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EXP_BAUMGARTNER2016 - evaluation of baumgartner2016 model
Program code:
function varargout = exp_baumgartner2016(varargin)
%EXP_BAUMGARTNER2016 evaluation of baumgartner2016 model
% Usage: data = exp_baumgartner2016(flag)
%
% EXP_BAUMGARTNER2016(flag) reproduces figures of the study from
% Baumgartner et al. (2016).
%
% The following flags can be specified
%
% 'plot' Plot the output of the experiment. This is the default.
%
% 'no_plot' Don't plot, only return data.
%
% 'fig2' or 'ratelevelcurves'
% Rate-level curves of the three different fiber types
% represented in the auditory-periphery model. Firing rates were
% evaluated at a CF of 4 kHz in response to Gaussian white noise at
% various SPLs. Note that high-SR fibers saturate already at low SPLs,
% medium-SR fibers at moderate SPLs, and low-SR fibers not at all.
%
% 'fig3' or 'baseline'
% Correspondence between actual and predicted
% baseline performance for the 23 normal-hearing listeners after
% listener-specific calibration of the models sensitivity parameter (S).
%
% 'fig4' or 'tab1' or 'hearingthreshold'
% Hearing thresholds estimated for simulated OHC gains
% (COHC) within the range of 1 (normal active cochlea) to 0 (passive
% cochlea). The selected set of OHC gains results in approximately
% equal increments of high-frequency thresholds.
% Table I: Simulated Conditions of OHC Dysfunction,
% Estimated PTAs, and Corresponding Hearing Loss Categories.
%
% 'fig5' or 'numchan' and 'spatstrat'
% Model evaluation for normal-hearing listeners tested
% on the effects of spectral resolution (by number of vocoder
% channels in Goupell et al., 2010) and spectral warping (Majdak,
% Walder, et al., 2013). Model data (filled circles) are compared
% with actual data (open circles) from the two studies. Error bars
% represent SDs. Symbols are slightly shifted along the abscissa for
% better visibility. BB = broadband noise burst; CL = broadband click
% train (infinite number of channels); LP = low-pass filtered at
% 8.5 kHz; W = HRTFs spectrally warped from 2.8 to 16 kHz to
% 2.8 to 8.5 kHz.
%
% 'fig6' or 'evalSPLtem'
% Effect of template SPL on predictive power of the
% model for the two studies (Goupell et al., 2010; Majdak, Walder,
% et al., 2013) shown in Figure 5. Predictions based on a single
% template SPL equivalent to the actual SPL of the target sounds of
% 60 dB result in similar prediction residues as based on templates
% mixed across a broad range of SPLs. Higher plausibility of the
% mixed-SPL templates was the reason to choose this representation
% for all further simulations (including predictions shown in Figure 5).
%
% 'fig7' or 'impairment'
% Effects of OHC dysfunctions and selective activity of
% AN fibers on predicted quadrant error rates (top) and local RMS
% errors (bottom). Thick bar: interquartile range (IQR). Thin
% bar: data range within 1.5 IQR. Horizontal line within thick
% bar: average. Dashed horizontal line: chance performance.
%
% 'fig8' or 'sensitivity'
% Sensitivity (dprime) of AN fibers in level discrimination
% as function of SPL predicted for different fiber types and OHC
% dysfunctions. Sensitivities were evaluated for SPL increments of
% 10 dB and averaged across 28 CFs from 0.7 to 18 kHz. Gray area:
% stimulus range of target sounds at 60 dB SPL.
%
% 'fig9' or 'effectOnCues'
% Effect of OHC dysfunction on positive spectral gradients.
% Exemplary median-plane HRTFs from one listener (NH46). Note the
% distinct direction-specific patterns for the normal and moderate
% OHC dysfunctions (COHC>=0.4), which are almost absent in the
% cases of the severe and complete OHC dysfunctions (COHC<=0.1).
%
%
% 'baseline_ex' 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.
%
%
% 'spatstrat_ex' 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.
%
% 'numchan_ex' 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.
