deglat
idlat = find(latseg <= 30 & latseg >= -30);
else % consider only median plane (+-10 deg)
idlat = latseg == 0;
end
relfreqPerSubject = relfreq(:,idlat)./repmat(sum(relfreq(:,idlat),2),1,3);
pe = sum(relfreqPerSubject .* pe(:,idlat) , 2);
qe = sum(relfreqPerSubject .* qe(:,idlat) , 2);
if flags.do_fig12
% IDs for xlabel
NHs = nan(Ns,4);
for ll = 1:Ns
NHs(ll,:) = s(ll).id;
end
if flags.do_plot
[tmp,idsort] = sort([s.la]);
la = [s.la];
le = [s.le];
ci = [s.ci]';
lr = [s.lr]';
fig=figure;
plot_baumgartner2014_likelistat(la(idsort),le(idsort),ci(idsort,:),lr(idsort,:))
ylabel({'Likelihood'},'FontSize',kv.FontSize)
xlabel('Listener (NH)','FontSize',kv.FontSize)
set(gca,'XLim',[0 Ns+1],'XTickLabel',NHs(idsort,3:4),...
'YMinorTick','on','FontSize',kv.FontSize)
% Bottom Line
hold on
ylim = [min(lr(:,1)) max(lr(:,4))];
plot([0,Ns+1],[ylim(1),ylim(1)]+0.0015*diff(ylim),'k')
set(fig,'PaperPosition',[1,1,9,3.5])
end
end
if flags.do_tab1
Labels = {'ID','N','S','actual QE','predicted QE','actual PE','predicted PE'};
mtx = zeros(length(Labels),length(s));
for ll = 1:length(s)
mtx(1,ll) = str2double(s(ll).id(3:end));
mtx(2,ll) = sum([s(ll).Ntargets{:}]);
mtx(3,ll) = s(ll).S;
mtx(4,ll) = s(ll).qe_exp;
mtx(5,ll) = qe(ll);
mtx(6,ll) = s(ll).pe_exp;
mtx(7,ll) = pe(ll);
end
[tmp,idsort] = sort(mtx(1,:)); % sort acc. to ID
mtx = mtx(:,idsort);
varargout{1} = mtx;
varargout{2} = Labels;
end
end
%% ------ FIG 14 ----------------------------------------------------------
if flags.do_fig14
[s,qe,pe,qe_exp,pe_exp,latseg] = amt_cache('get','baseline',flags.cachemode);
if isempty(s)
exp_baumgartner2014('fig5',flags.cachemode);
[s,qe,pe,qe_exp,pe_exp,latseg] = amt_cache('get','baseline',flags.cachemode);
end
[paradata.perr,~,paradata.qerr,~,paradata.g,paradata.mrs] = ...
amt_cache('get','parametrization',flags.cachemode);
idmrs0 = paradata.g == 6 & paradata.mrs == 0;
mrs0.pe = paradata.perr(:,:,idmrs0);
mrs0.qe = paradata.qerr(:,:,idmrs0);
%% # of targets
Ns = length(s);
Nlat = length(latseg);
Ntlat = zeros(Ns,Nlat);
relfreq = zeros(Ns,Nlat);
Ntall = zeros(Ns,1);
for jj = 1:Ns
Ntlat(jj,:) = [s(jj).Nt{:}];
Ntall(jj) = sum(Ntlat(jj,:));
relfreq(jj,:) = Ntlat(jj,:)/Ntall(jj);
end
relfreq = relfreq.*repmat(Ntall,1,Nlat)/sum(Ntall);
%% Pooling to lateralization
idlat0 = round(Nlat/2);
idleft = idlat0-1:-1:1;
idright = idlat0+1:Nlat;
latseg = latseg(idlat0:end);
relfreqLR = Ntlat(:,idleft) ./ (Ntlat(:,idleft) + Ntlat(:,idright) + eps);
pe = [pe(:,idlat0) , relfreqLR.*pe(:,idleft) + (1-relfreqLR).*pe(:,idright)];
pe_exp = [pe_exp(:,idlat0) , relfreqLR.*pe_exp(:,idleft) + (1-relfreqLR).*pe_exp(:,idright)];
qe = [qe(:,idlat0) , relfreqLR.*qe(:,idleft) + (1-relfreqLR).*qe(:,idright)];
qe_exp = [qe_exp(:,idlat0) , relfreqLR.*qe_exp(:,idleft) + (1-relfreqLR).*qe_exp(:,idright)];
relfreq = [relfreq(:,idlat0) , relfreq(:,1:idlat0-1) + relfreq(:,Nlat:-1:idlat0+1)];
mrs0.pe = [mrs0.pe(:,idlat0) , relfreqLR.*mrs0.pe(:,idleft) + (1-relfreqLR).*mrs0.pe(:,idright)];
mrs0.qe = [mrs0.qe(:,idlat0) , relfreqLR.*mrs0.qe(:,idleft) + (1-relfreqLR).*mrs0.qe(:,idright)];
%% Evaluation Metrics
idnum = not(isnan(pe_exp) | isnan(pe));
dpe = sqrt( relfreq(idnum)' * (pe_exp(idnum) - pe(idnum)).^2 );
dqe = sqrt( relfreq(idnum)' * (qe_exp(idnum) - qe(idnum)).^2 );
r_pe = corrcoef(pe_exp(idnum),pe(idnum));
r_qe = corrcoef(qe_exp(idnum),qe(idnum));
mrs0.dpe = sqrt( relfreq(idnum)' * (pe_exp(idnum) - mrs0.pe(idnum)).^2 );
mrs0.dqe = sqrt( relfreq(idnum)' * (qe_exp(idnum) - mrs0.qe(idnum)).^2 );
[mrs0.r_pe,mrs0.p_pe] = corrcoef(pe_exp(idnum),mrs0.pe(idnum));
[mrs0.r_qe,mrs0.p_qe] = corrcoef(qe_exp(idnum),mrs0.qe(idnum));
%% Quartiles
quart_pe = zeros(3,length(latseg),2); % 1st dim: 25/50/75 quantiles; 2nd dim: lat; 3rd dim: model/experiment/mrs0
quart_qe = zeros(3,length(latseg),2);
qlow = 0.25;
qhigh = 0.75;
for ii = 1:length(latseg)
id = not(isnan(pe(:,ii)));
quart_pe(:,ii,1) = quantile(pe(id,ii),[qlow .50 qhigh]);
quart_pe(:,ii,3) = quantile(mrs0.pe(id,ii),[qlow .50 qhigh]);
id = not(isnan(pe_exp(:,ii)));
quart_pe(:,ii,2) = quantile(pe_exp(id,ii),[qlow .50 qhigh]);
id = not(isnan(qe(:,ii)));
quart_qe(:,ii,1) = quantile(qe(id,ii),[qlow .50 qhigh]);
quart_qe(:,ii,3) = quantile(mrs0.qe(id,ii),[qlow .50 qhigh]);
id = not(isnan(qe_exp(:,ii)));
quart_qe(:,ii,2) = quantile(qe_exp(id,ii),[qlow .50 qhigh]);
end
if flags.do_plot
dx = 3;
%% PE
fig = figure;
subplot(1,2,1)
errorbar(latseg-dx,quart_pe(2,:,1),...
quart_pe(2,:,1)-quart_pe(1,:,1),...
quart_pe(3,:,1)-quart_pe(2,:,1),...
'ok-','MarkerSize',kv.MarkerSize,'MarkerFaceColor','k');
hold on
errorbar(latseg+dx,quart_pe(2,:,3),...
quart_pe(2,:,3)-quart_pe(1,:,3),...
quart_pe(3,:,3)-quart_pe(2,:,3),...
'vk--','MarkerSize',kv.MarkerSize,'MarkerFaceColor','k');
errorbar(latseg,quart_pe(2,:,2),...
quart_pe(2,:,2)-quart_pe(1,:,2),...
quart_pe(3,:,2)-quart_pe(2,:,2),...
'ok-','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w');
titstr = {['w/ SMM: e_{PE} = ' num2str(dpe,'%0.1f') '\circ , r_{PE} = ' num2str(r_pe(2),'%0.2f')];...
['w/o SMM: e_{PE} = ' num2str(mrs0.dpe,'%0.1f') '\circ , r_{PE} = ' num2str(mrs0.r_pe(2),'%0.2f')]};
amt_disp(titstr);
title(titstr,'FontSize',kv.FontSize)
set(gca,'XLim',[min(latseg)-2*dx,max(latseg)+2*dx],'YLim',[21.1,45.9],...
'YMinorTick','on','FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
ylabel('Local Polar RMS Error (deg)','FontSize',kv.FontSize)
xlabel('Magnitude of Lateral Angle (deg)','FontSize',kv.FontSize)
%% QE
subplot(1,2,2)
errorbar(latseg-dx,quart_qe(2,:,1),...
quart_qe(2,:,1)-quart_qe(1,:,1),...
quart_qe(3,:,1)-quart_qe(2,:,1),...
'ok-','MarkerSize',kv.MarkerSize,'MarkerFaceColor','k');
hold on
errorbar(latseg+dx,quart_qe(2,:,3),...
quart_qe(2,:,3)-quart_qe(1,:,3),...
quart_qe(3,:,3)-quart_qe(2,:,3),...
'vk--','MarkerSize',kv.MarkerSize,'MarkerFaceColor','k');
errorbar(latseg,quart_qe(2,:,2),...
quart_qe(2,:,2)-quart_qe(1,:,2),...
quart_qe(3,:,2)-quart_qe(2,:,2),...
'ok-','MarkerSize',kv.MarkerSize,'MarkerFaceColor','w');
titstr = {['w/ SMM: e_{QE} = ' num2str(dqe,'%0.1f') '% , r_{QE} = ' num2str(r_qe(2),'%0.2f')];...
['w/o SMM: e_{QE} = ' num2str(mrs0.dqe,'%0.1f') '% , r_{QE} = ' num2str(mrs0.r_qe(2),'%0.2f')]};
amt_disp(titstr);
title(titstr,'FontSize',kv.FontSize)
set(gca,'XLim',[min(latseg)-2*dx,max(latseg)+2*dx],'YLim',[2.1,26.9],...
