THE AUDITORY MODELING TOOLBOX
Go to function
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.
%
% 'fig5_baumgartner2015aro' Reproduce Fig.5 of Baumgartner et al. (2015):
% Effect of background noise on reliability of contralateral
% cues for various lateral eccentricities. Top row:
% Across-listener averages of performance measures for
% contralateral ear. Bottom row: Contralateral re ipsilateral
% averages of performance measures.
%
% 'fig2_baumgartner2015jaes' Reproduce Fig.2 of Baumgartner and Majdak (2015):
% Example showing the spectral discrepancies obtained by VBAP.
% The targeted spectrum is the HRTF for 20 deg polar angle.
% The spectrum obtained by VBAP is the superposition of two
% HRTFs from directions 40 deg polar angle apart of each
% other with the tar- geted source direction centered in between.
%
% 'fig4_baumgartner2015jaes' Reproduce Fig.4 of Baumgartner and Majdak (2015):
% Response predictions to sounds created by VBAP with two
% loudspeakers in the median plane positioned at polar
% angles of -15 and 30 deg, respectively. Predictions for
% two exemplary listeners and pooled across all listeners.
% Each column of a panel shows the predicted PMV of
% polar-angle responses to a certain sound. Note the
% inter-individual differences and the generally small
% probabilities at response angles not occupied by the loudspeakers.
%
% 'fig5_baumgartner2015jaes' Reproduce Fig.5 of Baumgartner and Majdak (2015):
% Listener-specific increases in polar error as a function of
% the panning angle. Increase in polar error defined as
% the difference between the polar error obtained by the
% VBAP source and the polar error obtained by the real
% source at the corresponding panning angle. Same loudspeaker
% arrangement as for Fig. 4. Note the large inter-individual
% differences and the increase in polar error being largest
% at panning angles centered between the loudspeakers, i.e.,
% at panning ratios around R = 0 dB.
%
% 'fig6_baumgartner2015jaes' Reproduce Fig.6 of Baumgartner and Majdak (2015):
% Panning angles for the loudspeaker arrangement of Fig. 4
% judged best for reference sources at polar angles of
% 0 or 15 deg in the median plane. Comparison between
% experimental results from [2] and simulated results
% based on various response strategies: PM, CM, and both
% mixed. Dotted horizontal line: polar angle of the reference
% source. Hor- izontal line within box: median;
% box: inter-quartile range (IQR);
% whisker: within quartile +-1.5 IQR;
% star: outlier.
% Note that the simulations predicted a bias similar to
% the results from Pulkki (2001) for the reference source at 0 deg.
%
% 'tab1_baumgartner2015jaes' Reproduce Tab.1 of Baumgartner and Majdak (2015):
% Means and standard deviations of responded panning angles for the
% two reference sources (Ref.) together with corresponding GOFs
% evaluated for the actual results from Pulkki (2001) and
% predicted results based on various response strategies.
% Note the relatively large GOFs for the simulations based on
% mixed response strategies indicating a good correspondence
% between actual and predicted results.
%
% 'fig7_baumgartner2015jaes' Reproduce Fig.7 of Baumgartner and Majdak (2015):
% Increase in polar error (defined as in Fig. 5) as a function
% of loudspeaker span in the median plane with panning ratio
% R = 0 dB. Black line with gray area indicates mean
% +-1 standard deviation across listeners. Note that the
% increase in polar error monotonically increases with
% loudspeaker span.
%
% 'fig8_baumgartner2015jaes' Reproduce Fig.8 of Baumgartner and Majdak (2015):
% Effect of loudspeaker span in the median plane on coefficient
% of determination, r^2, for virtual source directions
% created by VBAP. Separate analysis for frontal, rear,
% and overall (frontal and rear) targets. Data pooled
% across listeners. Note the correspondence with the
% results obtained by Bremen et al. (2010).
%
% 'tab3_baumgartner2015jaes' Reproduce Tab.3 of Baumgartner and Majdak (2015):
% Predicted across-listener average of increase in polar
% errors as referred to a reference system containing
% loudspeakers at all considered directions. Distinction
% between mean and maximum degradation across directions.
% N: Number of loudspeakers. Ele.: Elevation of second layer.
% Notice that this elevation has a larger effect on mean
% and maximum degradation than N.
%
% 'fig9_baumgartner2015jaes' Reproduce Fig.9 of Baumgartner and Majdak (2015):
% Predicted polar error as a function of the lateral and
% polar angle of a virtual source created by VBAP in
% various multichannel systems. Open circles indicate
% loudspeaker directions. Reference shows polar error
% predicted for a real source placed at the virtual
% source directions investigated for systems A, ..., F.
%
% Further, cache flags (see amt_cache) and plot flags can be specified:
%
% 'plot' Plot the output of the experiment. This is the default.
%
% 'noplot' 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 hrtf/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');
%
% To display Fig.5 of Baumgartner et al. (2015) use :
%
% exp_baumgartner2014('fig5_baumgartner2015aro');
%
% To display Fig.2 of Baumgartner and Majdak (2015) use :
%
% exp_baumgartner2014('fig2_baumgartner2015jaes');
%
% To display Fig.4 of Baumgartner and Majdak (2015) use :
%
% exp_baumgartner2014('fig4_baumgartner2015jaes');
%
% To display Fig.5 of Baumgartner and Majdak (2015) use :
%
% exp_baumgartner2014('fig5_baumgartner2015jaes');
%
% To display Fig.6 of Baumgartner and Majdak (2015) use :
%
% exp_baumgartner2014('fig6_baumgartner2015jaes');
%
% To display Fig.7 of Baumgartner and Majdak (2015) use :
%
% exp_baumgartner2014('fig7_baumgartner2015jaes');
%
% To display Fig.8 of Baumgartner and Majdak (2015) use :
%
% exp_baumgartner2014('fig8_baumgartner2015jaes');
%
% To display Fig.9 of Baumgartner and Majdak (2015) use :
%
% exp_baumgartner2014('fig9_baumgartner2015jaes');
%
% To display Tab.1 of Baumgartner and Majdak (2015) use :
%
% exp_baumgartner2014('tab1_baumgartner2015jaes');
%
% To display Tab.3 of Baumgartner and Majdak (2015) use :
%
% exp_baumgartner2014('tab3_baumgartner2015jaes');
%
% See also: baumgartner2014 data_baumgartner2014
%
% References:
% R. Baumgartner, P. Majdak, and B. Laback. The reliability of
% contralateral spectral cues for sound localization in sagittal planes.
% In Midwinter Meeting of the Association for Research in Otolaryngology,
% Baltimore, MD, Feb 2015.
%
% 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.