%
% 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 the rate-level curves use :
%
% exp_baumgartner2016('fig2');
%
% To display the baseline prediction use :
%
% exp_baumgartner2016('fig3');
%
% To display estimations of hearing thresholds use :
%
% exp_baumgartner2016('fig4');
%
% To display model evaluation for normal-hearing listeners use :
%
% exp_baumgartner2016('fig5');
%
% To display evaluation results for template SPL use :
%
% exp_baumgartner2016('fig6');
%
% To display predicted effects of sensorineural hearing loss use (requires Matlab 2013b or higher) :
%
% exp_baumgartner2016('fig7');
%
% To display sensitivity evaluation for different fiber types use :
%
% exp_baumgartner2016('fig8');
%
% To display effect of OHC damage on exemplary spectral cue representation use :
%
% exp_baumgartner2016('fig9');
%
% See also: baumgartner2016 data_baumgartner2016
%
% References:
% R. Baumgartner, P. Majdak, and B. Laback. Modeling the effects of
% sensorineural hearing loss on auditory localization in the median
% plane. Trends in Hearing, 20:1--11, 2016.
%
% P. Majdak, T. Walder, and B. Laback. Effect of long-term training on
% sound localization performance with spectrally warped and band-limited
% head-related transfer functions. J. Acoust. Soc. Am., 134:2148--2159,
% 2013.
%
% M. J. Goupell, P. Majdak, and B. Laback. Median-plane sound
% localization as a function of the number of spectral channels using a
% channel vocoder. J. Acoust. Soc. Am., 127:990--1001, 2010.
%
%
% Url: http://amtoolbox.org/amt-1.5.0/doc/experiments/exp_baumgartner2016.php
% #Author Robert Baumgartner (2016)
% This file is licensed unter the GNU General Public License (GPL) either
% version 3 of the license, or any later version as published by the Free Software
% Foundation. Details of the GPLv3 can be found in the AMT directory "licences" and
% at <https://www.gnu.org/licenses/gpl-3.0.html>.
% You can redistribute this file and/or modify it under the terms of the GPLv3.
% This file is distributed without any warranty; without even the implied warranty
% of merchantability or fitness for a particular purpose.
%% ------ Check input options --------------------------------------------
definput.import={'amt_cache','localizationerror','baumgartner2014_pmv2ppp'};
definput.flags.experiment = {'missingflag',...
'fig2','fig3','fig4','fig5','fig6','fig7','fig8','fig9','tab1',...
'sensitivity','baseline','baseline_ex',...
'baseline_lat','parametrization',...
'spatstrat_ex','spatstrat','numchan_ex','numchan',...
'evalSPLtem','cOHCvsSens',...
'sabin2005','impairment','effectOnCues','dynrangecheck',...
'localevel','hearingthreshold','ratelevelcurves','evalSpectralContrast'};
definput.keyvals.ModelSettings = {};
% Figure Settings
definput.flags.plot = {'plot','no_plot'};
definput.keyvals.FontSize = 10;
definput.keyvals.MarkerSize = 6;
definput.keyvals.gap = 0;
definput.keyvals.marg_h = [.08,.05];
definput.keyvals.marg_w = .05;
definput.keyvals.TickLength = [0.02,0.04];
% For: sabin2005
definput.keyvals.SL2SPL = 10;
definput.flags.gainInDeg = {'','gainInDeg'};
definput.keyvals.sabin2005_Sdivisor = 1;
definput.flags.sabin2005nomrs = {'','nomrs'};
% For: impairment
definput.keyvals.subjects = [];
definput.keyvals.SPLset = 60;
definput.keyvals.FTset = {1:3,1,2,3};
definput.keyvals.cOHCset = [1,0.4,0.1,0];
definput.flags.impairment={'FTlabel','noFTlabel'};
definput.flags.SPLseparation={'splitSPL','compriseSPL'};
% For: localevel
definput.flags.localevelplot = {'performance','pmv'};
% For: parametrization
definput.flags.parametrize = {'gamma','mrsandgamma'};
% For: dynrangecheck
definput.flags.dynrangecheck = {'ratelevel','dynrangeDiff','dprime'};
definput.flags.dynrangecheck_comb = {'separate','combined'};
definput.flags.effectOnCues={'OHC','FT'};
% General: availability of Statistics Toolbox
definput.flags.statistics = {'stat','nostat'};
[flags,kv] = ltfatarghelper({},definput,varargin);
model.definput.import={'baumgartner2016'};
[model.flags,model.kv] = ltfatarghelper({},model.definput,kv.ModelSettings);
errorflag = [flags.errorflag,flags.ppp];
if flags.do_missingflag
flagnames=[sprintf('%s, ',definput.flags.experiment{2:end-2}),...