'XTick',latseg,'YMinorTick','on','FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
ylabel('Quadrant Error (%)','FontSize',kv.FontSize)
xlabel('Magnitude of Lateral Angle (deg)','FontSize',kv.FontSize)
l = legend('P with SMM','P w/o SMM','Actual');
set(l,'FontSize',kv.FontSize-1,'Location','northwest')
set(fig,'PaperPosition',[1,1,10,3.5])
end
end
%% ------ FIG 5 -----------------------------------------------------------
if flags.do_fig5
latdivision = 0; % lateral angle
dlat = 15;
% Experimental Settings
Conditions = {'BB','LP','W'};
%% Computations
s = data_baumgartner2014('pool',flags.cachemode);
s = s(ismember({s.id},'NH12'));
amt_disp(['Listener: ' s.id],'documentation');
chance = [];
for C = 1:length(Conditions)
Cond = Conditions{C};
%% Data
% Experimental data
data = data_majdak2013(Cond);
for ll = 1:length(s)
if sum(ismember({data.id},s(ll).id)) % participant ?
s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx;
for ii = 1:length(latdivision)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
mm2 = s(ll).itemlist(idlat,:);
chance = [chance;mm2];
s(ll).target{ii} = mm2(:,6); % polar angle of target
s(ll).response{ii} = mm2(:,8); % polar angle of response
end
end
end
for ll = 1:length(s)
for ii = 1:length(latdivision)
s(ll).spdtfs{ii} = 0; % init
s(ll).polang{ii} = 0; % init
[s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(...
latdivision(ii),s(ll).Obj);
if C == 1 % Learn
s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
elseif C == 2 % Dummy
temp=amt_load('baumgartner2014','spatstrat_lpfilter.mat');
s(ll).spdtfs_c{ii} = filter(temp.blp,temp.alp,s(ll).spdtfs{ii});
elseif C == 3 % Warped
s(ll).spdtfs_c{ii} = local_warphrtf(s(ll).spdtfs{ii},s(ll).fs);
end
end
end
%% Run Model
for ll = 1:length(s)
qe = zeros(1,length(latdivision));
pe = zeros(1,length(latdivision));
qe_t = zeros(1,length(latdivision));
pe_t = zeros(1,length(latdivision));
for ii = 1:length(latdivision)
[s(ll).p{ii},rang] = baumgartner2014(...
s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',latdivision(ii),...
'polsamp',s(ll).polang{ii});
respangs{ii} = rang;
[ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp(s(ll).p{ii} , s(ll).polang{ii} , rang);
if sum(ismember({data.id},s(ll).id)) % if participant then actual targets
[ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , s(ll).polang{ii} , rang , s(ll).target{ii} );
end
end
% Model results of pool
s(ll).qe_pool(C,1) = mean(qe);
s(ll).pe_pool(C,1) = mean(pe);
if sum(ismember({data.id},s(ll).id)) % participant ?
% Actual experimental results
s(ll).qe_exp(C,1) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
s(ll).pe_exp(C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
s(ll).Nt(C,1) = size(s(ll).itemlist,1);
% Model results of participants (actual target angles)
if length(latdivision) == 3
s(ll).qe_part(C,1) = (qe_t(1)*length(s(ll).target{1}) + ...
qe_t(2)*length(s(ll).target{2}) + ...
qe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
s(ll).pe_part(C,1) = (pe_t(1)*length(s(ll).target{1}) + ...
pe_t(2)*length(s(ll).target{2}) + ...
pe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
else
s(ll).qe_part(C,1) = mean(qe_t);
s(ll).pe_part(C,1) = mean(pe_t);
end
if flags.do_plot
if C == 1; figure; end
subplot(1,3,C)
ii = find(latdivision==0);
responses = [];
targets = [];
for jj = ii
responses = [responses;s(ll).response{jj}];
targets = [targets;s(ll).target{jj}];
end
plot_baumgartner2014(s(ll).p{ii},s(ll).polang{ii},rang,...
targets,responses,'MarkerSize',kv.MarkerSize,'cmax',0.05,'no_colorbar')
text(90,240,Cond,...
'FontSize',kv.FontSize,'Color','w','HorizontalAlignment','center')
Nt = length(targets);
tmp.m = [zeros(Nt,5) targets(:) zeros(Nt,1) responses(:)];
tmp.qe = localizationerror(tmp.m,'querrMiddlebrooks');
tmp.pe = localizationerror(tmp.m,'rmsPmedianlocal');
title({['A: PE = ' num2str(tmp.pe,2) '\circ, QE = ' num2str(tmp.qe,2) '%'];...
['P: PE = ' num2str(pe_t(ii),2) '\circ, QE = ' num2str(qe_t(ii),2) '%']},'FontSize',kv.FontSize-1)
xlabel('Target Angle (deg)','FontSize',kv.FontSize)
ylabel('Response Angle (deg)','FontSize',kv.FontSize)
set(gca,'FontSize',kv.FontSize-1)
set(gca,'XTickLabel',{[];[];0;[];60;[];120;[];180;[];[]})
set(gca,'YTickLabel',{-60;[];0;[];60;[];120;[];180;[];240})
end
% [la(C,ll),le(C,ll),ci(C,ll,:)] = baumgartner2014_likelistat(s(ll).p,s(ll).polang,respangs,s(ll).target,s(ll).response);
end
end
end
varargout{1} = s;
end
%% ------ FIG 6 -----------------------------------------------------------
if flags.do_fig6
[s,cc] = amt_cache('get','spatstrat',flags.cachemode);
if isempty(s)
latdivision = [-20,0,20]; % lateral angle
dlat = 10;
% Experimental Settings
Conditions = {'BB','LP','W'};
%% Computations
s = data_baumgartner2014('pool',flags.cachemode);
% chance = [];
for C = 1:length(Conditions)
Cond = Conditions{C};
%% Data
% Experimental data
data = data_majdak2013(Cond);
for ll = 1:length(s)
if sum(ismember({data.id},s(ll).id)) % if actual participant
s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx;
for ii = 1:length(latdivision)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
mm2 = s(ll).itemlist(idlat,:);
% chance = [chance;mm2];
s(ll).target{ii} = mm2(:,6); % polar angle of target
s(ll).response{ii} = mm2(:,8); % polar angle of response
end
end
end
for ll = 1:length(s)
for ii = 1:length(latdivision)
s(ll).spdtfs{ii} = 0; % init
s(ll).polang{ii} = 0; % init
[s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(...
latdivision(ii),s(ll).Obj);
if C == 1 % Learn
s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
elseif C == 2 % Dummy
temp=amt_load('baumgartner2014','spatstrat_lpfilter.mat');
s(ll).spdtfs_c{ii} = filter(temp.blp,temp.alp,s(ll).spdtfs{ii});
elseif C == 3 % Warped
s(ll).spdtfs_c{ii} = local_warphrtf(s(ll).spdtfs{ii},s(ll).fs);
end
end
end
%% Run Model
for ll = 1:length(s)
qe = zeros(1,length(latdivision));
pe = zeros(1,length(latdivision));
qe_t = zeros(1,length(latdivision));
pe_t = zeros(1,length(latdivision));
for ii = 1:length(latdivision)
[s(ll).p{ii},rang] = baumgartner2014(...
s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',latdivision(ii),...
'polsamp',s(ll).polang{ii});
respangs{ii} = rang;
[ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp(s(ll).p{ii} , s(ll).polang{ii} , rang);
if sum(ismember({data.id},s(ll).id)) % if actual participant actual targets
[ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , s(ll).polang{ii} , rang , s(ll).target{ii} );
end
end
% Model results of pool
wlat = cos(deg2rad(latdivision)); % weighting compensating lateral compression
wlat = wlat/sum(wlat);
s(ll).qe_pool(C,1) = wlat * qe(:);
s(ll).pe_pool(C,1) = wlat * pe(:);
if sum(ismember({data.id},s(ll).id)) % if actual participant
% Actual experimental results
s(ll).qe_exp(C,1) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
s(ll).pe_exp(C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
s(ll).Nt(C,1) = size(s(ll).itemlist,1);
% Model results of participants (actual target angles)
if length(latdivision) == 3
s(ll).qe_part(C,1) = (qe_t(1)*length(s(ll).target{1}) + ...
qe_t(2)*length(s(ll).target{2}) + ...
qe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
s(ll).pe_part(C,1) = (pe_t(1)*length(s(ll).target{1}) + ...
pe_t(2)*length(s(ll).target{2}) + ...
pe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
else
s(ll).qe_part(C,1) = mean(qe_t);
s(ll).pe_part(C,1) = mean(pe_t);
end
[la(C,ll),le(C,ll),ci(C,ll,:)] = baumgartner2014_likelistat(s(ll).p,s(ll).polang,respangs,s(ll).target,s(ll).response);
end
end
end
s = rmfield(s,{'Obj','spdtfs_c','spdtfs'});% reduce file size
%% Compute Chance Performance
% chance = repmat(chance,10,1);
% id_chance = randi(size(chance,1),size(chance,1),1);
% chance(:,8) = chance(id_chance,6);
% pe_chance = localizationerror(chance,'rmsPmedianlocal');
% qe_chance = localizationerror(chance,'querrMiddlebrooks');
[r,p] = corrcoef([s.qe_exp],[s.qe_part]);
cc.qe.r = r(2);
cc.qe.p = p(2);
amt_disp(['QE: r = ' num2str(r(2),'%0.2f') ', p = ' num2str(p(2),'%0.3f')],'documentation');
[r,p] = corrcoef([s.pe_exp],[s.pe_part]);
cc.pe.r = r(2);
cc.pe.p = p(2);
amt_disp(['PE: r = ' num2str(r(2),'%0.2f') ', p = ' num2str(p(2),'%0.3f')],'documentation');
amt_cache('set','spatstrat',s,cc)
end
varargout{1} = s;
varargout{2} = cc;
%% Measures
% Quartiles
quart_pe_part = quantile([s.pe_part]',[.25 .50 .75]);
quart_qe_part = quantile([s.qe_part]',[.25 .50 .75]);
quart_pe_pool = quantile([s.pe_pool]',[.25 .50 .75]);
quart_qe_pool = quantile([s.qe_pool]',[.25 .50 .75]);
quart_pe_exp = quantile([s.pe_exp]',[.25 .50 .75]);
quart_qe_exp = quantile([s.qe_exp]',[.25 .50 .75]);
% RMS Differences
% individual:
Ntargets = [s.Nt]'; % # of targets
relfreq = Ntargets/sum(Ntargets(:));
sd_pe = ([s.pe_part]'-[s.pe_exp]').^2; % squared differences
dpe = sqrt(relfreq(:)' * sd_pe(:)); % weighted RMS diff.
sd_qe = ([s.qe_part]'-[s.qe_exp]').^2;
dqe = sqrt(relfreq(:)' * sd_qe(:));
% Chance performance
% qe0 = qe_chance;
% pe0 = pe_chance;
[qe0,pe0] = baumgartner2014_pmv2ppp('chance');
if flags.do_plot
dx = 0.15;
figure
subplot(121)
errorbar((1:3)+dx,quart_pe_part(2,:),...
quart_pe_part(2,:) - quart_pe_part(1,:),...
quart_pe_part(3,:) - quart_pe_part(2,:),...
'ko-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
hold on
errorbar((1:3)-dx,quart_pe_pool(2,:),...
quart_pe_pool(2,:) - quart_pe_pool(1,:),...
quart_pe_pool(3,:) - quart_pe_pool(2,:),...
'ks-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
errorbar((1:3),quart_pe_exp(2,:),...
quart_pe_exp(2,:) - quart_pe_exp(1,:),...
quart_pe_exp(3,:) - quart_pe_exp(2,:),...
'ko-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','w');
plot([0,4],[pe0,pe0],'k:')
title(['e_{PE} = ' num2str(dpe,'%0.1f') '\circ , r_{PE} = ' num2str(cc.pe.r,'%0.2f')],...
'FontSize',kv.FontSize)
ylabel('Local Polar RMS Error (deg)','FontSize',kv.FontSize)
set(gca,...
'XLim',[0.5 3.5],...
'XTick',1:3,...
'YLim',[27 54.9],...
'XTickLabel',{'BB';'LP';'W'},...
'YMinorTick','on','FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
subplot(122)
errorbar((1:3)+dx,quart_qe_part(2,:),...
quart_qe_part(2,:) - quart_qe_part(1,:),...
quart_qe_part(3,:) - quart_qe_part(2,:),...