%
% R. Baumgartner and P. Majdak. Modeling Localization of Amplitude-Panned
% Virtual Sources in Sagittal Planes. J. Audio Eng. Soc.,
% 63(7/8):562--569, Aug. 2015. [1]http ]
%
% References
%
% 1. http://www.aes.org/e-lib/browse.cfm?elib=17842
%
% bremen2010pinna goupell2010numchan macpherson2007 macpherson2003ripples
% majdak2013spatstrat middlebrooks1999nonindividualized morimoto2001
% pulkki2001localization
%
% Url: http://amtoolbox.sourceforge.net/amt-0.10.0/doc/experiments/exp_baumgartner2014.php
% Copyright (C) 2009-2020 Piotr Majdak and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 0.10.0
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR: Robert Baumgartner
%% ------ 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',...
'fig5_baumgartner2015aro',...
'fig2_baumgartner2015jaes','fig4_baumgartner2015jaes',...
'fig5_baumgartner2015jaes','fig6_baumgartner2015jaes',...
'fig7_baumgartner2015jaes','fig8_baumgartner2015jaes',...
'fig9_baumgartner2015jaes','tab1_baumgartner2015jaes',...
'tab3_baumgartner2015jaes',...
};
definput.flags.plot = {'plot','noplot'};
[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'])
% 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,'nocolorbar');
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 several hours!','progress')
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);
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,flags.cachemode);
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'])
% 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'])
if flags.do_plot
%% Plot residues for various gamma
% Interpolate data
gamma_int = logspace(0,2.1,1000);
inttype = 'cubic';
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 = 'cubic';
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,'progress');
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,'progress');
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])
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} = warp_hrtf(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,'nocolorbar')
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} = warp_hrtf(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')]);
[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')]);
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])
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] = 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) = 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,'nocolorbar');
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] = 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) = 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.'],'progress')
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')]);
[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')]);
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!','progress');
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')],'progress');
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)
middlebroxplot(1-dx,qe_own.quantiles,kv.MarkerSize)
plot(1-dx,qe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
middlebroxplot(1+dx,data.qe_own.quantiles,kv.MarkerSize)
plot(1+dx,data.qe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
middlebroxplot(2-dx,qe_other.quantiles,kv.MarkerSize)
plot(2-dx,qe_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
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)
middlebroxplot(1-dx,pe_own.quantiles,kv.MarkerSize)
plot(1-dx,pe_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
middlebroxplot(1+dx,data.pe_own.quantiles,kv.MarkerSize)
plot(1+dx,data.pe_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
middlebroxplot(2-dx,pe_other.quantiles,kv.MarkerSize)
plot(2-dx,pe_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
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)
middlebroxplot(1-dx,pb_own.quantiles,kv.MarkerSize)
plot(1-dx,pb_own.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
middlebroxplot(1+dx,data.pb_own.quantiles,kv.MarkerSize)
plot(1+dx,data.pb_own.mean,data.Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',data.MFC)
middlebroxplot(2-dx,pb_other.quantiles,kv.MarkerSize)
plot(2-dx,pb_other.mean,Marker,'MarkerSize',kv.MarkerSize,'MarkerFaceColor',MFC)
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'],'progress');
end
amt_disp(' ','progress')
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};
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):')
amt_disp(['w/ PSGE: r = ' num2str(rDCN,'%0.2f')])
amt_disp(['w/o PSGE: r = ' num2str(rnoDCN,'%0.2f')])
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,'signals','HarvardWords');
% if not(exist(fnHarvard,'dir'))
% amt_disp('The Harvard word list is missing.')
% amt_disp('Please, contact Virginia Best (ginbest@bu.edu) or Craig Jin (craig.jin@sydney.edu.au) for providing their speech recordings.')
% amt_disp(['Then, move the folder labeled HarvardWords to: ' fullfile(amt_basepath,'auxdata','baumgartner2014') '.'])
% return
% end
fnHarvard = fullfile(amt_basepath,'auxdata','baumgartner2014','HarvardWords');
amt_disp('Note that this computation may take several hours!','progress')
%% 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'],'progress')
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} = warp_hrtf(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:')
elseif cond == 2
amt_disp('LP:')
else
amt_disp('W:')
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')])
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'],'progress')
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
%% ------ FIG 5 of baumgartner2015aro -------------------------------------
if flags.do_fig5_baumgartner2015aro
[perr,qerr,snrFront,bwcoef,lat] = amt_cache('get','fig5_baumgartner2015aro',flags.cachemode);
if isempty(perr)
snrFront = -20:2:40; % in dB
latecc = [10,30,50];% lateral eccentricities
mrs = 17; % no sensorimotor mapping
s = data_baumgartner2014('pool',flags.cachemode);
bwcoef = [13,+eps,-eps]; % configuration of binaural weighting stage (binaural, ipsilateral, contralateral
lat = [-fliplr(latecc),latecc];
maskerNoise = noise(0.05*s(1).Obj.Data.SamplingRate,1,'white');
targetNoise = noise(0.05*s(1).Obj.Data.SamplingRate-255,1,'white');
perr = nan(length(snrFront),length(bwcoef),length(lat),length(s));
qerr = nan(length(snrFront),length(bwcoef),length(lat),length(s));
for isub=1:length(s)
idfrontal = find(s(isub).Obj.SourcePosition(:,1)==0 & s(isub).Obj.SourcePosition(:,2)==0);
frontalDtfs = shiftdim(s(isub).Obj.Data.IR(idfrontal,:,:),2);
frontalTarget = convolve(targetNoise,frontalDtfs);
lvl = mean(dbspl(frontalTarget)); % level of frontal target stimulus in dB
for ilat = 1:length(lat)
[spdtfs,tang] = extractsp(lat(ilat),s(isub).Obj);
targets = convolve(targetNoise,spdtfs);
targets = reshape(targets,[length(targets),size(targets,2)/2,2]);
for isnr = 1:length(snrFront)
targetsPlusMasker = targets + ...
repmat(setdbspl(maskerNoise,lvl-snrFront(isnr)),[1,size(targets,2),2]);
for ibwc = 1:length(bwcoef)
[p,rang] = baumgartner2014(targetsPlusMasker,s(isub).Obj,...
'S',s(isub).S,'mrsmsp',mrs,...
'lat',lat(ilat),'bwcoef',bwcoef(ibwc));
[ qerr(isnr,ibwc,ilat,isub) , perr(isnr,ibwc,ilat,isub) ] = ...
baumgartner2014_pmv2ppp(p,tang,rang);
end
end
end
amt_disp([num2str(isub) ' of ' num2str(length(s)) ' completed'])
end
amt_cache('set','fig5_baumgartner2015aro',perr,qerr,snrFront,bwcoef,lat)
end
r = struct('perr',perr,'qerr',qerr,'snrFront',snrFront,'bwcoef',bwcoef,'lat',lat);
varargout{1} = r;
if flags.do_plot
% pool left/right
perr = (r.perr(:,:,length(r.lat)/2:-1:1,:) + r.perr(:,:,1+length(r.lat)/2:length(r.lat),:))/2;
qerr = (r.qerr(:,:,length(r.lat)/2:-1:1,:) + r.qerr(:,:,1+length(r.lat)/2:length(r.lat),:))/2;
latecc = r.lat(1+length(r.lat)/2:length(r.lat)); % lateral eccentricity
perr_ipsipro = squeeze(perr(:,3,:,:) - perr(:,2,:,:)); % contra minus ipsi
qerr_ipsipro = squeeze(qerr(:,3,:,:) - qerr(:,2,:,:));
snr_int = r.snrFront(1):r.snrFront(end);
figure
% display 0-error line
for ii = 1:2
subplot(2,2,2+ii)
plot(snr_int,zeros(length(snr_int),1),'k:')
hold on
end
color = [ 0.2081 0.1663 0.8292;...