sprintf('%s or %s',definput.flags.experiment{end-1},definput.flags.experiment{end})];
error('%s: You must specify one of the following flags: %s.',upper(mfilename),flagnames);
end;
%% Define cache name according to settings for auditory periphery model
cachename = ['g' num2str(model.kv.gamma,'%u') ...
'_mrs' num2str(model.kv.mrsmsp,'%u') ...
'_do' num2str(model.kv.do,'%u') ...
'_tem' num2str(model.kv.SPLtem,'%u') 'dB_' model.flags.fbank];
if model.flags.do_gammatone
cachename = [cachename '_' num2str(1/model.kv.space,'%u') 'bpERB'];
if model.flags.do_middleear; cachename = [cachename '_middleear']; end
if model.flags.do_ihc; cachename = [cachename '_ihc']; end
else % zilany
cachename = [cachename '_' model.flags.fibertypeseparation];
end
if model.kv.prior > 0
cachename = [cachename '_prior' num2str(model.kv.prior,'%u')];
end
if model.kv.tiwin < 0.5
cachename = [cachename '_tiwin' num2str(model.kv.tiwin*1e3) 'ms'];
end
cachename = [cachename '_mgs' num2str(model.kv.mgs)];
%% Hearing thresholds following OHC dysfunction
if flags.do_hearingthreshold || flags.do_fig4
cOHC = kv.cOHCset;
flow = 125;%700; % Hz
fhigh = 18000; % Hz
spl = -20:100; % dB
Ncf = 40; % # CF
fs = 100e3; % Hz
t = 0:1/fs:0.1; % s
cf = audspace(flow,fhigh,Ncf); % CFs under test
cachename = [model.flags.fbank '_' model.flags.fibertypeseparation];
cachename = [cachename '_cohc' num2str(cOHC)];
cachename = strrep(cachename,' ','');cachename = strrep(cachename,'.','p');
cachename = ['hearingthreshold_' cachename];
afr = amt_cache('get',cachename,flags.cachemode);
if isempty(afr)
afr = nan(length(cf),length(cOHC),length(spl),3);
for ff = 1:length(cf)
sig = sin(2*pi*cf(ff)*t);
for iiOHC = 1:length(cOHC)
for iispl = 1:length(spl)
for ft = 1:3
ANoutTem = zilany2014(scaletodbspl(sig,spl(iispl)),fs,...
cf(ff),'fiberType',ft,'cohc',cOHC(iiOHC),'cihc',1);
afr(ff,iiOHC,iispl,ft) = mean(ANoutTem,1);
end
end
end
amt_disp([num2str(ff) ' of ' num2str(length(cf)) ' done.']);
end
amt_cache('set',cachename,afr)
end
ftd = [0.16,0.23,0.61]; % fiber type distribution from Liberman (1978)
%% Evaluate avg. firing rate at normal hearing threshold (afrLMHc1)
sizeafr = size(afr);
afrLMH = sum(afr.*repmat(shiftdim(ftd,-2),[sizeafr(1:3),1]),4);
afrLMHc1 = nan(length(cf),1);
HT0 = nan(length(cf),1);
for ff = 1:length(cf)
[tmp,id] = min(abs(spl-absolutethreshold(cf(ff),'hda200')));
HT0(ff) = spl(id);
afrLMHc1(ff) = afrLMH(ff,1,id);
end
%%
% ftd(1:2) = 0;
afr = sum(afr.*repmat(shiftdim(ftd,-2),[sizeafr(1:3),1]),4);
HT = nan(length(cf),length(cOHC));
afr0 = nan(length(cf),1);
for ff = 1:length(cf)
for iiOHC = 1:length(cOHC)
id = find(afr(ff,iiOHC,:)>=afrLMHc1(ff),1,'first');
if isempty(id)
id = length(spl);
end