'ko-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
hold on
errorbar((1:3)-dx,quart_qe_pool(2,:),...
quart_qe_pool(2,:) - quart_qe_pool(1,:),...
quart_qe_pool(3,:) - quart_qe_pool(2,:),...
'ks-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
errorbar((1:3),quart_qe_exp(2,:),...
quart_qe_exp(2,:) - quart_qe_exp(1,:),...
quart_qe_exp(3,:) - quart_qe_exp(2,:),...
'ko-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','w');
l = legend('Part.','Pool','Actual');
set(l,'Location','northwest','FontSize',kv.FontSize-1)
plot([0,4],[qe0 qe0],'k:')
title(['e_{QE} = ' num2str(dqe,'%0.1f') '% , r_{QE} = ' num2str(cc.qe.r,'%0.2f')],...
'FontSize',kv.FontSize)
ylabel('Quadrant Error (%)','FontSize',kv.FontSize)
set(gca,...
'XLim',[0.5 3.5],...
'XTick',1:3,...
'YLim',[0.1 54],...
'XTickLabel',{'BB';'LP';'W'},...
'YAxisLocation','left',...
'YMinorTick','on','FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
set(gcf,'PaperPosition',[1,1,10,3.5])
end
end
%% ------ FIG 7 -----------------------------------------------------------
if flags.do_fig7
% Model Settings
latdivision = 0; % lateral angle
dlat = 10;
% Experimental Settings
Conditions = {'N24','N9','N3'};
% Vocoder Settings
flow = 300; % lowest corner frequency
fhigh = 16000; % highest corner frequency
N = [24,9,3];
%% Computations
s = data_baumgartner2014('pool',flags.cachemode);
s = s(ismember({s.id},'NH12'));
amt_disp(['Listener: ' s.id],'documentation');
chance = [];
for C = 1:length(Conditions)
Cond = Conditions{C};
%% Data
% Experimental data
data = data_goupell2010(Cond);
for ll = 1:length(s)
if sum(ismember({data.id},s(ll).id)) % if actual participant
s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx;
for ii = 1:length(latdivision)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
mm2 = s(ll).itemlist(idlat,:);
chance = [chance;mm2];
s(ll).target{ii} = mm2(:,6); % polar angle of target
s(ll).response{ii} = mm2(:,8); % polar angle of response
end
end
end
% SP-DTFs
for ll = 1:length(s)
for ii = 1:length(latdivision)
s(ll).spdtfs{ii} = 0; % init
s(ll).polang{ii} = 0; % init
[s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(latdivision(ii),s(ll).Obj);
end
end
%% Genereate conditional HRIRs
stimPar.SamplingRate = s(ll).fs;
imp = [1;zeros(2^12-1,1)]; % smooth results for 2^12
for ll = 1:length(s)
for ii = 1:length(latdivision)
if N(C)==Inf
s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
else
n = N(C);
[syncrnfreq, GETtrain] = local_getVocoder('',imp,n,flow,fhigh,0,100,stimPar);
corners = [syncrnfreq(1);syncrnfreq(:,2)];
ref = s(ll).spdtfs{ii};
cond = zeros(length(imp),size(ref,2),2);
for ch = 1:size(ref,3)
for ang = 1:size(ref,2)
cond(:,ang,ch) = local_channelize('', 0.5*ref(:,ang,ch), ref(:,1), imp, n, corners, [], ...
GETtrain, stimPar, 1, 0.01*s(ll).fs, 0.01*s(ll).fs);
end
end
s(ll).spdtfs_c{ii} = cond;
end
end
end
%% Run Model
for ll = 1:length(s)
clear qe pe qe_t pe_t
for ii = 1:length(latdivision)
[p,rang] = baumgartner2014(...
s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',latdivision(ii),...
'polsamp',s(ll).polang{ii});
[ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp(p , s(ll).polang{ii} , rang);
if sum(ismember({data.id},s(ll).id)) % if participant then actual targets
[ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
p , s(ll).polang{ii} , rang , s(ll).target{ii} );
end
end
% Model results of pool
s(ll).qe_pool(C,1) = mean(qe);
s(ll).pe_pool(C,1) = mean(pe);
if sum(ismember({data.id},s(ll).id)) % if actual participant
% Actual experimental results
s(ll).qe_exp(C,1) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
s(ll).pe_exp(C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
s(ll).Nt(C,1) = size(s(ll).itemlist,1);
% Model results of participants (actual target angles)
if length(latdivision) == 3
s(ll).qe_part(C,1) = (qe_t(1)*length(s(ll).target{1}) + ...
qe_t(2)*length(s(ll).target{2}) + ...
qe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
s(ll).pe_part(C,1) = (pe_t(1)*length(s(ll).target{1}) + ...
pe_t(2)*length(s(ll).target{2}) + ...
pe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
else
s(ll).qe_part(C,1) = mean(qe_t);
s(ll).pe_part(C,1) = mean(pe_t);
end
if flags.do_plot && latdivision(ii) == 0
if C==1; figure; end
subplot(1,3,C)
plot_baumgartner2014(p,s(ll).polang{ii},rang,...
s(ll).target{ii},s(ll).response{ii},...
'MarkerSize',kv.MarkerSize,'cmax',0.05,'no_colorbar');
title({['A: PE = ' num2str(s(ll).pe_exp(C,1),2) '\circ, QE = ' num2str(s(ll).qe_exp(C,1),2) '%'];...
['P: PE = ' num2str(s(ll).pe_part(C,1),2) '\circ, QE = ' num2str(s(ll).qe_part(C,1),2) '%']},'FontSize',kv.FontSize-1)
text(90,240,Cond,...
'FontSize',kv.FontSize,'Color','w','HorizontalAlignment','center')
xlabel('Target Angle (deg)','FontSize',kv.FontSize)
ylabel('Response Angle (deg)','FontSize',kv.FontSize)
set(gca,'FontSize',kv.FontSize-1)
set(gca,'XTickLabel',{[];[];0;[];60;[];120;[];180;[];[]})
set(gca,'YTickLabel',{-60;[];0;[];60;[];120;[];180;[];240})
end
end
end
end
varargout{1} = s;
end
%% ------ FIG 8 ----------------------------------------------------------
if flags.do_fig8
[s,cc,N] = amt_cache('get','numchan',flags.cachemode);
if isempty(s)
% Model Settings
latdivision = 0; % lateral angle
dlat = 10;
% Experimental Settings
Conditions = {'CL','N24','N18','N12','N9','N6','N3'};
% Vocoder Settings
N = fliplr([3,6,9,12,18,24,30]); % # of vocoder channels
flow = 300; % lowest corner frequency
fhigh = 16000; % highest corner frequency
%% Computations
s = data_baumgartner2014('pool',flags.cachemode);
% chance = [];
for C = 1:length(Conditions)
Cond = Conditions{C};
%% Data
% Experimental data
data = data_goupell2010(Cond);
for ll = 1:length(s)
if sum(ismember({data.id},s(ll).id)) % if actual participant
s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx;
for ii = 1:length(latdivision)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
mm2 = s(ll).itemlist(idlat,:);
% chance = [chance;mm2];
s(ll).target{ii} = mm2(:,6); % polar angle of target
s(ll).response{ii} = mm2(:,8); % polar angle of response
end
end
end
% SP-DTFs
for ll = 1:length(s)
for ii = 1:length(latdivision)
s(ll).spdtfs{ii} = 0; % init
s(ll).polang{ii} = 0; % init
[s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(latdivision(ii),s(ll).Obj);
end
end
%% Genereate conditional HRIRs
stimPar.SamplingRate = s(ll).fs;
imp = [1;zeros(2^12-1,1)]; % smooth results for 2^12
for ll = 1:length(s)
for ii = 1:length(latdivision)
if C==1
s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
else
n = N(C);
[syncrnfreq, GETtrain] = local_getVocoder('',imp,n,flow,fhigh,0,100,stimPar);
corners = [syncrnfreq(1);syncrnfreq(:,2)];
ref = s(ll).spdtfs{ii};
cond = zeros(length(imp),size(ref,2),2);
for ch = 1:size(ref,3)
for ang = 1:size(ref,2)
cond(:,ang,ch) = local_channelize('', 0.5*ref(:,ang,ch), ref(:,1), imp, n, corners, [], ...
GETtrain, stimPar, 1, 0.01*s(ll).fs, 0.01*s(ll).fs);
end
end
s(ll).spdtfs_c{ii} = cond;
end
end
end
%% Run Model
for ll = 1:length(s)
clear qe pe qe_t pe_t
for ii = 1:length(latdivision)
[p,rang] = baumgartner2014(...
s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',latdivision(ii),...
'polsamp',s(ll).polang{ii});
s(ll).p{ii} = p;
respangs{ii} = rang;
[ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp(p , s(ll).polang{ii} , rang);
if sum(ismember({data.id},s(ll).id)) % if actual participant actual targets
[ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
p , s(ll).polang{ii} , rang , s(ll).target{ii} );
end
end
% Model results of pool
s(ll).qe_pool(C,1) = mean(qe);
s(ll).pe_pool(C,1) = mean(pe);
if sum(ismember({data.id},s(ll).id)) % if actual participant
% Actual experimental results
s(ll).qe_exp(C,1) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
s(ll).pe_exp(C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
s(ll).Nt(C,1) = size(s(ll).itemlist,1);
% Model results of participants (actual target angles)
if length(latdivision) == 3
s(ll).qe_part(C,1) = (qe_t(1)*length(s(ll).target{1}) + ...
qe_t(2)*length(s(ll).target{2}) + ...
qe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
s(ll).pe_part(C,1) = (pe_t(1)*length(s(ll).target{1}) + ...
pe_t(2)*length(s(ll).target{2}) + ...
pe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
else
s(ll).qe_part(C,1) = mean(qe_t);
s(ll).pe_part(C,1) = mean(pe_t);
end
[la(C,ll),le(C,ll),ci(C,ll,:)] = baumgartner2014_likelistat(s(ll).p,s(ll).polang,respangs,s(ll).target,s(ll).response);
end
end
amt_disp(['Condition ' Cond ' completed.']);
end
% Crange = max(chance(:,8))-min(chance(:,8));
% chance(:,8) = Crange*rand(size(chance,1),1)-min(chance(:,8));
% pe_chance = localizationerror(chance,'rmsPmedianlocal');
% qe_chance = localizationerror(chance,'querrMiddlebrooks');
[r,p] = corrcoef([s.qe_exp],[s.qe_part]);
cc.qe.r = r(2);
cc.qe.p = p(2);
amt_disp(['QE: r = ' num2str(r(2),'%0.2f') ', p = ' num2str(p(2),'%0.3f')],'documentation');
[r,p] = corrcoef([s.pe_exp],[s.pe_part]);
cc.pe.r = r(2);
cc.pe.p = p(2);
amt_disp(['PE: r = ' num2str(r(2),'%0.2f') ', p = ' num2str(p(2),'%0.3f')],'documentation');
s = rmfield(s,{'spdtfs','spdtfs_c','Obj','itemlist'});
amt_cache('set','numchan',s,cc,N)
end
varargout{1} = s;
varargout{2} = cc;
varargout{3} = N;
%% Measures
% Quartiles
quart_pe_part = fliplr(quantile([s.pe_part]',[.25 .50 .75]));
quart_qe_part = fliplr(quantile([s.qe_part]',[.25 .50 .75]));
quart_pe_pool = fliplr(quantile([s.pe_pool]',[.25 .50 .75]));
quart_qe_pool = fliplr(quantile([s.qe_pool]',[.25 .50 .75]));
quart_pe_exp = fliplr(quantile([s.pe_exp]',[.25 .50 .75]));
quart_qe_exp = fliplr(quantile([s.qe_exp]',[.25 .50 .75]));
% RMS Differences
% individual:
Ntargets = [s.Nt]'; % # of targets
relfreq = Ntargets/sum(Ntargets(:));
sd_pe = ([s.pe_part]'-[s.pe_exp]').^2; % squared differences
dpe = sqrt(relfreq(:)' * sd_pe(:)); % weighted RMS diff.
sd_qe = ([s.qe_part]'-[s.qe_exp]').^2;
dqe = sqrt(relfreq(:)' * sd_qe(:));
% Chance performance
% qe0 = qe_chance;
% pe0 = pe_chance;
[qe0,pe0] = baumgartner2014_pmv2ppp('chance');
if flags.do_plot
dx = 0.7;
figure
%% PE
subplot(121)
errorbar(fliplr(N)+dx,quart_pe_part(2,:),...
quart_pe_part(2,:) - quart_pe_part(1,:),...
quart_pe_part(3,:) - quart_pe_part(2,:),...