0.8292 0.1663 0.2081;...
0.8081 0.6081 0.2081];
for ii=1:length(latecc)
subplot(2,2,1)
abspecontra_int = interp1(r.snrFront,mean(perr(:,ii,3,:),4),snr_int,'spline');
h(ii) = plot(snr_int,abspecontra_int,'Color',color(ii,:)); hold on
xlabel('SNR (dB)','FontSize',kv.FontSize)
ylabel('PE_{contra} (deg)','FontSize',kv.FontSize)
axis([-20,40,31,54])
set(gca,'FontSize',kv.FontSize)
subplot(2,2,2)
absqecontra_int = interp1(r.snrFront,mean(qerr(:,ii,3,:),4),snr_int,'spline');
h(ii) = plot(snr_int,absqecontra_int,'Color',color(ii,:)); hold on
xlabel('SNR (dB)','FontSize',kv.FontSize)
ylabel('QE_{contra} (deg)','FontSize',kv.FontSize)
axis([-20,40,6,49])
set(gca,'FontSize',kv.FontSize)
subplot(2,2,3)
pe_int = interp1(r.snrFront,mean(perr_ipsipro(:,ii,:),3),snr_int,'spline');
h(ii) = plot(snr_int,pe_int,'Color',color(ii,:));
xlabel('SNR (dB)','FontSize',kv.FontSize)
ylabel('PE_{contra} - PE_{ipsi} (deg)','FontSize',kv.FontSize)
axis([-20,40,-4,29])
set(gca,'FontSize',kv.FontSize)
subplot(2,2,4)
qe_int = interp1(r.snrFront,mean(qerr_ipsipro(:,ii,:),3),snr_int,'spline');
plot(snr_int,qe_int,'Color',color(ii,:))
xlabel('SNR (dB)','FontSize',kv.FontSize)
ylabel('QE_{contra} - QE_{ipsi} (deg)','FontSize',kv.FontSize)
axis([-20,40,-4,29])
set(gca,'FontSize',kv.FontSize)
end
subplot(2,2,3)
legendentries = [repmat('\phi = \pm',length(latecc),1) num2str(latecc(:)) repmat('\circ',length(latecc),1)];
leg = legend(h,legendentries,'Location','north');
set(leg,'FontSize',kv.FontSize)
end
end
%% ------------------------------------------------------------------------
% ---- baumgartner2015jaes -----------------------------------------------
% ------------------------------------------------------------------------
%% ------ FIG 2 of baumgartner2015jaes ------------------------------------
if flags.do_fig2_baumgartner2015jaes
pol1 = 0;
pol2 = 40;
s = data_baumgartner2014('pool');
fs = s(1).Obj.Data.SamplingRate;
[dtfs,pol] = extractsp(0,s(1).Obj);
polphant = pol1 + (pol2-pol1)/2;
dtf1 = 10*dtfs(:,pol==pol1,1);
dtf2 = 10*dtfs(:,pol==pol2,1);
dtfreal = 10*dtfs(:,pol==polphant,1);
dtfphant = (dtf1+dtf2)/2;
%%
if flags.do_plot
figure
set(gca,'LineWidth',1)
plotfft(fft(dtfphant),fs,'posfreq')
hold on
plotfft(fft(dtfreal),fs,'posfreq')
plotfft(fft(dtf1),fs,'posfreq')
plotfft(fft(dtf2),fs,'posfreq');
% Set line styles
Color = {[0.4660 0.6740 0.1880];...
[0.3010 0.7450 0.9330];...
[0.8500 0.3250 0.0980];...
[ 0 0.4470 0.7410]};
LineWidth = [.5,.5,1,1];
LineStyle = {':',':','--','-'};
ch = get(gca,'Children');
for ii = 1:length(ch)
set(ch(ii),'Color',Color{ii},'LineStyle',LineStyle{ii},'LineWidth',LineWidth(ii));
end
leg = legend([num2str(polphant) '\circ VBAP'],...
[num2str(polphant) '\circ source'],...
[' ' num2str(pol1) '\circ source'],...
[num2str(pol2) '\circ source']);
set(leg,'Location','southeast','FontSize',kv.FontSize)
set(gca,'XLim',[500,17500],'YLim',[-39.9,9.9],'FontSize',kv.FontSize)
xticks = get(gca,'XTick');
set(gca,'XTickLabel',xticks/1000)
xlabel({'Frequency (kHz)';' '},'FontSize',kv.FontSize)
end
end
%% ------ FIG 4 of baumgartner2015jaes ------------------------------------
if flags.do_fig4_baumgartner2015jaes
[peI,s,pol0,DL,respang] = amt_cache('get','panningangle',flags.cachemode);
if isempty(peI)
MRS = 0;
pol1 = -15; % polar angle of lower Lsp.
pol2 = 30; % polar angle of higher Lsp.
lat = 0; % must be 0 otherwise VBAP wrong!
s = data_baumgartner2014('pool');
dPol = pol2-pol1;
s(1).spdtfs = [];
[s(1).spdtfs,polang] = extractsp(lat,s(1).Obj);
idtest = find(polang >= pol1 & polang <= pol2);
qeI = zeros(length(s),length(idtest));
peI = qeI;
for ii = 1:length(idtest)
id1 = idtest(1); % ID lower pos.
id2 = idtest(end); % ID higher pos.
id0 = idtest(ii); % ID pos. of phantom source
pol0(ii) = polang(id0);
% VBAP
[p(1,1),p(1,2),p(1,3)] = sph2cart(lat,deg2rad(pol0(ii)),1);
[L(1,1),L(1,2),L(1,3)] = sph2cart(lat,deg2rad(pol1),1);
[L(2,1),L(2,2),L(2,3)] = sph2cart(lat,deg2rad(pol2),1);
g = p/L;
g = g/norm(g);
DL(ii) = db(g(1)) - db(g(2));
for ll = 1:length(s)
s(ll).spdtfs = extractsp(lat,s(ll).Obj);
% superposition
s(ll).dtfs2{ii} = g(1)*s(ll).spdtfs(:,id1,:) + g(2)*s(ll).spdtfs(:,id2,:);
[s(ll).p1(:,ii),respang] = baumgartner2014(...