HT(ff,iiOHC) = spl(id);
end
end
% Pure-tone average HL
PTAf{1} = [500,1000,2000]; % Rakerd et al. (1998)
PTAf{2} = [3150,5000,8000]; % Rakerd et al. (1998)
PTAf{3} = [4000,8000,11000]; % Otte et al. (2013)
for cc = 1:length(cOHC)
for ii = 1:length(PTAf)
for ff = 1:length(PTAf{ii})
HL(ff,cc,ii) = interp1(cf,HT(:,cc),PTAf{ii}(ff));
end
end
end
HL = HL(:,2:end,:) - repmat(HL(:,1,:),[1,length(cOHC)-1,1]);
PTA = shiftdim(mean(HL));
legendentries = cat(2,repmat('C_{OHC} = ',length(cOHC),1),num2str(cOHC(:),'%2.1f'));
for cc = 1:length(cOHC)-1
RowNames{cc} = legendentries(cc+1,:);
end
PTA = round(PTA);
if verLessThan('matlab','8.2'),
disp('PTAlow_Rakerd98 PTAhigh_Rakerd98 PTAhigh_Otte13');
PTA
else
table(PTA(:,1),PTA(:,2),PTA(:,3),'RowNames',RowNames,'VariableNames',{'PTAlow_Rakerd98','PTAhigh_Rakerd98','PTAhigh_Otte13'})
end
varargout{1} = HT;
varargout{2} = cf;
if flags.do_plot
figure
symb = {'-k*','-kd','-k>','-kp'};
for ii = 1:size(HT,2)
h(ii) = semilogx(cf,HT(:,ii),symb{ii});
hold on
end
set(h,'MarkerFaceColor','w','MarkerSize',kv.MarkerSize)
set(gca,'XLim',[cf(1),cf(end)],'YDir','reverse','FontSize',kv.FontSize,'TickLength',kv.TickLength)
% set(gca,'XTickLabel',{'0.2','0.3','','0.5','','0.7','','','1','2','3','','5','','7','','','10'})
set(gca,'Layer', 'top')
xlabel('Frequency (Hz)','FontSize',kv.FontSize)
ylabel('Hearing threshold (dB SPL)','FontSize',kv.FontSize)
leg = legend(legendentries,'Location','southwest');
set(leg,'FontSize',kv.FontSize)
end
end
%% ------ BASELINE EXAMPLES --------------------------------------------------------
if flags.do_baseline_ex
SL = 50; % presentation level of stimuli
model.kv.SPL = SL + kv.SL2SPL;
latseg = 0;ii=1;%[-20,0,20]; ii = 2; % centers of lateral segments
% dlat = 10; % lateral range (+-) of each segment
s = data_baumgartner2016('argimport',model.flags,model.kv);
% idselect = ismember({s.id},{'NH15','NH22','NH62','NH12','NH39','NH18'});
idselect = 19:23;
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] = baumgartner2016(...
s(ll).sphrtfs{ii},s(ll).sphrtfs{ii},'argimport',model.flags,model.kv,...
'ID',s(ll).id,'fs',s(ll).fs,...
'mrsmsp',s(ll).mrs,'S',s(ll).S,'lat',latseg(ii),'polsamp',polang,...
'priordist',s(ll).priordist);
[ 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(2,3,ll)
Nmax = min(150,s(ll).Ntar{ii});
idplot = round(1:s(ll).Ntar{ii}/Nmax:s(ll).Ntar{ii});
plot_baumgartner2014(s(ll).p{ii},polang,respangs,...
s(ll).target{ii}(idplot),s(ll).response{ii}(idplot),...
'MarkerSize',kv.MarkerSize,'cmax',0.05,'no_colorbar');
title({['A: PE = ' num2str(s(ll).pe_exp_lat(ii),2) '\circ, QE = ' num2str(s(ll).qe_exp_lat(ii),2) '%'];['P: PE = ' num2str(pe(ll,ii),2) '\circ, QE = ' num2str(qe(ll,ii),2) '%']},...