'ko-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
hold on
errorbar(fliplr(N)-dx,quart_pe_pool(2,:),...
quart_pe_pool(2,:) - quart_pe_pool(1,:),...
quart_pe_pool(3,:) - quart_pe_pool(2,:),...
'ks-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
errorbar(fliplr(N),quart_pe_exp(2,:),...
quart_pe_exp(2,:) - quart_pe_exp(1,:),...
quart_pe_exp(3,:) - quart_pe_exp(2,:),...
'ko-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','w');
plot([0,2*max(N)],[pe0,pe0],'k:')
xlabel('Num. of Channels','FontSize',kv.FontSize)
ylabel('Local Polar RMS Error (deg)','FontSize',kv.FontSize)
title(['e_{PE} = ' num2str(dpe,'%0.1f') '\circ , r_{PE} = ' num2str(cc.pe.r,'%0.2f')],...
'FontSize',kv.FontSize)
set(gca,'XLim',[1 32],'XTick',[3 6 9 12 18 24 30],...
'XTickLabel',{3;6;9;12;18;24;'CL'},...
'YLim',[27 54.9],...
'YMinorTick','on','FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
%% QE
subplot(122)
errorbar(fliplr(N)+dx,quart_qe_part(2,:),...
quart_qe_part(2,:) - quart_qe_part(1,:),...
quart_qe_part(3,:) - quart_qe_part(2,:),...
'ko-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
hold on
errorbar(fliplr(N)-dx,quart_qe_pool(2,:),...
quart_qe_pool(2,:) - quart_qe_pool(1,:),...
quart_qe_pool(3,:) - quart_qe_pool(2,:),...
'ks-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
errorbar(fliplr(N),quart_qe_exp(2,:),...
quart_qe_exp(2,:) - quart_qe_exp(1,:),...
quart_qe_exp(3,:) - quart_qe_exp(2,:),...
'ko-','MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','w');
l = legend('Part.','Pool','Actual');
set(l,'Location','northeast','FontSize',kv.FontSize-1)
plot([0,2*max(N)],[qe0,qe0],'k:')
title(['e_{QE} = ' num2str(dqe,'%0.1f') '% , r_{QE} = ' num2str(cc.qe.r,'%0.2f')],...
'FontSize',kv.FontSize)
xlabel('Num. of Channels','FontSize',kv.FontSize)
ylabel('Quadrant Error (%)','FontSize',kv.FontSize)
set(gca,'XLim',[1 32],'XTick',[3 6 9 12 18 24 30],...
'XTickLabel',{3;6;9;12;18;24;'CL'},...
'YLim',[0.1 54],...
'YMinorTick','on',...
'YAxisLocation','left','FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
set(gcf,'PaperPosition',[1,1,10,3.5])
end
end
%% ------ FIG 9 ----------------------------------------------------------
if flags.do_fig9
[qe_pool,pe_pool,pb_pool] = amt_cache('get','nonindividual',flags.cachemode);
if isempty(qe_pool)
% Settings
latdivision = [-20,0,20]; % lateral center angles of SPs
flow = 4e3;
s = data_baumgartner2014('pool',flags.cachemode);
ns = length(s);
% DTFs of the SPs
for ll = 1:ns
for ii = 1:length(latdivision)
s(ll).latang{ii} = latdivision(ii);
s(ll).polangs{ii} = [];
s(ll).spdtfs{ii} = [];
[s(ll).spdtfs{ii},s(ll).polangs{ii}] = extractsp(...
s(ll).latang{ii},s(ll).Obj);
end
end
amt_disp('Please wait a moment!');
qe = zeros(ns,ns,length(latdivision)); % init QEs
pe = qe; % init PEs
pb = qe; % init Polar Biases
for ll = 1:ns % listener
for jj = 1:ns % ears
for ii = 1:length(latdivision) % SPs
s(ll).p{jj,ii} = [];
s(ll).respangs{ii} = [];
[s(ll).p{jj,ii},s(ll).respangs{ii}] = baumgartner2014(...
s(jj).spdtfs{ii},s(ll).spdtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',s(ll).latang{ii},...
'polsamp',s(ll).polangs{ii},'flow',flow);
[ qe(ll,jj,ii),pe(ll,jj,ii),pb(ll,jj,ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{jj,ii} , s(jj).polangs{ii} , s(ll).respangs{ii});
end
end
amt_disp([' Subject ' num2str(ll,'%2u') ' of ' num2str(ns,'%2u')]);
end
lat_weight = cos(pi*latdivision/180); %lateral weight compensating compression of polar dimension
lat_weight = lat_weight/sum(lat_weight); % normalize
lat_weight = repmat(reshape(lat_weight,[1,1,length(latdivision)]),[ns,ns,1]);
qe_pool = sum(qe.*lat_weight,3);
pe_pool = sum(pe.*lat_weight,3);
pb_pool = sum(pb.*lat_weight,3);
amt_cache('set','nonindividual',qe_pool,pe_pool,pb_pool);
end
varargout{1} = struct('qe',qe_pool,'pe',pe_pool,'pb',pb_pool,'dimensions',{'listener (template)','ears (target)'});
data = data_middlebrooks1999;
%% Model outcomes
ns = size(pe_pool,1);
own = eye(ns) == 1;
other = not(own);
pb_pool = abs(pb_pool);
qe_own.quantiles = quantile(qe_pool(own),[0,0.05,0.25,0.5,0.75,0.95,1]);
pe_own.quantiles = quantile(pe_pool(own),[0,0.05,0.25,0.5,0.75,0.95,1]);
pb_own.quantiles = quantile(pb_pool(own),[0,0.05,0.25,0.5,0.75,0.95,1]);
qe_own.mean = mean(qe_pool(own));
pe_own.mean = mean(pe_pool(own));
pb_own.mean = mean(pb_pool(own));
qe_other.quantiles = quantile(qe_pool(other),[0,0.05,0.25,0.5,0.75,0.95,1]);
pe_other.quantiles = quantile(pe_pool(other),[0,0.05,0.25,0.5,0.75,0.95,1]);
pb_other.quantiles = quantile(pb_pool(other),[0,0.05,0.25,0.5,0.75,0.95,1]);
qe_other.mean = mean(qe_pool(other));
pe_other.mean = mean(pe_pool(other));
pb_other.mean = mean(pb_pool(other));
if flags.do_plot
dx = -0.2;
Marker = 'ks';
data.Marker = 'ko';
MFC = 'k'; % Marker Face Color
data.MFC = 'w';
figure;
subplot(131)
local_middlebroxplot(1-dx,qe_own.quantiles,kv.MarkerSize)
plot(1-dx,qe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
local_middlebroxplot(1+dx,data.qe_own.quantiles,kv.MarkerSize)
plot(1+dx,data.qe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
local_middlebroxplot(2-dx,qe_other.quantiles,kv.MarkerSize)
plot(2-dx,qe_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
local_middlebroxplot(2+dx,data.qe_other.quantiles,kv.MarkerSize)
plot(2+dx,data.qe_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
ylabel('Quadrant Errors (%)','FontSize',kv.FontSize)
set(gca,'YLim',[-2 43],'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
subplot(132)
plot(1-dx,pe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
hold on
plot(1+dx,data.pe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
local_middlebroxplot(1-dx,pe_own.quantiles,kv.MarkerSize)
plot(1-dx,pe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
local_middlebroxplot(1+dx,data.pe_own.quantiles,kv.MarkerSize)
plot(1+dx,data.pe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
local_middlebroxplot(2-dx,pe_other.quantiles,kv.MarkerSize)
plot(2-dx,pe_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
local_middlebroxplot(2+dx,data.pe_other.quantiles,kv.MarkerSize)
plot(2+dx,data.pe_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
ylabel('Local Polar RMS Error (deg)','FontSize',kv.FontSize)
set(gca,'YLim',[-2 62],'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
subplot(133)
local_middlebroxplot(1-dx,pb_own.quantiles,kv.MarkerSize)
plot(1-dx,pb_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
local_middlebroxplot(1+dx,data.pb_own.quantiles,kv.MarkerSize)
plot(1+dx,data.pb_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
local_middlebroxplot(2-dx,pb_other.quantiles,kv.MarkerSize)
plot(2-dx,pb_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
local_middlebroxplot(2+dx,data.pb_other.quantiles,kv.MarkerSize)
plot(2+dx,data.pb_other.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
ylabel('Magnitude of Elevation Bias (deg)','FontSize',kv.FontSize)
set(gca,'YLim',[-2 55],'XLim',[0.5 2.5],...
'XTick',1:2,'XTickLabel',{'Own' 'Other'},'FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
end
end
%% ------ FIG 10 ----------------------------------------------------------
if flags.do_fig10
[pe_exp1,pe_exp2,pe_flat,noDCN] = amt_cache('get','ripples',flags.cachemode);
if isempty(pe_exp1)
do_exp1 = true;
do_exp2 = true;
plotpmv = false;
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
%% Stimulus:
% 250-ms bursts, 20-ms raised-cosine fade in/out, flat from 0.6-16kHz
fs = 48e3; % sampling rate
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 = real(ifft(10.^(Sexp1/20),2*Nf));
sexp1 = circshift(sexp1,Nf); % IR corresponding to ripple modification
% 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 = real(ifft(10.^(Sexp2/20),2*Nf));
sexp2 = circshift(sexp2,Nf); % IR corresponding to ripple modification
%% Modeling
for psge = 0:1
if psge == 1
s = data_baumgartner2014('pool',flags.cachemode);
else % recalib
s = data_baumgartner2014('pool','do',psge,flags.cachemode);
end
latseg = 0; % centers of lateral segments
runs = 5; % # runs of virtual experiments
pe_exp1 = zeros(length(latseg),length(s),length(density),2);
pe_exp2 = zeros(length(latseg),length(s),length(depth),2);
pe_flat = zeros(length(latseg),length(s));
for ss = 1:length(s)
for ll = 1:length(latseg)
[spdtfs,polang] = extractsp(latseg(ll),s(ss).Obj);
% target elevation range of +-60 deg
idt = find( polang<=60 | polang>=120 );
targets = spdtfs(:,idt,:);
tang = polang(idt);
[pflat,rang] = baumgartner2014(targets,spdtfs,...