s(ll).spdtfs(:,id0,:),s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
'mrsmsp',MRS,'polsamp',polang);
s(ll).p2(:,ii) = baumgartner2014(...
s(ll).dtfs2{ii},s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
'mrsmsp',MRS,'polsamp',polang);
[s(ll).qe1(ii),s(ll).pe1(ii)] = baumgartner2014_pmv2ppp(...
s(ll).p1(:,ii),polang(id0),respang);
[s(ll).qe2(ii),s(ll).pe2(ii)] = baumgartner2014_pmv2ppp(...
s(ll).p2(:,ii),polang(id0),respang);
% Increse of error
qeI(ll,ii) = s(ll).qe2(ii) - s(ll).qe1(ii);
peI(ll,ii) = s(ll).pe2(ii) - s(ll).pe1(ii);
end
amt_disp([num2str(ii) ' of ' num2str(length(idtest)) ' completed'],'progress');
end
amt_cache('set','panningangle',peI,s,pol0,DL,respang);
end
id1 = 'NH71'; % positive example
id2 = 'NH62'; % negative example
if flags.do_plot
cmp= [0.2081 0.1663 0.5292;
0.2052 0.2467 0.6931;
0.0843 0.3472 0.8573;
0.0157 0.4257 0.8789;
0.0658 0.4776 0.8532;
0.0777 0.5300 0.8279;
0.0356 0.5946 0.8203;
0.0230 0.6443 0.7883;
0.0485 0.6793 0.7341;
0.1401 0.7085 0.6680;
0.2653 0.7327 0.5916;
0.4176 0.7471 0.5142;
0.5624 0.7487 0.4529;
0.6872 0.7433 0.4029;
0.7996 0.7344 0.3576;
0.9057 0.7261 0.3105;
0.9944 0.7464 0.2390;
0.9847 0.8141 0.1734;
0.9596 0.8869 0.1190;
0.9763 0.9831 0.0538]; % parula colormap (defined for compatibility with older Matlab versions)
figure
p_pool = nan(size(s(1).p2,1),size(s(1).p2,2),length(s));
for ll = 1:length(s)
p_pool(:,:,ll) = s(ll).p2;
if strcmp(s(ll).id,id1)
subplot(1,4,1)
plot_baumgartner2014(s(ll).p2,pol0,respang,'cmax',0.08)
colormap(cmp)
title(s(ll).id,'FontSize',kv.FontSize)
xlabel('');
ylabel('Response angle (deg)','FontSize',kv.FontSize);
colorbar off
elseif strcmp(s(ll).id,id2)
subplot(1,4,2)
plot_baumgartner2014(s(ll).p2,pol0,respang,'cmax',0.08)
colormap(cmp)
xlabel('Panning angle (deg)','FontSize',kv.FontSize);
ylabel(''); set(gca,'YTickLabel',[]);
title(s(ll).id,'FontSize',kv.FontSize)
colorbar off
end
end
p_pool = mean(p_pool,3);
subplot(1,4,3)
plot_baumgartner2014(p_pool,pol0,respang,'cmax',0.08)
colormap(cmp)
title('Pool','FontSize',kv.FontSize)
xlabel('');
ylabel(''); set(gca,'YTickLabel',[]);
colorbar off
subplot(1,4,4)
ymax = 8;
y = -0.15:0.1:ymax+0.15;
Ny = length(y);
pcolor(1:2,y,repmat(y(:),1,2))
shading flat
axis tight
colormap(cmp)
title({' ';' '})
set(gca,'XTick',[],'YTick',0:1:ymax,...
'YDir','normal','YAxisLocation','right','FontSize',kv.FontSize)
ylabel({'Probability density (% per 5\circ)'},'FontSize',kv.FontSize)
set(gca,'Position',get(gca,'Position').*[1.05,1,0.3,1])
end
end
%% ------ FIG 5 of baumgartner2015jaes ------------------------------------
if flags.do_fig5_baumgartner2015jaes
[peI,s,pol0,DL,respang] = amt_cache('get','panningangle',flags.cachemode);
if isempty(peI)
exp_baumgartner2014('fig4_baumgartner2015jaes','noplot',flags.cachemode);
[peI,s,pol0,DL,respang] = amt_cache('get','panningangle',flags.cachemode);
end
figure
plot(peI')
ylabel('Increase in polar error (deg)')
% Panning angle axis
set(gca,'XTick',1:10,'XTickLabel',round(pol0),...
'YMinorTick','on','XLim',[1,10],'YLim',[-9,59])
xlabel('Panning angle (deg)')
end
%% ------ FIG 6 of baumgartner2015jaes ------------------------------------
if flags.do_fig6_baumgartner2015jaes
results = amt_cache('get','replicatePulkki2001',flags.cachemode);
if isempty(results)
amt_disp('Results may slightly vary from simulation to simulation because noise stimulus is not fixed.','progress')
MRS = 0;
pol1 = -15; % polar angle of lower Lsp.
pol2 = 30; % polar angle of higher Lsp.
polphant = [0,15]; % polar angles of phantom sources
Pmax = nan(length(polphant),23); % max Probabilities
panang_Pmax = nan(length(polphant),23); % panning angle selected by max P
panang_Cen = nan(length(polphant),23); % panning angle selected by centroid
for pp = 1:length(polphant)
lat = 0; % must be 0 otherwise VBAP wrong!
s = data_baumgartner2014('pool');
s(1).spdtfs = [];
[s(1).spdtfs,polang] = extractsp(lat,s(1).Obj);
% restrict response range
idrang = find(polang >= pol1 & polang <= pol2); % to range between loudspeakers
% idrang = find(polang <= 90); % to the front
idtest = find(polang >= pol1 & polang <= pol2);
id1 = find(polang >= pol1,1); % ID lower pos.
id2 = find(polang <= pol2,1,'last'); % ID higher pos.
tang = polang(id1:id2);
for ll = 1:length(s)
s(ll).spdtfs = extractsp(lat,s(ll).Obj);
for ii = 1:length(idtest)
id0 = idtest(ii); % ID pos. of phantom source
% VBAP
[p(1,1),p(1,2),p(1,3)] = sph2cart(lat,deg2rad(polang(id0)),1);
[L(1,1),L(1,2),L(1,3)] = sph2cart(lat,deg2rad(pol1),1);
[L(2,1),L(2,2),L(2,3)] = sph2cart(lat,deg2rad(pol2),1);
g = p/L;
g = g/norm(g);
DL(ii) = db(g(1)) - db(g(2));
% superposition
s(ll).dtfs2{ii} = g(1)*s(ll).spdtfs(:,id1,:) + g(2)*s(ll).spdtfs(:,id2,:);
[s(ll).p(:,ii),rang] = baumgartner2014(...
s(ll).dtfs2{ii},s(ll).spdtfs(:,idrang,:),s(ll).fs,'S',s(ll).S,...
'mrsmsp',MRS,'polsamp',polang(idrang),'rangsamp',5,...