'FontSize',kv.FontSize-1)
text(90,240,s(ll).id,'FontSize',kv.FontSize,...
'Color','w','HorizontalAlignment','center')
xlabel('Target Angle (deg)','FontSize',kv.FontSize)
ylabel('Response Angle (deg)','FontSize',kv.FontSize)
set(gca,'FontSize',kv.FontSize-1)
set(gca,'XTickLabel',{[];[];0;[];60;[];120;[];180;[];[]})
set(gca,'YTickLabel',{-60;[];0;[];60;[];120;[];180;[];240})
end
end
s = rmfield(s,{'Obj','itemlist','sphrtfs'}); % reduce file size
varargout{1} = s;
end
%% ------ PARAMETRIZATION -----------------------------------------------------------
if flags.do_parametrization
if flags.do_mrsandgamma
[gamma,mrs] = amt_cache('get','parametrization', flags.cachemode);
if isempty(gamma)
amt_disp('Note that this procedure lasts at least 2 hours!');
tempfn = fullfile(amt_basepath,'experiments','exp_baumgartner2014_parametrization'); % temporary folder
mkdir(tempfn)
s = data_baumgartner2016('argimport',model.flags,model.kv);
[gamma,mrs] = baumgartner2016_parametrization(s,'SPLtem',kv.SPLtem,...
flags.fbank,flags.fibertypeseparation,'mgs',kv.mgs);
amt_cache('set','parametrization',gamma,mrs);
end
varargout{1} = {gamma,mrs};
else
gamma = 7:2:30;%[3:2:7,8:13,15:2:30];
dtot = nan(size(gamma));
for g = 1:length(gamma)
d = exp_baumgartner2016('argimport',model.flags,model.kv,'baseline','gamma',gamma(g));
dtot(g) = d.total;
end
[d_opt,id_opt] = min(dtot);
gamma_opt = gamma(id_opt);
varargout{1} = gamma_opt;
if flags.do_plot
figure;
plot(gamma,dtot)
xlabel('Gamma')
ylabel('Prediction deviation')
end
end
end
%% ------ BASELINE ----------------------------------------------------------
if flags.do_baseline || flags.do_fig3
SL = 50; % presentation level of stimuli
model.kv.SPL = SL + kv.SL2SPL;
cachename = ['baseline_tar' num2str(model.kv.SPL,'%u') 'dB_' cachename];
if not(isempty(model.flags.errorflag))
cachename = [cachename '_' model.flags.errorflag];
end
[Pcorr,d,s] = amt_cache('get',cachename,flags.cachemode);
if isempty(Pcorr)
s = data_baumgartner2016('argimport',model.flags,model.kv);
% # of targets for evaluation of prediction residues
Ntargets = cat(1,s.Ntar); % # of targets
Ntargets = cat(1,Ntargets{:});
relfreq = Ntargets/sum(Ntargets(:));
if isempty(model.flags.errorflag)
errorflag = 'querrMiddlebrooks';
else
errorflag = model.flags.errorflag;
end
for ii = 1:length(s)
[s(ii).err,pred,m] = baumgartner2016(s(ii).Obj,s(ii).Obj,...
'argimport',model.flags,model.kv,'ID',s(ii).id,'Condition','baseline',...
'mrsmsp',s(ii).mrs,'S',s(ii).S,'priordist',s(ii).priordist,errorflag);
[s(ii).qe_pred,s(ii).pe_pred] = baumgartner2014_pmv2ppp(pred,'exptang',s(ii).itemlist(:,6));
end
if isempty(model.flags.errorflag) % QE and PE
qe_exp = cat(1,s.qe_exp);
pe_exp = cat(1,s.pe_exp);
qe_pred = cat(1,s.qe_pred);
pe_pred = cat(1,s.pe_pred);
% correlation
[Pcorr.qe.r,Pcorr.qe.p] = local_corrcoeff(qe_exp,qe_pred);
[Pcorr.pe.r,Pcorr.pe.p] = local_corrcoeff(pe_exp,pe_pred);
% prediction residues
sd_pe = (pe_pred-pe_exp).^2; % squared differences
d.pe = sqrt(relfreq(:)' * sd_pe(:)); % weighted RMS diff.