'S',s(ss).S,'polsamp',polang,...
'lat',latseg(ll),'stim',[1;0],'do',psge); % Impulse
mflat = baumgartner2014_virtualexp(pflat,tang,rang,'runs',runs);
[f,r] = localizationerror(mflat,'sirpMacpherson2000');
pe_flat(ll,ss) = localizationerror(mflat,f,r,'perMacpherson2003');
if plotpmv,
figure;
plot_baumgartner2014(pflat,tang,rang,mflat(:,6),mflat(:,8));title(num2str(pe_flat(ll,ss),2));pause(0.5);
end
if do_exp1 % Exp. I
for ii = 1:2*length(density)
[p,rang] = baumgartner2014(targets,spdtfs,...
'S',s(ss).S,'polsamp',polang,...
'lat',latseg(ll),'stim',sexp1(:,ii),'do',psge);
m = baumgartner2014_virtualexp(p,tang,rang,'runs',runs);
pe_exp1(ll,ss,ii) = localizationerror(m,f,r,'perMacpherson2003');% - pe_flat(ll,ss);
if plotpmv; figure; plot_baumgartner2014(p,tang,rang,m(:,6),m(:,8));title([num2str(density(mod(ii-1,10)+1)) 'ripples/oct; PE:' num2str(pe_exp1(ll,ss,ii),2) '%']);pause(0.5); end
end
end
if do_exp2 % Exp. II
for ii = 1:2*length(depth)
[p,rang] = baumgartner2014(targets,spdtfs,...
'S',s(ss).S,'polsamp',polang,...
'lat',latseg(ll),'stim',sexp2(:,ii),'do',psge);
m = baumgartner2014_virtualexp(p,tang,rang,'runs',runs);
pe_exp2(ll,ss,ii) = localizationerror(m,f,r,'perMacpherson2003');% - pe_flat(ll,ss);
if plotpmv; plot_baumgartner2014(p,tang,rang,m(:,6),m(:,8));title([num2str(depth(mod(ii-1,4)+1)) 'dB; PE:' num2str(pe_exp2(ll,ss,ii),2) '%']);pause(0.5); end
end
end
end
amt_disp([num2str(ss,'%2u') ' of ' num2str(length(s),'%2u') ' subjects completed']);
end
amt_disp();
if length(latseg) > 1
pe_exp1 = squeeze(mean(pe_exp1));
pe_exp2 = squeeze(mean(pe_exp2));
pe_flat = squeeze(mean(pe_flat));
else
pe_exp1 = squeeze(pe_exp1);
pe_exp2 = squeeze(pe_exp2);
pe_flat = squeeze(pe_flat);
end
%% Save
if psge==0
noDCN.pe_exp1 = pe_exp1;
noDCN.pe_exp2 = pe_exp2;
noDCN.pe_flat = pe_flat;
delete(which('baumgartner2014calibration.mat'))
end
end
amt_cache('set','ripples',pe_exp1,pe_exp2,pe_flat,noDCN)
end
varargout{1} = {pe_exp1,pe_exp2,pe_flat,noDCN};
amt_disp(...
{'Note: Predicted results slightly deviate from the original publication ';...
'because of a mismatch in the selection of responses considered for the ';...
'evaluation of polar error rates. Macpherson and Middlebrooks (2003) restricted ';...
'the target angles whereas Baumgartner et al. (2014) restricted the response angles.';...
'Now results are calculated for restricted target angles in line with ';...
'Macpherson and Middlebrooks (2003).'})
dcn_flag = true;
% Original data:
data = data_macpherson2003;
%% Phase condition handling
pe_exp1 = mean(pe_exp1,3);
data.pe_exp1 = mean(data.pe_exp1,3);
pe_exp2 = mean(pe_exp2,3);
data.pe_exp2 = mean(data.pe_exp2,3);
if dcn_flag
noDCN.pe_exp1 = mean(noDCN.pe_exp1,3);
noDCN.pe_exp2 = mean(noDCN.pe_exp2,3);
end
idphase = 1;
%% Increase
pe_exp1 = pe_exp1 - repmat(pe_flat(:),1,size(pe_exp1,2));
pe_exp2 = pe_exp2 - repmat(pe_flat(:),1,size(pe_exp2,2));
if dcn_flag
noDCN.pe_exp1 = noDCN.pe_exp1 - repmat(noDCN.pe_flat(:),1,size(noDCN.pe_exp1,2));
noDCN.pe_exp2 = noDCN.pe_exp2 - repmat(noDCN.pe_flat(:),1,size(noDCN.pe_exp2,2));
end
%% Statistics
quart_pe_flat = quantile(pe_flat,[.25 .50 .75]);
quart_pe_data_flat = quantile(data.pe_flat,[.25 .50 .75]);
quart_pe_exp1 = quantile(pe_exp1,[.25 .50 .75]);
quart_pe_data_exp1 = quantile(data.pe_exp1,[.25 .50 .75]);
quart_pe_exp2 = quantile(pe_exp2,[.25 .50 .75]);
quart_pe_data_exp2 = quantile(data.pe_exp2,[.25 .50 .75]);
if dcn_flag
noDCN.quart_pe_flat = quantile(noDCN.pe_flat,[.25 .50 .75]);
noDCN.quart_pe_exp1 = quantile(noDCN.pe_exp1,[.25 .50 .75]);
noDCN.quart_pe_exp2 = quantile(noDCN.pe_exp2,[.25 .50 .75]);
end
if flags.do_plot
dx = 1.05;
FontSize = kv.FontSize;
MarkerSize = kv.MarkerSize;
% Exp1
figure;
subplot(2,8,1:8)
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),...
'ks-','MarkerSize',MarkerSize,...
'MarkerFaceColor','k');
hold on
if dcn_flag
errorbar(data.density*dx,noDCN.quart_pe_exp1(2,:,idphase),...
noDCN.quart_pe_exp1(2,:,idphase) - noDCN.quart_pe_exp1(1,:,idphase),...
noDCN.quart_pe_exp1(3,:,idphase) - noDCN.quart_pe_exp1(2,:,idphase),...
'kd--','MarkerSize',MarkerSize-1,...
'MarkerFaceColor','k');
end
errorbar(data.density,quart_pe_data_exp1(2,:,idphase),...
quart_pe_data_exp1(2,:,idphase) - quart_pe_data_exp1(1,:,idphase),...
quart_pe_data_exp1(3,:,idphase) - quart_pe_data_exp1(2,:,idphase),...
'ko-','MarkerSize',MarkerSize,...
'MarkerFaceColor','w');
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)
if dcn_flag
leg = legend('P with PSGE','P w/o PSGE','Actual');
else
leg = legend('Predicted','Actual');
end
set(leg,'FontSize',FontSize-1,'Location','southwest')
legpos = get(leg,'Position');
legpos(1) = legpos(1)+0.05;
set(leg,'Position',legpos)
%% Exp2
subplot(2,8,9:13)
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),...
'ks-','MarkerSize',MarkerSize,...
'MarkerFaceColor','k');
hold on
if dcn_flag
errorbar(data.depth+1,noDCN.quart_pe_exp2(2,:,idphase),...
noDCN.quart_pe_exp2(2,:,idphase) - noDCN.quart_pe_exp2(1,:,idphase),...
noDCN.quart_pe_exp2(3,:,idphase) - noDCN.quart_pe_exp2(2,:,idphase),...
'kd--','MarkerSize',MarkerSize-1,...
'MarkerFaceColor','k');
end
errorbar(data.depth,quart_pe_data_exp2(2,:,idphase),...
quart_pe_data_exp2(2,:,idphase) - quart_pe_data_exp2(1,:,idphase),...
quart_pe_data_exp2(3,:,idphase) - quart_pe_data_exp2(2,:,idphase),...
'ko-','MarkerSize',MarkerSize,...
'MarkerFaceColor','w');
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,quart_pe_flat(2),...
quart_pe_flat(2) - quart_pe_flat(1),...
quart_pe_flat(3) - quart_pe_flat(2),...
'ks-','MarkerSize',MarkerSize,...
'MarkerFaceColor','k');
hold on
if dcn_flag
errorbar(0.5,noDCN.quart_pe_flat(2),...
noDCN.quart_pe_flat(2) - noDCN.quart_pe_flat(1),...
noDCN.quart_pe_flat(3) - noDCN.quart_pe_flat(2),...
'kd-','MarkerSize',MarkerSize-1,...
'MarkerFaceColor','k');
end
errorbar(0,quart_pe_data_flat(2),...
quart_pe_data_flat(2) - quart_pe_data_flat(1),...
quart_pe_data_flat(3) - quart_pe_data_flat(2),...
'ko-','MarkerSize',MarkerSize,...
'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)
%% Overall correlation between actual and predicted median values
if dcn_flag
m_pe_pred = [quart_pe_exp1(2,:,idphase) quart_pe_exp2(2,:,idphase)];
m_pe_pred_noDCN = [noDCN.quart_pe_exp1(2,:,idphase) noDCN.quart_pe_exp2(2,:,idphase)];
m_pe_actual = [quart_pe_data_exp1(2,:,idphase) quart_pe_data_exp2(2,:,idphase)];
r = corrcoef(m_pe_pred,m_pe_actual);
rDCN = r(2);
r = corrcoef(m_pe_pred_noDCN,m_pe_actual);
rnoDCN = r(2);
% r = corrcoef(m_pe_pred,m_pe_pred_noDCN);
% rInter = r(2);
% [t,p] = corrdifftest(rDCN,rnoDCN,rInter,14,'hotelling')
% z = corrdifftest(rDCN,rnoDCN,rInter,14,'steiger')
amt_disp('Correlation between actual and predicted median values (15 conditions):','documentation');
amt_disp(['w/ PSGE: r = ' num2str(rDCN,'%0.2f')],'documentation');
amt_disp(['w/o PSGE: r = ' num2str(rnoDCN,'%0.2f')],'documentation');
end
end
end
%% ------ FIG 11 ----------------------------------------------------------
if flags.do_fig11
[ape_all,qe_all,ape_BBnoise,qe_BBnoise] = amt_cache('get','highfreqatten_do1',flags.cachemode);
noDCN = amt_cache('get','highfreqatten_do0',flags.cachemode);
if isempty(ape_all) || isempty(noDCN.ape_all)
fnHarvard = fullfile(amt_basepath,'auxdata','baumgartner2014','HarvardWords');
amt_disp('Note that this computation may take several hours!')