'stim',noise(10000,1,'pink')); % phantom source
end
id_rang = rang == polphant(pp);
% interpolation between target angles
tang_int = tang(1):1:tang(end);
p_int = interp2(tang(:)',rang(:),s(ll).p,tang_int(:)',rang(:),'spline');
p_int = p_int./repmat(sum(p_int,1),size(p_int,1),1); % normalize to PMVs
% Variant 1: max P at source direction
[Pmax(pp,ll),id_best_pan] = max(p_int(id_rang,:));
panang_Pmax(pp,ll) = tang_int(id_best_pan);
% Variant 2: centroid closest to source direction
M = rang*p_int;
[tmp,id_best_pan] = min(abs(M-polphant(pp)));
panang_Cen(pp,ll) = tang_int(id_best_pan);
end
fprintf([num2str(pp) ' of ' num2str(length(polphant)) ' completed \n']);
end
results = struct('panang_Pmax',panang_Pmax,'Pmax',Pmax,...
'panang_Cen',panang_Cen,'polphant',polphant,'DL',DL,'rang',rang);
amt_cache('set','replicatePulkki2001',results);
end
[panang_varStrat,nCM_varStrat,p_varStrat,muhat,sigmahat] = amt_cache('get','replicatePulkki2001_varStrat',flags.cachemode);
if isempty(panang_varStrat)
pulkki01 = data_pulkki2001;
[muhat(1),sigmahat(1)] = normfit(pulkki01(1,:));
[muhat(2),sigmahat(2)] = normfit(pulkki01(2,:));
Nsub = size(results.panang_Cen,2);
panang_all = [];
p = [];
nCM = [];
ii = 1;
for inCM = 1:Nsub+1
c = nchoosek(1:Nsub,inCM-1); % listeners with CM strategy
lenC = size(c,1);
nCM = [nCM;repmat(inCM,lenC,1)];
panang_all = cat(3,panang_all , nan(2,Nsub,lenC));
p = [p ; nan(lenC,2)];
for ic = 1:lenC
idCM = false(1,Nsub);
idCM(c(ic,:)) = true;
panang_all(:,:,ii) = [results.panang_Cen(:,idCM) results.panang_Pmax(:,not(idCM))];
[tmp.h1,p(ii,1)] = kstest((panang_all(1,:,ii)-muhat(1))/sigmahat(1)); % center data acc. to target distribution and then test similarity to standard normal distribution
[tmp.h2,p(ii,2)] = kstest((panang_all(2,:,ii)-muhat(2))/sigmahat(2));
ii = ii+1;
end
disp([num2str(inCM) ' of ' num2str(Nsub+1) ' done'])
end
[tmp.min,idmax] = max(sum(p,2)); % best fit
panang_varStrat = panang_all(:,:,idmax);
nCM_varStrat = nCM(idmax);
p_varStrat = p(idmax,:);
amt_cache('set','replicatePulkki2001_varStrat',panang_varStrat,nCM_varStrat,p_varStrat,muhat,sigmahat)
end
if flags.do_plot
pulkki01 = data_pulkki2001;
Nsub = size(results.panang_Cen,2);
figure
for ii = 1:2
subplot(1,2,ii)
X = nan(size(pulkki01,2)*size(pulkki01,3),4);
X(:,1) = pulkki01(ii,:);
X(1:Nsub,2) = results.panang_Pmax(ii,:)';
X(1:Nsub,3) = results.panang_Cen(ii,:)';
X(1:Nsub,4) = panang_varStrat(ii,:)';
plot([0,5],(ii-1)*15*[1,1],'k:')
hold on
boxplot(X,'symbol','k*','outliersize',3)
set(gca,'YLim',[-17,32], 'XTickLabel',{'[2]','PM','CM','Mixed'});
if ~verLessThan('matlab','8.4'), set(gca,'XTickLabelRotation',45); end
if ii==1;
ylabel('Panning angle (deg)')
text(0.7,27.5,'0\circ')
else
set(gca,'YTickLabel',[]);
text(0.7,27.5,'15\circ')
end
end
end
end
%% ------ FIG 7 of baumgartner2015jaes ------------------------------------
if flags.do_fig7_baumgartner2015jaes
[peI,dPol] = amt_cache('get','loudspeakerspan',flags.cachemode);
if isempty(peI)
MRS = 0;
flags.do_fig20 = false;
flags.do_fig19 = false;
s = data_baumgartner2014('pool');
if flags.do_fig19
dPol = [0 30,60];
s = s(2); % NH12
else
dPol = 10:10:90;
end
lat = 0;
s(1).spdtfs = [];
[s(1).spdtfs,polang] = extractsp(lat,s(1).Obj);
peI = zeros(length(s),length(dPol));
peA = zeros(length(s),length(dPol)+1);
ii = 0;
while ii < length(dPol)
ii = ii + 1;
% find comparable angles
id0 = [];
id1 = [];
id2 = [];
for jj = 1: length(polang)
t0 = find( round(polang) == round(polang(jj)+dPol(ii)/2) );
t2 = find( round(polang) == round(polang(jj)+dPol(ii)) );
if ~isempty(t0) && ~isempty(t2)
id0 = [id0 t0];
id1 = [id1 jj];
id2 = [id2 t2];
end
end
pol2{ii} = (polang(id1)+polang(id2)) /2;
amt_disp([' Span: ' num2str(dPol(ii)) 'deg'],'progress');
for ll = 1:length(s)
s(ll).spdtfs = extractsp(lat,s(ll).Obj);
% superposition
s(ll).dtfs2{ii} = s(ll).spdtfs(:,id1,:) + s(ll).spdtfs(:,id2,:);
[s(ll).p1{ii},respang] = baumgartner2014(...
s(ll).spdtfs(:,id0,:),s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
'mrsmsp',MRS,'polsamp',polang);
s(ll).p2{ii} = baumgartner2014(...
s(ll).dtfs2{ii},s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
'mrsmsp',MRS,'polsamp',polang);
% RMS Error
[s(ll).qe1{ii},s(ll).pe1{ii}] = baumgartner2014_pmv2ppp(...
s(ll).p1{ii},polang(id0),respang);
[s(ll).qe2{ii},s(ll).pe2{ii}] = baumgartner2014_pmv2ppp(...
s(ll).p2{ii},pol2{ii},respang);
% Increse of error
peI(ll,ii) = s(ll).pe2{ii} - s(ll).pe1{ii};
end
end
amt_cache('set','loudspeakerspan',peI,dPol)
end
peIm = mean(peI,1);
peIstd = std(peI,1,1);
if flags.do_plot
figure
p = patch([dPol,fliplr(dPol)],[peIm+peIstd,fliplr(peIm-peIstd)],.7*[1,1,1]);
set(p,'EdgeColor',.7*[1,1,1]);
hold on
h = plot(dPol,peIm,'k');
set(h,'LineWidth',1)
set(gca,'XTick',dPol,'XTickLabel',dPol,...