sd_qe = (qe_pred-qe_exp).^2;
d.qe = sqrt(relfreq(:)' * sd_qe(:));
else % arbitrary localization measure
for ii = 1:length(s)
s(ii).err_exp = localizationerror(s(ii).itemlist,model.flags.errorflag);
end
err_exp = cat(1,s.err_exp);
err_pred = cat(1,s.err);
[Pcorr.r,Pcorr.p] = local_corrcoeff(err_exp,err_pred,2);
sd = (err_pred-err_exp).^2; % squared differences
d = sqrt(relfreq(:)' * sd(:)); % weighted RMS diff.
end
s = rmfield(s,{'Obj'});
amt_cache('set',cachename,Pcorr,d,s)
end
if isempty(model.flags.errorflag)
d.total = (d.pe/90 + d.qe/100) /2;
amt_disp(['Corr. QE: ' num2str(Pcorr.qe.r,'%2.2f') ' (p = ',num2str(Pcorr.qe.p,'%0.3f'),'), PE: ' num2str(Pcorr.pe.r,'%2.2f') ' (p = ',num2str(Pcorr.pe.p,'%0.3f'),'), QE+PE: ' num2str((Pcorr.qe.r+Pcorr.pe.r)/2,'%2.2f')],'documentation');
end
if nargout >0; varargout{1} = d; end
if nargout >1; varargout{2} = r; end
if nargout >2; varargout{3} = s; end
if flags.do_plot
if isempty(model.flags.errorflag)
qe_exp = cat(1,s.qe_exp);
pe_exp = cat(1,s.pe_exp);
qe_pred = cat(1,s.qe_pred);
pe_pred = cat(1,s.pe_pred);
figure
subplot(122)
minqe = min([qe_exp(:);qe_pred(:)])-2;
maxqe = max([qe_exp(:);qe_pred(:)])+2;
limqe = [minqe,maxqe];
plot(limqe,limqe,'k--')
hold on
h(1) = plot(qe_exp,qe_pred,'kd');
axis equal
axis([minqe maxqe minqe maxqe])
xlabel('Actual QE','FontSize',kv.FontSize)
ylabel('Predicted QE','FontSize',kv.FontSize)
title(['e_{QE} = ' num2str(d.qe,'%0.1f') '% , r_{QE} = ' num2str(Pcorr.qe.r,'%0.2f')],...
'FontSize',kv.FontSize)
subplot(121)
minpe = min([pe_exp(:);pe_pred(:)])-2;
maxpe = max([pe_exp(:);pe_pred(:)])+2;
limpe = [minpe,maxpe];
plot(limpe,limpe,'k--')
hold on
h(2) = plot(pe_exp,pe_pred,'ks');
set(h,'MarkerFaceColor','w')
axis equal
axis([minpe maxpe minpe maxpe])
xlabel('Actual PE','FontSize',kv.FontSize)
ylabel('Predicted PE','FontSize',kv.FontSize)
title(['e_{PE} = ' num2str(d.pe,'%0.1f') '\circ , r_{PE} = ' num2str(Pcorr.pe.r,'%0.2f')],...
'FontSize',kv.FontSize)
else
figure
plot(cat(1,s.err_exp),cat(1,s.err),'ko')
axis equal square
xlim = get(gca,'XLim');
hold on
plot(xlim,xlim,'k:')
xlabel('Actual','FontSize',kv.FontSize)
ylabel('Predicted','FontSize',kv.FontSize)
title({model.flags.errorflag;...
['(e = ' num2str(d,'%0.1f') ' , r = ' num2str(r,'%0.2f') ')']},...