%% Settings
latseg = 0;%[-20,0,20]; % centers of lateral segments
NsampModel = 260; % # of modeled speech samples (takes 30min/sample); max: 260
startSamp = 1;
plotpmv = false;
plotspec = false;
%% Load Data
% Speech Samples from Harvard Word list
speechsample = amt_cache('get','best2005speechSamples');
if isempty(speechsample)
fs_orig = 80e3; % Hz
fs = 48e3; % Hz
p_resamp = fs/fs_orig;
kk = 1;
if NsampModel <= 51
Nsamp = NsampModel;
Nlists = 1;
else
Nsamp = 260;
Nlists = 5;
end
lsamp = 120000*p_resamp;
speechsample = cell(Nsamp,1);
for ii = 1:Nlists
tmp.list = ['list' num2str(ii,'%1.0u')];
tmp.path = fullfile(fnHarvard,tmp.list);
tmp.dir = dir(fullfile(tmp.path,'*.mat'));
for jj = 1:length(tmp.dir)
if jj > Nsamp; break; end
% load(fullfile(tmp.path,tmp.dir(jj).name))
sig = amt_load('baumgartner2014',fullfile('HarvardWords',tmp.list,tmp.dir(jj).name));
signal = resample(sig.word,p_resamp*10,10);
gcurve = exp(-0.5 * (0:0.001:10).^2) ./ (sqrt(2*pi));
env = filter(gcurve,1,signal.^2);
idon = max(find(env > 5e7,1,'first')-1e3,1);
idoff = min(find(env > 5e7,1,'last')+1e3,lsamp);
lwin = idoff-idon+1;
speechsample{kk} = signal(idon:idoff) .* tukeywin(lwin,0.01)';
kk = kk + 1;
end
end
amt_cache('set','best2005speechSamples',speechsample);
end
% 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
for psge = 0:1
s = data_baumgartner2014('pool','do',psge,flags.cachemode);
cname = ['result_best2005noise_do' num2str(psge,'%u')];
[ape_BBnoise,qe_BBnoise] = amt_cache('get',cname,flags.cachemode);
if isempty(ape_BBnoise)
ape_BBnoise = zeros(1,length(s),length(latseg));
qe_BBnoise = ape_BBnoise;
for ss = 1:length(s)
for ll = 1:length(latseg)
[spdtfs,polang] = extractsp(latseg(ll),s(ss).Obj);
[p,rang] = baumgartner2014(spdtfs,spdtfs,'do',psge,...
'S',s(ss).S,'polsamp',polang,'lat',latseg(ll),'notprint');
ape_BBnoise(1,ss,ll) = baumgartner2014_pmv2ppp(p,polang,rang,'absPE');
qe_BBnoise(1,ss,ll) = baumgartner2014_pmv2ppp(p,polang,rang);
if plotpmv; figure; plot_baumgartner2014(p,polang,rang); title(num2str(ape_BBnoise(1,ss,ll),2)); end
end
end
% Pool Lateral Segments
if length(latseg) > 1
ape_BBnoise = mean(ape_BBnoise,3);
qe_BBnoise = mean(qe_BBnoise,3);
end
amt_cache('set',cname,ape_BBnoise,qe_BBnoise);
end
end
ape_all = zeros(length(lp),length(s),NsampModel-startSamp+1,2);
qe_all = ape_all;
for kk = startSamp:NsampModel
for psge = 0:1
cname = ['result_best2005speech_samp' num2str(kk) '_do' num2str(psge,'%u')];
[ape_lat,qe_lat] = amt_cache('get',cname,flags.cachemode);
if isempty(ape_lat)
s = data_baumgartner2014('pool','do',psge,flags.cachemode);
ape_lat = zeros(length(lp),length(s),length(latseg));
qe_lat = ape_lat;
for ss = 1:length(s)
for ll = 1:length(latseg)
for ilp = 1:length(lp)
stim = filter(lp{ilp},1,speechsample{kk});
if plotspec; figure; audspecgram(stim(:),fs,'dynrange',150); end
[spdtfs,polang] = extractsp(latseg(ll),s(ss).Obj);
[p,rang] = baumgartner2014(spdtfs,spdtfs,'do',psge,...
'S',s(ss).S,'polsamp',polang,...
'lat',latseg(ll),'stim',stim,'notprint');
ape_lat(ilp,ss,ll) = baumgartner2014_pmv2ppp(p,polang,rang,'absPE');
qe_lat(ilp,ss,ll) = baumgartner2014_pmv2ppp(p,polang,rang);
if plotpmv; figure; plot_baumgartner2014(p,polang,rang); title(num2str(ape_lat(ilp,ss,ll),2)); end
end
end
end
% Pool Lateral Segments
if length(latseg) > 1
ape_lat = mean(ape_lat,3);
qe_lat = mean(qe_lat,3);
end
amt_cache('set',cname,ape_lat,qe_lat);
amt_disp([num2str(kk,'%1.0u') ' of ' num2str(NsampModel,'%2.0u') ' samples completed']);
end
ape_all(:,:,kk,psge+1) = ape_lat;
qe_all(:,:,kk,psge+1) = qe_lat;
end
end
noDCN.ape_all = ape_all(:,:,:,1);
noDCN.qe_all = qe_all(:,:,:,1);
[noDCN.ape_BBnoise,noDCN.qe_BBnoise] = amt_cache('get','result_best2005noise_do1');
amt_cache('set','highfreqatten_do0',noDCN)
ape_all = ape_all(:,:,:,2);
qe_all = qe_all(:,:,:,2);
[ape_BBnoise,qe_BBnoise] = amt_cache('get','result_best2005noise_do1');
amt_cache('set','highfreqatten_do1',ape_all,qe_all,ape_BBnoise,qe_BBnoise)
end
varargout{1} = {ape_all,qe_all,ape_BBnoise,qe_BBnoise};
varargout{2} = noDCN;
data = data_best2005;
% Pool Samples
ape_pooled = mean(ape_all,3);
qe_pooled = mean(qe_all,3);
noDCN.ape_pooled = mean(noDCN.ape_all,3);
noDCN.qe_pooled = mean(noDCN.qe_all,3);
% Confidence Intervals or standard errors
df_speech = size(ape_all,2)-1;%*size(ape_all,3)-1;
tquant_speech = 1;%icdf('t',.975,df_speech);
seape_speech = std(ape_pooled,0,2)*tquant_speech/(df_speech+1);
df_noise = size(ape_BBnoise,2)-1;
tquant_noise = 1;%icdf('t',.975,df_noise);
seape_noise = std(ape_BBnoise,0,2)*tquant_noise/(df_noise+1);
seape = [seape_noise;seape_speech];
% DCN
df_speech = size(noDCN.ape_all,2)-1;%*size(ape_all,3)-1;
tquant_speech = 1;%icdf('t',.975,df_speech);
seape_speech = std(noDCN.ape_pooled,0,2)*tquant_speech/(df_speech+1);
df_noise = size(noDCN.ape_BBnoise,2)-1;
tquant_noise = 1;%icdf('t',.975,df_noise);
seape_noise = std(noDCN.ape_BBnoise,0,2)*tquant_noise/(df_noise+1);
noDCN.seape = [seape_noise;seape_speech];
% Means
ape = mean([ape_BBnoise ; ape_pooled],2);
qe = mean([qe_BBnoise ; qe_pooled],2);
noDCN.ape = mean([noDCN.ape_BBnoise ; noDCN.ape_pooled],2);
noDCN.qe = mean([noDCN.qe_BBnoise ; noDCN.qe_pooled],2);
if flags.do_plot
dx = 0;
MarkerSize = kv.MarkerSize;
FontSize = kv.FontSize;
xticks = 0:size(ape_all,1);
ape0 = baumgartner2014_pmv2ppp('absPE','chance');
figure;
subplot(211)
h(1) = errorbar(xticks-dx,ape,seape,'ks');
set(h(1),'MarkerFaceColor','k','MarkerSize',MarkerSize,'LineStyle','-')
hold on
h(3) = errorbar(xticks+dx,noDCN.ape,noDCN.seape,'kd');
set(h(3),'MarkerFaceColor','k','MarkerSize',MarkerSize-1,'LineStyle','--')
h(2) = errorbar(xticks,data.ape,data.seape,'ko');
set(h(2),'MarkerFaceColor','w','MarkerSize',MarkerSize,'LineStyle','-')
plot([-0.5 4.5],[ape0 ape0],'k:') % chance performance
% ylabel('| \theta - \vartheta | (deg)','FontSize',FontSize)
ylabel('Polar Error (deg)','FontSize',FontSize)
set(gca,'XTick',xticks,'XTickLabel',[],'FontSize',FontSize)
set(gca,'XLim',[-0.5 4.5],'YLim',[12 95],'YMinorTick','on')
pos = get(gca,'Position');
pos(2) = pos(2)-0.11;
set(gca,'Position',pos)
% leg = legend('Predicted with edge extraction','Predicted without edge extraction','Actual');
% set(leg,'FontSize',FontSize-2,'Location','northoutside')
% pos = get(leg,'Position');
% pos(2) = pos(2)+0.14;
% set(leg,'Position',pos)
qe0 = baumgartner2014_pmv2ppp('QE','chance');
subplot(212)
h(1) = plot(xticks-dx,qe,'ks');
set(h(1),'MarkerFaceColor','k','MarkerSize',MarkerSize,'LineStyle','-')
hold on
h(3) = plot(xticks+dx,noDCN.qe,'kd');
set(h(3),'MarkerFaceColor','k','MarkerSize',MarkerSize-1,'LineStyle','--')
h(2) = plot(xticks([1 2 5]),data.qe([1 2 5]),'ko');
% In Baumgartner et al. (2014), we accidentially missed the actual data
% for the -20dB and -40dB conditions. Uncomment the following line to
% also show these data points.
% h(2) = plot(xticks,data.qe,'ko');
set(h(2),'MarkerFaceColor','w','MarkerSize',MarkerSize,'LineStyle','-')
plot([-0.5 4.5],[qe0 qe0],'k:') % chance performance
ylabel('Quadrant Err. (%)','FontSize',FontSize)
set(gca,'XTick',xticks,'XTickLabel',data.meta,'FontSize',FontSize,...
'XLim',[-0.5 4.5],'YLim',[-3 54],'YMinorTick','on')
end
end
%% ------ TAB 2 ----------------------------------------------------------
if flags.do_tab2
[qe_exp,pe_exp,qe_part,pe_part] = amt_cache('get','spatstrat_do0',flags.cachemode);
if isempty(qe_exp)
latdivision = [-20,0,20]; % lateral angle
dlat = 10;
% Experimental Settings
Conditions = {'BB','LP','W'};
%% Computations
for ido = 0:1
if ido == 1
s = data_baumgartner2014('pool',flags.cachemode);
else % recalib
s = data_baumgartner2014('pool','do',0,flags.cachemode);
end
for C = 1:length(Conditions)
Cond = Conditions{C};
%% Data
% Experimental data
data = data_majdak2013(Cond);
% Consider only actual participants
idpart = [];
for ii = 1:length(data)
idpart = [idpart,find(ismember({s.id},data(ii).id))];
end
s = s(idpart);
for ll = 1:length(s)
s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx;
for ii = 1:length(latdivision)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
mm2 = s(ll).itemlist(idlat,:);
s(ll).target{ii} = mm2(:,6); % polar angle of target
s(ll).response{ii} = mm2(:,8); % polar angle of response
end
end
for ll = 1:length(s)
for ii = 1:length(latdivision)
s(ll).spdtfs{ii} = 0; % init
s(ll).polang{ii} = 0; % init
[s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(...
latdivision(ii),s(ll).Obj);
if C == 1 % Learn
s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
elseif C == 2 % Dummy
temp=amt_load('baumgartner2014','spatstrat_lpfilter.mat');
s(ll).spdtfs_c{ii} = filter(temp.blp,temp.alp,s(ll).spdtfs{ii});
elseif C == 3 % Warped
s(ll).spdtfs_c{ii} = local_warphrtf(s(ll).spdtfs{ii},s(ll).fs);
end
end
end
%% Run Model
for ll = 1:length(s)
fh = [8500,18000]; % Hz
for ff=1:length(fh)
qe = zeros(1,length(latdivision));
pe = zeros(1,length(latdivision));
qe_t = zeros(1,length(latdivision));
pe_t = zeros(1,length(latdivision));
for ii = 1:length(latdivision)
if C == 1
fhigh = 18000;
else
fhigh = fh(ff);
end
[s(ll).p{ii},rang] = baumgartner2014(...
s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',latdivision(ii),...