'YMinorTick','on','Box','on','Layer','top')
axis([9.8,90.2,-2,27])
ylabel({'Increase in polar error (deg)'})
xlabel({'Loudspeaker span (deg)';''})
end
end
%% ------ FIG 8 of baumgartner2015jaes ------------------------------------
if flags.do_fig8_baumgartner2015jaes
[r2,dPol] = amt_cache('get','loudspeakerspan_r2',flags.cachemode);
if isempty(r2)
MRS = 0;
s = data_baumgartner2014('pool');
dPol = 0:10:105;
lat = 0;
runs = 100;
DL = -13:5:7; % panning ratios in dB
s(1).spdtfs = [];
[s(1).spdtfs,polang] = extractsp(lat,s(1).Obj);
r2 = zeros(length(s),length(dPol));
ii = 0;
while ii < length(dPol) % various spans
ii = ii + 1;
disp([' Span: ' num2str(dPol(ii)) 'deg']);
r2total = nan(length(s),length(DL));
r2front = nan(length(s),length(DL));
r2rear = nan(length(s),length(DL));
for idl = 1:length(DL)
% find comparable angles
id1 = []; % id of speaker with smaller polar angle
id2 = []; % id of speaker with larger polar angle
for jj = 1: length(polang)
% t0 = find( round(polang) == round(polang(jj)+ipf*dPol(ii)/polFrac) );
t2 = find( round(polang) == round(polang(jj)+dPol(ii)) );
if ~isempty(t2)
% id0 = [id0 t0];
id1 = [id1 jj];
id2 = [id2 t2];
end
end
g = inv([1,1;1,-10^(DL(idl)/20)]) * [1;0]; % derived from db(g1/g2)=DL and g1+g2=1 (chosen arbitrarily since energy preservation is not relevant here)
pol0 = nan(length(id1),1); % panning angles
for jj = 1:length(id1)
[L(1,1),L(1,2),L(1,3)] = sph2cart(0,deg2rad(polang(id1(jj))),1);
[L(2,1),L(2,2),L(2,3)] = sph2cart(0,deg2rad(polang(id2(jj))),1);
t = g'*L;
[azi,ele,tmp.r] = cart2sph(t(1),t(2),t(3));
[tmp.lat,pol0(jj)] = sph2hor(rad2deg(azi),rad2deg(ele));
end
for ll = 1:length(s) % various listeners
s(ll).spdtfs = extractsp(lat,s(ll).Obj);
% superposition
s(ll).dtfs2{ii} = g(1)*s(ll).spdtfs(:,id1,:) + g(2)*s(ll).spdtfs(:,id2,:);
[s(ll).p2{ii},rang] = baumgartner2014(...
s(ll).dtfs2{ii},s(ll).spdtfs,s(ll).fs,'S',s(ll).S,...
'mrsmsp',MRS,'polsamp',polang,'rangsamp',1); % phantom
% total polar range
m2 = baumgartner2014_virtualexp(s(ll).p2{ii},pol0,rang,'runs',runs);
% R2 via correlation coefficient
r = corrcoef(m2(:,8),m2(:,6));
r2total(ll,idl) = r(2);
% restricted to frontal polar range
idfront = rang <= 90;
respangfront = rang(idfront);
idpol0front = pol0 <= 90;
pol0front = pol0(idpol0front);
p = s(ll).p2{ii}(idfront,idpol0front);
m2front = baumgartner2014_virtualexp(p,pol0front,respangfront,'runs',runs);
r = corrcoef(m2front(:,8),m2front(:,6));
r2front(ll,idl) = r(2);
% restricted to rear polar range
idrear = rang >= 90;
respangrear = rang(idrear);
idpol0rear = pol0 >= 90;
pol0rear = pol0(idpol0rear);
p = s(ll).p2{ii}(idrear,idpol0rear);
m2rear = baumgartner2014_virtualexp(p,pol0rear,respangrear,'runs',runs);
r = corrcoef(m2rear(:,8),m2rear(:,6));
r2rear(ll,idl) = r(2);
end
end
r2.total(:,ii) = nanmean(r2total,2);
r2.front(:,ii) = nanmean(r2front,2);
r2.rear(:,ii) = nanmean(r2rear,2);
end
amt_cache('set','loudspeakerspan_r2',r2,dPol)
end
% data extracted from Fig.6 (data:AVG) of Bremen et al. (2010, J Neurosci,
% 30:194-204) via http://arohatgi.info/WebPlotDigitizer
bremen2010.pol = 15:15:105;
bremen2010.r2 = [.85 , .81 , .63 , .38 , .19 , .11 , .21];
if flags.do_plot
figure
h(1) = plot(bremen2010.pol,bremen2010.r2,'bo-');
hold on
h(2) = plot(dPol,mean(r2.front),'bo-');
h(3) = plot(dPol,mean(r2.rear),'rs-');
h(4) = plot(dPol,mean(r2.total),'kd-');
set(h(1),'MarkerSize',kv.MarkerSize,'MarkerFaceColor','b')
set(h(2:4),'MarkerSize',kv.MarkerSize,'MarkerFaceColor','w')
set(gca,'XLim',[-5,110],'YLim',[-.05,1.05])
leg = legend('Frontal from [18]','Frontal','Rear','Overall','Location','best');
set(leg,'FontSize',kv.FontSize)
xlabel({'Loudspeaker span (deg)';''})
ylabel('\it{r}^{ 2}')
end
end
%% ------ FIG 9 of baumgartner2015jaes ------------------------------------
if flags.do_fig9_baumgartner2015jaes
SysName{1} = 'NHK 22.2 (without bottom layer)';
LSPsetup{1} = [ 0,0 ; 30,0 ; 60,0 ; 90,0 ; 135,0 ; ...
180,0 ;-30,0 ;-60,0 ; -90,0 ;-135,0 ; ...
0,45; 45,45; 90,45; 135,45; 180,45; ...
-135,45;-90,45;-45,45; 0,90];
SysName{2} = 'Samsung 11.2';
LSPsetup{2} = [ 0,0 ; 60,0 ; 90,0 ; 135,0 ; ...
-60,0 ; -90,0 ;-135,0 ; ...
45,45;135,45; -45,45;-135,45];
SysName{3} = 'Samsung 10.2';
LSPsetup{3} = [ 0,0 ; 60,0 ; 90,0 ; 135,0 ; ...
-60,0 ; -90,0 ;-135,0 ; ...
45,45;180,45; -45,45];
SysName{4} = 'USC 10.2';
LSPsetup{4} = [ 0,0 ; 30,0 ; 60,0 ; 115,0 ; ...
180,0 ;-30,0 ;-60,0 ; -115,0 ; ...
45,45;-45,45];
SysName{5} = 'Auro-3D 10.1';
LSPsetup{5} = [ 30,0 ; 30,30 ; 135,30 ; 135,0;...