'FontSize',kv.FontSize)
end
end
end
%% ------ SPATSTRAT EXAMPLES -----------------------------------------------------------
if flags.do_spatstrat_ex
SL = 50; % presentation level of stimuli
model.kv.SPL = SL + kv.SL2SPL;
latdivision = 0; % lateral angle
dlat = 15;
% Experimental Settings
Conditions = {'BB','LP','W'};
%% Computations
s = data_baumgartner2016('argimport',model.flags,model.kv);
s = s(ismember({s.id},'NH58'));
amt_disp(['Listener: ' s.id],'documentation');
chance = [];
for C = 1:length(Conditions)
Cond = Conditions{C};
%% Data
% Experimental data
data = data_majdak2013(Cond);
for ll = 1:length(s)
if sum(ismember({data.id},s(ll).id)) % participant ?
s(ll).itemlist=data(ismember({data.id},s(ll).id)).mtx;
for ii = 1:length(latdivision)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latdivision(ii)+dlat & latresp > latdivision(ii)-dlat;
mm2 = s(ll).itemlist(idlat,:);
chance = [chance;mm2];
s(ll).target{ii} = mm2(:,6); % polar angle of target
s(ll).response{ii} = mm2(:,8); % polar angle of response
end
end
end
for ll = 1:length(s)
for ii = 1:length(latdivision)
s(ll).spdtfs{ii} = 0; % init
s(ll).polang{ii} = 0; % init
[s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(...
latdivision(ii),s(ll).Obj);
if C == 1 % Learn
s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
elseif C == 2 % Dummy
temp=amt_load('baumgartner2014','spatstrat_lpfilter.mat');
s(ll).spdtfs_c{ii} = filter(temp.blp,temp.alp,s(ll).spdtfs{ii});
elseif C == 3 % Warped
s(ll).spdtfs_c{ii} = local_warphrtf(s(ll).spdtfs{ii},s(ll).fs);
end
end
end
%% Run Model
for ll = 1:length(s)
qe = zeros(1,length(latdivision));
pe = zeros(1,length(latdivision));
qe_t = zeros(1,length(latdivision));
pe_t = zeros(1,length(latdivision));
for ii = 1:length(latdivision)
switch Cond
case 'BB'
clabel = 'baseline';
case 'LP'
clabel = 'lowpassed';
case 'W'
clabel = 'warped';
end
[s(ll).p{ii},rang] = baumgartner2016(...
s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},...
'argimport',model.flags,model.kv,...
'ID',s(ll).id,'Condition',clabel,'fs',s(ll).fs,...
'mrsmsp',s(ll).mrs,'S',s(ll).S,'lat',latdivision(ii),...
'polsamp',s(ll).polang{ii},'priordist',s(ll).priordist);
respangs{ii} = rang;
[ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp(s(ll).p{ii} , s(ll).polang{ii} , rang);
if sum(ismember({data.id},s(ll).id)) % if participant then actual targets
[ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , s(ll).polang{ii} , rang , s(ll).target{ii} );
end
end
% Model results of pool
s(ll).qe_pool(C,1) = mean(qe);
s(ll).pe_pool(C,1) = mean(pe);
if sum(ismember({data.id},s(ll).id)) % participant ?
% Actual experimental results
s(ll).qe_exp(C,1) = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
s(ll).pe_exp(C,1) = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
s(ll).Nt(C,1) = size(s(ll).itemlist,1);
% Model results of participants (actual target angles)
if length(latdivision) == 3
s(ll).qe_part(C,1) = (qe_t(1)*length(s(ll).target{1}) + ...
qe_t(2)*length(s(ll).target{2}) + ...
qe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
s(ll).pe_part(C,1) = (pe_t(1)*length(s(ll).target{1}) + ...
pe_t(2)*length(s(ll).target{2}) + ...
pe_t(3)*length(s(ll).target{3}))/...
(length(s(ll).target{1})+length(s(ll).target{2})+length(s(ll).target{3}));
else
s(ll).qe_part(C,1) = mean(qe_t);
s(ll).pe_part(C,1) = mean(pe_t);
end
if flags.do_plot
if C == 1; figure; end
subplot(1,3,C)
ii = find(latdivision==0);
responses = [];
targets = [];
for jj = ii
responses = [responses;s(ll).response{jj}];
targets = [targets;s(ll).target{jj}];
end
plot_baumgartner2014(s(ll).p{ii},s(ll).polang{ii},rang,...
targets,responses,'MarkerSize',kv.MarkerSize,'cmax',0.05,'no_colorbar')
text(90,240,Cond,...