'polsamp',s(ll).polang{ii},'do',ido,'fhigh',fhigh);
respangs{ii} = rang;
[ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , s(ll).polang{ii} , rang , s(ll).target{ii} );
end
% Model results of participants
if length(latdivision) == 3
qe_part(ll,C,2*ido+ff) = (qe_t(1)*length(s(ll).target{1}) + ...
qe_t(2)*length(s(ll).target{2}) + ...
qe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
pe_part(ll,C,2*ido+ff) = (pe_t(1)*length(s(ll).target{1}) + ...
pe_t(2)*length(s(ll).target{2}) + ...
pe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
else
s(ll).qe_part(C,2*ido+ff) = mean(qe_t);
s(ll).pe_part(C,2*ido+ff) = mean(pe_t);
end
end
% Actual experimental results
qe_exp(ll,C) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
pe_exp(ll,C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
% s(ll).Nt(C,1) = size(s(ll).itemlist,1);
end
end
end
s = rmfield(s,{'Obj','spdtfs_c','spdtfs'});% reduce file size
amt_cache('set','spatstrat_do0',qe_exp,pe_exp,qe_part,pe_part)
end
result = struct('qe_exp',qe_exp,'pe_exp',pe_exp,'qe_part',qe_part,'pe_part',pe_part);
meta = {'DCN no, BWA yes';...
'DCN no, BWA no ';...
'DCN yes, BWA yes';...
'DCN yes, BWA no ';...
};
[qe0,pe0] = baumgartner2014_pmv2ppp('chance');
qe_exp = permute(result.qe_exp,[2,1]);
pe_exp = permute(result.pe_exp,[2,1]);
qe_all = permute(result.qe_part,[2,1,3]);
pe_all = permute(result.pe_part,[2,1,3]);
% Statistics
for cond = 1:3
if cond == 1
amt_disp('BB:','documentation');
elseif cond == 2
amt_disp('LP:','documentation');
else
amt_disp('W:','documentation');
end
Ns = size(pe_exp,2);
group{1} = repmat(meta{1},Ns,1);
for im = 2:length(meta)
group{1} = [group{1} ; repmat(meta{im},Ns,1)];
end
group{2} = repmat(1:Ns,1,length(meta));
data = sqrt(((qe_all(cond,:,:) - repmat(qe_exp(cond,:),[1,1,length(meta)]))/qe0).^2) + ...
sqrt(((pe_all(cond,:,:) - repmat(pe_exp(cond,:),[1,1,length(meta)]))/pe0).^2);
[p,t,stat] = friedman(squeeze(data),1);
% [p,t,stat] = anovan(data(:),group,'display','off');
amt_disp([' Chi-sq = ' num2str(t{2,5},'%5.2f') ', p = ' num2str(p(1),'%3.3f')],'documentation');
if p(1) < 0.05
figure
[c,m,h,nms] = multcompare(stat,'display','on');
set(gca,'YTickLabel',flipud(meta))
end
end
e_qe = zeros(length(meta),3); % model, BB/LP/W
e_pe = e_qe;
r_pe = e_qe;
r_qe = e_qe;
for m = 1:length(meta)
for c = 1:3
e_qe(m,c) = rms(qe_all(c,:,m) - qe_exp(c,:));
e_pe(m,c) = rms(pe_all(c,:,m) - pe_exp(c,:));
tmp.r = corrcoef(qe_all(c,:,m) , qe_exp(c,:));
r_qe(m,c) = tmp.r(2);
tmp.r = corrcoef(pe_all(c,:,m) , pe_exp(c,:));
r_pe(m,c) = tmp.r(2);
end
end
%% Write Table
mtx = [e_pe e_qe];
mtx(:,1:2:end) = e_pe;
mtx(:,2:2:end) = e_qe;
mtx = round(mtx*10)/10;
columnLabels = {'Spect. Proc.',...
'$e_\mathrm{PE}$','$e_\mathrm{QE}$',...
'$e_\mathrm{PE}$','$e_\mathrm{QE}$',...
'$e_\mathrm{PE}$','$e_\mathrm{QE}$'};
rowLabels = meta;
varargout{1} = mtx;
varargout{2} = rowLabels;
varargout{3} = columnLabels;
end
%% ------ TAB 3 ----------------------------------------------------------
if flags.do_tab3
[s,qe,pe,qe_exp,pe_exp,latseg,bwcoef] = amt_cache('get','binWeighting',flags.cachemode);
if isempty(s)
bwcoef = [13 eps -eps Inf];
latseg = -60:20:60; % centers of lateral segments
dlat = 10; % lateral range (+-) of each segment
s = data_baumgartner2014('baseline',flags.cachemode);
qe_exp = zeros(length(s),length(latseg));
pe_exp = zeros(length(s),length(latseg));
for ll = 1:length(s)
s(ll).target = [];
s(ll).response = [];
s(ll).Nt = [];
for ii = 1:length(latseg)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latseg(ii)+dlat & latresp > latseg(ii)-dlat;
s(ll).mm2 = s(ll).itemlist(idlat,:);
s(ll).mm2(:,7) = 0; % set lateral angle to 0deg such that localizationerror works outside +-30deg
pe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'rmsPmedianlocal'));
qe_exp(ll,ii) = real(localizationerror(s(ll).mm2,'querrMiddlebrooks'));
s(ll).target{ii} = real(s(ll).mm2(:,6)); % polar angle of target
s(ll).response{ii} = real(s(ll).mm2(:,8)); % polar angle of response
s(ll).Nt{ii} = length(s(ll).target{ii});
end
end
%% LocaMo
qe = zeros(length(s),length(latseg),length(bwcoef));
pe = zeros(length(s),length(latseg),length(bwcoef));
for b = 1:length(bwcoef)
for ll = 1:length(s)
for ii = 1:length(latseg)
s(ll).sphrtfs{ii} = 0; % init
s(ll).p{ii} = 0; % init
[s(ll).sphrtfs{ii},polang{ii}] = extractsp( latseg(ii),s(ll).Obj );
[s(ll).p{ii},respangs{ii}] = baumgartner2014(...
s(ll).sphrtfs{ii},s(ll).sphrtfs{ii},s(ll).fs,...
'S',s(ll).S,'lat',latseg(ii),'polsamp',polang{ii},...
'bwcoef',bwcoef(b));
if s(ll).Nt{ii} > 0
[ qe(ll,ii,b),pe(ll,ii,b) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , polang{ii} , respangs{ii} , s(ll).target{ii});
else
qe(ll,ii,b) = NaN;
pe(ll,ii,b) = NaN;
end
end
end
amt_disp([num2str(b,'%1.0f') ' of ' num2str(length(bwcoef),'%1.0f') ' completed']);
end
s = rmfield(s,{'Obj','itemlist','mm2','sphrtfs'}); % reduce file size
amt_cache('set','binWeighting',s,qe,pe,qe_exp,pe_exp,latseg,bwcoef)
end
% load(fn);
varargout{1} = {s,qe,pe,qe_exp,pe_exp,latseg,bwcoef};
%% # of targets
Ns = length(s);
Nlat = length(latseg);
Ntlat = zeros(Ns,Nlat);
relfreq = zeros(Ns,Nlat);
Ntall = zeros(Ns,1);
for jj = 1:Ns
Ntlat(jj,:) = [s(jj).Nt{:}];
Ntall(jj) = sum(Ntlat(jj,:));
relfreq(jj,:) = Ntlat(jj,:)/Ntall(jj);
end
relfreq = relfreq.*repmat(Ntall,1,Nlat)/sum(Ntall);
%% Pooling to lateralization
idlat0 = round(Nlat/2);
idleft = idlat0-1:-1:1;
idright = idlat0+1:Nlat;
latseg = latseg(idlat0:end);
relfreqLR = Ntlat(:,idleft) ./ (Ntlat(:,idleft) + Ntlat(:,idright) + eps);
relfreq = [relfreq(:,idlat0) , relfreq(:,1:idlat0-1) + relfreq(:,Nlat:-1:idlat0+1)];
pe_exp = [pe_exp(:,idlat0) , relfreqLR.*pe_exp(:,idleft) + (1-relfreqLR).*pe_exp(:,idright)];
qe_exp = [qe_exp(:,idlat0) , relfreqLR.*qe_exp(:,idleft) + (1-relfreqLR).*qe_exp(:,idright)];
%% Evaluation Metrics
for b=1:length(bwcoef)
% pooling lat.
pe_b = [pe(:,idlat0,b) , relfreqLR.*pe(:,idleft,b) + (1-relfreqLR).*pe(:,idright,b)];
qe_b = [qe(:,idlat0,b) , relfreqLR.*qe(:,idleft,b) + (1-relfreqLR).*qe(:,idright,b)];
idnum = not(isnan(pe_exp) | isnan(pe_b));
dpe(b) = sqrt( relfreq(idnum)' * (pe_exp(idnum) - pe_b(idnum)).^2 );
dqe(b) = sqrt( relfreq(idnum)' * (qe_exp(idnum) - qe_b(idnum)).^2 );
Mpe(b) = relfreq(idnum)' * pe_b(idnum);
Mqe(b) = relfreq(idnum)' * qe_b(idnum);
r = corrcoef(pe_exp(idnum),pe_b(idnum));
r_pe(b) = r(2);
r = corrcoef(qe_exp(idnum),qe_b(idnum));
r_qe(b) = r(2);
end
%% Table
% Write Table
mtx = [round([dpe' dqe']*10)/10 , round([r_pe' r_qe']*100)/100 , round([Mpe' Mqe']*10)/10];
columnLabels = {'','$e_\mathrm{PE}$','$e_\mathrm{QE}$','$r_\mathrm{PE}$',...