0,0 ;-30,0 ;-30,30 ;-135,30 ;-135,0; 0,90];
SysName{6} = 'Auro-3D 9.1';
LSPsetup{6} = [ 30,0 ; 30,30 ; 135,30 ; 135,0;...
0,0 ;-30,0 ;-30,30 ;-135,30 ;-135,0];
latall = -45:5:45;
polall = 0:10:180;
pe = amt_cache('get','locaVBAP',flags.cachemode);
if isempty(pe)
MRS = 0;
s = data_baumgartner2014('pool');
pe = zeros(length(latall),length(polall),length(s),length(LSPsetup),2); % predicted local polar RMS errors
for ll = 1:length(LSPsetup)
for aa = 1:length(latall)
lat = latall(aa);
for pp = 1:length(polall)
pol = polall(pp);
% Select LSP-Triangle and compute VBAP gains
[source_pos(1),source_pos(2)] = hor2sph(lat,pol);
Nlsp = length(LSPsetup{ll});
[g,IDsp] = vbap(LSPsetup{ll},source_pos);
for jj = 1:length(s)
% Compute binaural impulse response of loudspeaker triple
dtfs = permute(double(s(jj).Obj.Data.IR),[3 1 2]);
lsp_hrirs = zeros(length(IDsp),size(dtfs,1),2);
for ii = 1:length(IDsp)
% [lat_sp,pol_sp] = sph2horpolar(LSPsetup{ll}(IDsp(ii),1),LSPsetup{ll}(IDsp(ii),2));
idx = findNearestPos_locaVBAP(...
LSPsetup{ll}(IDsp(ii),1:2),s(jj).Obj.SourcePosition(:,1:2));
lsp_hrirs(ii,:,:) = squeeze(dtfs(:,idx,:));
end
target(:,1,1) = g*lsp_hrirs(:,:,1);
target(:,1,2) = g*lsp_hrirs(:,:,2);
% SP-template
[spdtfs,polang] = extractsp(lat,s(jj).Obj);
% Run loca model
[p,rang] = baumgartner2014(...
target,spdtfs,s(jj).fs,'S',s(jj).S,...
'lat',lat,'polsamp',polang,'mrsmsp',MRS);
m = baumgartner2014_virtualexp(p,pol,rang,'runs',1000);
pe(aa,pp,jj,ll,1) = localizationerror(m,'precPnoquerr');
[~,pe(aa,pp,jj,ll,2)] = baumgartner2014_pmv2ppp(p,pol,rang);
end
end
end
amt_disp([num2str(ll) ' of ' num2str(length(LSPsetup)) ' done'],'progress')
end
amt_cache('set','locaVBAP',pe)
end
MRS = 0;
pe_ref = amt_cache('get','locaVBAP_ref',flags.cachemode);
if isempty(pe_ref)
s = data_baumgartner2014('pool');
latall = -45:5:45;
polall = 0:10:180;
pe_ref = zeros(length(latall),length(polall),length(s),1,2); % predicted local polar RMS errors
%% Computations
for jj = 1:length(s)
dtfs = permute(double(s(jj).Obj.Data.IR),[3 1 2]);
for aa = 1:length(latall)
lat = latall(aa);
for pp = 1:length(polall)
pol = polall(pp);
[lat_sp,pol_sp,idx] = findNearestPos_locaVBAP_ref(lat,pol,s(jj).Obj.SourcePosition(:,1:2));
target = dtfs(:,idx,:);
% SP-template
[spdtfs,polang] = extractsp(lat,s(jj).Obj);
% Run loca model
[p,rang] = baumgartner2014(...
target,spdtfs,s(jj).fs,'S',s(jj).S,...
'mrsmsp',MRS,'lat',lat,'polsamp',polang);
m = baumgartner2014_virtualexp(p,pol,rang,'runs',1000);
pe_ref(aa,pp,jj,1,1) = localizationerror(m,'precPnoquerr');
[~,pe_ref(aa,pp,jj,1,2)] = baumgartner2014_pmv2ppp(p,pol,rang);
end
end
end
amt_cache('set','locaVBAP_ref',pe_ref)
end
N = length(LSPsetup);
eRMS = pe_ref(:,:,:,:,2);
eRMS(:,:,:,2:N+1) = pe(:,:,:,:,2);
if flags.do_plot
labels = {'Reference';'\it A';'\it B';'\it C';'\it D';'\it E';'\it F'};
labels = labels(1:N+1,:);
figure
for ll = 1:N+1
pemean = squeeze(mean(eRMS(:,:,:,ll),3));
subplot(1,N+2,ll)
imagesc(latall,polall,pemean')
set(gca,'YTick',0:30:180,'XTick',-30:30:30)
axis equal tight
colormap hot
if MRS == 0
ymin = 15.5;
ymax = 49.5;
else
ymin = 15.5;
ymax = 49.5;
end
caxis([ymin ymax])
if ll==1;
ylabel('Polar angle (deg)','FontName','Helvetica');
else
set(gca,'YTickLabel',[])
end
if ll==4; xlabel('Lateral angle (deg)','FontName','Helvetica'); end
title(labels{ll},'FontName','Helvetica')
% Loudspeaker positions
if ll > 1
[lat_lsp,pol_lsp] = sph2hor(LSPsetup{ll-1}(:,1),LSPsetup{ll-1}(:,2));
idlat = abs(lat_lsp) <= max(abs(latall))+1;
hold on
h2 = plot(lat_lsp(idlat),pol_lsp(idlat),'wo');
set(h2,'MarkerSize',2*3.5);
h1 = plot(lat_lsp(idlat),pol_lsp(idlat),'ko');
set(h1,'MarkerSize',2*3);
end
end
subplot(1,N+2,N+2)
y = ymin:1:ymax;
Ny = length(y);
pcolor(1:2,y,repmat(y(:),1,2))
shading flat
axis tight
colormap hot
title({' ';' '})
set(gca,'XTick',[],'YTick',20:5:45,... %,'TickLength',[0.12,0.12]
'YDir','normal','YAxisLocation','right','FontSize',kv.FontSize)
ylabel({'Polar error (deg)'},'FontSize',kv.FontSize)
set(gca,'Position',get(gca,'Position').*[1.03,1.8,0.3,0.8])
end
end
%% ------ Tab 1 of baumgartner2015jaes ------------------------------------
if flags.do_tab1_baumgartner2015jaes
results = amt_cache('get','replicatePulkki2001',flags.cachemode);
[panang_varStrat,nCM_varStrat,p_varStrat,muhat,sigmahat] = amt_cache('get','replicatePulkki2001_varStrat',flags.cachemode);
if isempty(panang_varStrat)
exp_baumgartner2014('fig6_baumgartner2015jaes','noplot',flags.cachemode)
end
pulkki01 = data_pulkki2001;
[h1_pul,p1_pul] = kstest((pulkki01(1,:)-muhat(1))/sigmahat(1)); % center data acc. to target distribution and then test similarity to standard normal distribution
[h2_pul,p2_pul] = kstest((pulkki01(2,:)-muhat(2))/sigmahat(2));
[h1_pm,p1_pm] = kstest((results.panang_Pmax(1,:)-muhat(1))/sigmahat(1)); % center data acc. to target distribution and then test similarity to standard normal distribution
[h2_pm,p2_pm] = kstest((results.panang_Pmax(2,:)-muhat(2))/sigmahat(2));