'$r_\mathrm{QE}$','$\overline{\mathrm{PE}}$','$\overline{\mathrm{QE}}$'};
rowLabels = {'$\Phi = 13^\circ$','$\Phi \rightarrow +0^\circ$','$\Phi \rightarrow -0^\circ$','$\Phi \rightarrow \infty^\circ$'};
varargout{1} = mtx;
varargout{2} = rowLabels;
varargout{3} = columnLabels;
end
end
%% ------------------------------------------------------------------------
% ---- INTERNAL FUNCTIONS ------------------------------------------------
% ------------------------------------------------------------------------
function hM_warped = local_warphrtf(hM,fs)
% warps HRTFs acc. to Walder (2010)
% Usage: hM_warped = warp_hrtf(hM,fs)
N = fs;
fu = 2800;
fowarped = 8500;
fo = 16000;
fscala = [0:fs/N:fs-fs/N]';
hM_warped = zeros(512,size(hM,2),size(hM,3));
fuindex = max(find(fscala <= fu)); % 2800
fowindex = min(find(fscala >= fowarped));
foindex = min(find(fscala >= fo));
for canal = 1:size(hM,3)
for el = 1:size(hM,2)
yi = ones(fs/2+1,1)*(10^-(70/20));
flin1 = [fscala(1:fuindex-1)];
flin2 = [linspace(fscala(fuindex),fscala(foindex),fowindex-fuindex+1)]';
fscalawarped = [flin1 ; flin2];
% interpolate
x = fscala(1:foindex);
H = fft(hM(:,el,canal),N);
Y = H(1:foindex);
xi = fscalawarped;
yi(1:length(xi),1) = interp1(x, Y, xi,'linear');
yges=([yi; conj(flipud(yi(2:end-1)))]);
hges = ifft([yges(1:end)],length(yges));
hges=fftshift(ifft(yges));
hwin=hges(fs/2-256:fs/2+768);
hwinfade = local_FWfade(hwin,512,24,96,192);
hM_warped(1:end,el,canal)=hwinfade;
end
end
end
function [syncrnfreq, GETtrain] = local_getVocoder(filename,in,channum,lower,upper,alpha,GaussRate,stimpar)
warning('off')
% channel/timing parameters
srate=stimpar.SamplingRate;
nsamples=length(in); % length of sound record
duration=nsamples/srate; % duration of the signal (in s)
t=0:1/srate:duration;
t=t(1:nsamples);
extendedrange = 0;
if alpha == -1 % Log12ER case
extendedrange = 1;
alpha = 0.28;
elseif alpha == 0 % use predefined alphas
switch channum
case 3
alpha=1.42;
case 6
alpha=0.67;
case 9
alpha=0.45;
case 12
alpha=0.33;
case 18
alpha=0.22;
case 24
alpha=0.17;
otherwise
error(['Alpha not predefined for channels number of ' num2str(channu)]);
end
end
% Synthesis: These are the crossover frequencies that the output signal is mapped to
crossoverfreqs = logspace( log10(lower), log10(upper), channum + 1);
if extendedrange == 1
crossoverfreqs = [300,396,524,692,915,1209,1597,2110,2788,4200,6400,10000,16000];
end
syncrnfreq(:,1)=crossoverfreqs(1:end-1);
syncrnfreq(:,2)=crossoverfreqs(2:end);
for i=1:channum
cf(i) = sqrt( syncrnfreq(i,1)*syncrnfreq(i,2) );
end
% Pulse train parameters
Gamma = alpha*cf;
if extendedrange == 1
Gamma(9) = 1412;
Gamma(10) = 2200;
Gamma(11) = 3600;
Gamma(12) = 6000;
end
N = duration*GaussRate; % number of pulses
fN = floor(N);
Genv=zeros(fN,nsamples);
GETtrain=zeros(channum,nsamples);
% Make pulse trains
for i = 1:channum
Teff = 1000/Gamma(i);
if Teff > 3.75
% if modulation depth is not 100%, make pulse train then modulate
for n = 1:N
% delay pulses by half a period so first Gaussian pulse doesn't
% start at a max
T = (n-0.5)/N*duration;
Genv(n,:) = sqrt(Gamma(i)) * exp(-pi*(Gamma(i)*(t-T)).^2);
end
Genv_train(i,:) = sum(Genv);
%modulate carrier
GETtrain(i,:) = Genv_train(i,:) .* sin(2*pi*cf(i)*t);
%normalize energy
Energy(i) = norm(GETtrain(i,:))/sqrt(length(t));
% Energy(i) = rms(GETtrain(i,:)); % !!!!!!!!!!!!
GETtrain(i,:) = GETtrain(i,:)/Energy(i);
else
% if modulation depth is 100%, make modulated pulses and replicate
T=(0.5)/N*duration;
Genv=zeros(fN,nsamples);
Genv(1,:) = sqrt(Gamma(i)) * exp(-pi*(Gamma(i)*(t-T)).^2) .* sin(2*pi*cf(i)*t - T + pi/4); %!!! (t-T)
Genv=repmat(Genv(1,:),[fN 1]);
for n=1:N
T = round((n)/N*nsamples);
Genv(n,:)=circshift(Genv(n,:),[1 T-1]);
end
GETtrain(i,:) = sum(Genv);
%normalize energy
Energy(i) = norm(GETtrain(i,:))/sqrt(length(t));
% Energy(i) = rms(GETtrain(i,:)); % !!!!!!!!!!!!
GETtrain(i,:) = GETtrain(i,:)/Energy(i);
end
end
end
function out=local_channelize(fwavout, h, h0, in, channum, corners, syncrnfreq, ...
GETtrain, stimpar, amp, fadein, fadeout)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% *** v1.2.0 %
% GET Vocoder scaled by energy, not envelope %
% %
% *** v1.1.0 %
% Added Gaussian Envelope Tone (GET) Vocoder %
% GET pulse train is generated and passed to this function %
% M. Goupell, May 2008 %
% %
% *** v1.0.0 %
% Modified from ElecRang/matlab/makewav.m v1.3.1 %
% To be used with Loca by M. Goupell, Nov 2007 %
% Now program receives a sound rather than reading a speech file %
% Rewrote according to the specifications (PM, Jan. 2008) %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% h = hrtf
% h0 = hrtf for reference position
% in = reference noise
% noise = number of channels of noise vectors
srate=stimpar.SamplingRate;
N=length(in); % length of sound record
d=0.5*srate; % frequency scalar
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Read corner frequencies for channels %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Synthesis: These are the crossover frequencies that the output signal is mapped to
% calculated now in GETVocoder, passed to this function
% Analysis: These are the crossover frequencies that the input signal is subdivided by
if length(corners)<=channum
error('You need at least one more corner frequency than number of channels');
end
anacrnfreq(:,1)=corners(1:end-1);
anacrnfreq(:,2)=corners(2:end);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Analysis: Filter Signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
inX=fftfilt(h,in);
order=4; %order of butterworth filter
% out=zeros(1,N);
% filtX=zeros(channum,N);
for i=1:channum
[b, a]=butter(order, anacrnfreq(i,:)/d);
out=filter(b, a, inX); % bandpass-filtering
% E(i)=norm(out,1)/sqrt(N);
E(i) = rms(out);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Synthesis %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% each channel
for i=1:channum
outX(i,:) = GETtrain(i,:) * E(i);
end
% sum me up scotty
out=sum(outX,1);
% for i = 1:channum
% subplot(channum/3,3,i)
% plot(outX(i,(1:480)))
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Save file %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
out=out*(10^(amp/20))/sqrt(sum(out.^2))*sqrt(sum(in.^2))*sqrt(sum(h.^2))/sqrt(sum(h0.^2));
ii=max(max(abs(out)));
if ii>=1
error(['Maximum amplitude value is ' num2str(20*log10(ii)) 'dB. Set the HRTF scaling factor lower to avoid clipping']);
end
out=local_FWfade(out,0,fadein,fadeout);
% wavwrite(out,srate,stimpar.Resolution,fwavout);
end
function out = local_FWfade(inp, len, fadein, fadeout, offset)
% FW_FADE crop/extend and fade in/out a vector.
%
% OUT = FW_FADE(INP, LEN, FADEIN, FADEOUT, OFFSET) crops or extends with zeros the signal INP
% up to length LEN. Additionally, the result is faded in/out using HANN window with
% the length FADEIN/FADEOUT, respectively. If given, an offset can be added to show
% where the real signal begins.
%
% When used to crop signal, INP is cropped first, then faded out.
% When used to extend signal, INP is faded out first, then extended too provide fading.
%
% INP: vector with signal (1xN or Nx1)
% LEN: length of signal OUT (without OFFSET)
% FADEIN: number of samples to fade in, beginning from OFFSET
% FADEOUT: number of samples to fade out, ending at the end of OUT (without OFFSET)
% OFFSET: number of offset samples before FADEIN, optional
% OUT: cropped/extended and faded vector
%
% Setting a parameter to 0 disables corresponding functionality.
% ExpSuite - software framework for applications to perform experiments (related but not limited to psychoacoustics).
% Copyright (C) 2003-2010 Acoustics Research Institute - Austrian Academy of Sciences; Piotr Majdak and Michael Mihocic
% Licensed under the EUPL, Version 1.1 or ? as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "Licence")
% You may not use this work except in compliance with the Licence.
% You may obtain a copy of the Licence at: http://ec.europa.eu/idabc/eupl
% Unless required by applicable law or agreed to in writing, software distributed under the Licence is distributed on an "AS IS" basis, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
% See the Licence for the specific language governing permissions and limitations under the Licence.
% 7.11.2003
% 22.08.2005: improvement: INP may be 1xN or Nx1 now.
% Piotr Majdak (piotr@majdak.com)
ss=length(inp); % get length of inp
% offset
if ~exist('offset','var')
offset = 0;
end
offset=round(offset);
len=round(len);
fadein=round(fadein);
fadeout=round(fadeout);
if offset>ss
error('OFFSET is greater than signal length');
end
% create new input signal discarding offset
inp2=inp(1+offset:end);
ss=length(inp2);
% fade in
if fadein ~= 0
if fadein > ss
error('FADEIN is greater than signal length');
end
han=hanning(fadein*2);
if size(inp2,1)==1
han=han';
end
inp2(1:fadein) = inp2(1:fadein).*han(1:fadein);
end
% fade out window
if len == 0
len = ss;
end
if len <= ss
% crop and fade out
out=inp2(1:len);
% fade out
if fadeout ~= 0
if fadeout > len
error('FADEOUT is greater than cropped signal length');
end
han = hanning(2*fadeout);
if size(out,1)==1
han=han';
end
out(len-fadeout+1:end)=out(len-fadeout+1:end).*han(fadeout+1:end);
end
else
% fade out and extend
if fadeout ~= 0
if fadeout > ss
error('FADEOUT is greater than signal length');
end
han = hanning(2*fadeout);
inp2(ss-fadeout+1:end)=inp2(ss-fadeout+1:end).*han(fadeout+1:end);
end
out = [zeros(offset,1); inp2; zeros(len-ss,1)];
end
end
function local_middlebroxplot(x,quantiles,MarkerSize)
lilen = 0.14; % length of horizontal lines
% Symbols
plot(x,quantiles(1),'kx','MarkerSize',MarkerSize) % min
hold on
plot(x,quantiles(7),'kx','MarkerSize',MarkerSize) % max
% Horizontal lines
line(x+0.5*[-lilen,lilen],repmat(quantiles(2),2),'Color','k') % lower whisker
line(x+[-lilen,lilen],repmat(quantiles(3),2),'Color','k') % 25% Quartile
line(x+[-lilen,lilen],repmat(quantiles(4),2),'Color','k') % Median
line(x+[-lilen,lilen],repmat(quantiles(5),2),'Color','k') % 75% Quartile
line(x+0.5*[-lilen,lilen],repmat(quantiles(6),2),'Color','k') % upper whisker
% Vertical lines
line([x,x],quantiles(2:3),'Color','k') % connector lower whisker
line([x,x],quantiles(5:6),'Color','k') % connector upper whisker
line([x,x]-lilen,quantiles([3,5]),'Color','k') % left box edge
line([x,x]+lilen,quantiles([3,5]),'Color','k') % left box edge
end
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