[h1_cm,p1_cm] = kstest((results.panang_Cen(1,:)-muhat(1))/sigmahat(1)); % center data acc. to target distribution and then test similarity to standard normal distribution
[h2_cm,p2_cm] = kstest((results.panang_Cen(2,:)-muhat(2))/sigmahat(2));
amt_disp('p-values of K.S.-test:')
amt_disp('Real source at 0 deg:')
amt_disp(['Results Pulkki (2001): p = ' num2str(p1_pul,'%3.2f')])
amt_disp(['Probability Maximiz.: p = ' num2str(p1_pm,'%3.2f')])
amt_disp(['Centroid Match: p = ' num2str(p1_cm,'%3.2f')])
amt_disp(['Individual strategy: p = ' num2str(p_varStrat(1),'%3.2f') ' (#CM = ' num2str(nCM_varStrat) ')'])
amt_disp('Real source at 15 deg:')
amt_disp(['Results Pulkki (2001): p = ' num2str(p2_pul,'%3.2f')])
amt_disp(['Probability Maximi.: p = ' num2str(p2_pm,'%3.2f')])
amt_disp(['Centroid Match: p = ' num2str(p2_cm,'%3.2f')])
amt_disp(['Individual strategy: p = ' num2str(p_varStrat(2),'%3.2f') ' (#CM = ' num2str(nCM_varStrat) ')'])
end
%% ------ Tab 3 of baumgartner2015jaes ------------------------------------
if flags.do_tab3_baumgartner2015jaes
pe = amt_cache('get','locaVBAP',flags.cachemode);
pe_ref = amt_cache('get','locaVBAP_ref',flags.cachemode);
if isempty(pe)
exp_baumgartner2014('fig9_baumgartner2015jaes','noplot',flags.cachemode);
end
N = size(pe,4); % # loudspeakers
eRMS = pe_ref(:,:,:,:,2);
eRMS(:,:,:,2:N+1) = pe(:,:,:,:,2);
labels = {'Reference';'\it A';'\it B';'\it C';'\it D';'\it E';'\it F'};
labels = labels(1:N+1,:);
amt_disp('RMS error difference from reference averaged across directions')
amt_disp('System min mean max')
for ll = 2:N+1
IeRMS = eRMS(:,:,:,ll) - eRMS(:,:,:,1);
IeRMS = mean(IeRMS,3); % average across listeners
amt_disp([labels{ll} ' ' num2str(min(IeRMS(:)),'%2.1f') ' ' num2str(mean(IeRMS(:)),'%2.1f') ' ' num2str(max(IeRMS(:)),'%2.1f')])
end
end
end
%% ------------------------------------------------------------------------
% ---- INTERNAL FUNCTIONS ------------------------------------------------
% ------------------------------------------------------------------------
function hM_warped = warp_hrtf(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 = FW_fade(hwin,512,24,96,192);
hM_warped(1:end,el,canal)=hwinfade;
end
end
end
function [syncrnfreq, GETtrain] = 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=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));
% amt_disp(20*log10(sqrt(sum(out.^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=FW_fade(out,0,fadein,fadeout);
% wavwrite(out,srate,stimpar.Resolution,fwavout);
end
function out = FW_fade(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 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
function [idx,posN] = findNearestPos_locaVBAP(posdesired,posexist)
% FINDNEARESTPOS_LOCAVABAP finds nearest position. Data given in spherical
% coordinates
%
% Usage: [idx,posN] = findNearestPos(posdesired,posexist)
ds = deg2rad(posdesired);
es = deg2rad(posexist);
[d(:,1),d(:,2),d(:,3)] = sph2cart(ds(:,1),ds(:,2),ones(size(ds,1),1));
[e(:,1),e(:,2),e(:,3)] = sph2cart(es(:,1),es(:,2),ones(size(es,1),1));
D = e-repmat(d,length(es),1);
[Dmin,idx] = min(sum(D.^2,2));
posN = posexist(idx,:);
end
function [latN,polN,idx] = findNearestPos_locaVBAP_ref(lat,pol,aziele)
% FINDNEARESTPOS_LOCAVBAP_REF finds nearest position according to lat. and pol. angle
%
% Usage: [latN,polN,idx] = findNearestPos(lat,pol,aziele)
[positions(:,1),positions(:,2)] = sph2hor(aziele(:,1),aziele(:,2));
d_lat = abs(lat-positions(:,1));
d_pol = abs(pol-positions(:,2));
d = d_lat+d_pol;
[d_min,idx] = min(d);
latN = positions(idx,1);
polN = positions(idx,2);
end
function [g,IDspeaker] = vbap(lsp_coord,source_pos)
%VBAP Returns array of gains for VBAP triplet of speakers.
% Usage: [g,IDspeaker] = vbap(speaker_coord,source_pos)
%
% Input Parameters:
% lsp_coord : spherical (azi,ele) coordinates of loudspeakers
% source_pos : spherical (azi,ele) coordinates of phantom source
%
% Output Parameters:
% g : VBAP gains of loudspeaker triplet
% IDspeaker : indices of selected loudspeaker triplet
Nlsp = size(lsp_coord,1);
% Convert to spherical coordinates winth angles in radians
speaker_sph = deg2rad(lsp_coord);
source_sph = deg2rad(source_pos);
% Convert to cartesian coordinates
[speaker_cart(:,1),speaker_cart(:,2),speaker_cart(:,3)] = sph2cart(...
speaker_sph(:,1),speaker_sph(:,2),ones(Nlsp,1));
[source_cart(1),source_cart(2),source_cart(3)] = sph2cart(...
source_sph(1),source_sph(2),1);
% Add imaginary speaker below
if min(lsp_coord(:,2)) >= 0
speaker_cart = [speaker_cart;0,0,-10];
end
% Select lsp. triplet
dt = DelaunayTri(speaker_cart);
ch = convexHull(dt);
% figure; trisurf(ch, dt.X(:,1),dt.X(:,2),dt.X(:,3), 'FaceColor', 'cyan')
d = zeros(length(ch),1);
for ii = 1:length(ch)
d(ii) = sum(dist(source_cart,speaker_cart(ch(ii,:),:)));
end
[~,IDch] = min(d);
IDspeaker = ch(IDch,:);
% Compute lsp. gains
g = source_cart / speaker_cart(IDspeaker,:);
g = g / norm(g);
end
function d = dist(x,Y)
d = sqrt(sum((repmat(x,size(Y,1),1)-Y).^2,2));
end
Build with Bootstrap