'FontSize',kv.FontSize,'Color','w','HorizontalAlignment','center')
Nt = length(targets);
tmp.m = [zeros(Nt,5) targets(:) zeros(Nt,1) responses(:)];
tmp.qe = localizationerror(tmp.m,'querrMiddlebrooks');
tmp.pe = localizationerror(tmp.m,'rmsPmedianlocal');
title({['A: PE = ' num2str(tmp.pe,2) '\circ, QE = ' num2str(tmp.qe,2) '%'];...
['P: PE = ' num2str(pe_t(ii),2) '\circ, QE = ' num2str(qe_t(ii),2) '%']},'FontSize',kv.FontSize-1)
xlabel('Target Angle (deg)','FontSize',kv.FontSize)
ylabel('Response Angle (deg)','FontSize',kv.FontSize)
set(gca,'FontSize',kv.FontSize-1)
set(gca,'XTickLabel',{[];[];0;[];60;[];120;[];180;[];[]})
set(gca,'YTickLabel',{-60;[];0;[];60;[];120;[];180;[];240})
end
end
end
end
varargout{1} = s;
end
%% ------ SPATSTRAT -----------------------------------------------------------
if flags.do_spatstrat
SL = 50; % presentation level of stimuli
model.kv.SPL = SL + kv.SL2SPL;
cachename = ['spatstrat_tar' num2str(model.kv.SPL,'%u') 'dB_' cachename];
[r,d,s,act,pred] = amt_cache('get',cachename,flags.cachemode);
if isempty(r)
latdivision = 0;%[-20,0,20]; % lateral angle
dlat = 30;%10;
% Experimental Settings
Conditions = {'BB','LP','W'};
%% Computations
s = data_baumgartner2016('argimport',model.flags,model.kv);
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,:);
s(ll).target{ii} = mm2(:,6); % polar angle of target
s(ll).response{ii} = mm2(:,8); % polar angle of response
end
end
end
for ll = 1:length(s)
for ii = 1:length(latdivision)
s(ll).spdtfs{ii} = 0; % init
s(ll).polang{ii} = 0; % init
[s(ll).spdtfs{ii},s(ll).polang{ii}] = extractsp(...
latdivision(ii),s(ll).Obj);
if C == 1 % Learn
s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
elseif C == 2 % Dummy
temp=amt_load('baumgartner2014','spatstrat_lpfilter.mat');
s(ll).spdtfs_c{ii} = filter(temp.blp,temp.alp,s(ll).spdtfs{ii});
elseif C == 3 % Warped
s(ll).spdtfs_c{ii} = local_warphrtf(s(ll).spdtfs{ii},s(ll).fs);
end
end
end
%% Run Model
idpart = [];
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)
switch Cond
case 'BB'
clabel = 'baseline';
case 'LP'
clabel = 'lowpassed';
case 'W'
clabel = 'warped';
end
if sum(ismember({data.id},s(ll).id)) % if actual participant actual targets
idpart = [idpart,ll];
[s(ll).p{ii},rang] = baumgartner2016(...
s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},'argimport',model.flags,model.kv,...
'ID',s(ll).id,'Condition',clabel,'fs',s(ll).fs,...
'mrsmsp',s(ll).mrs,'S',s(ll).S,'lat',latdivision(ii),...
'polsamp',s(ll).polang{ii},'priordist',s(ll).priordist);
[ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , s(ll).polang{ii} , rang , s(ll).target{ii} );
end
end
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
end
end
end
s = rmfield(s,{'Obj','spdtfs_c','spdtfs'});% reduce file size
act.qe = [s(idpart).qe_exp]';
act.pe = [s(idpart).pe_exp]';
pred.qe = [s(idpart).qe_part]';
pred.pe = [s(idpart).pe_part]';
% Correlation coefficients
r.qe = local_corrcoeff(act.qe,pred.qe);
disp(['QE: r = ' num2str(r.qe,'%0.2f')]);
r.pe = local_corrcoeff(act.pe,pred.pe);
disp(['PE: r = ' num2str(r.pe,'%0.2f')]);
% RMS Differences
% individual:
Ntargets = [s.Nt]'; % # of targets
relfreq = Ntargets/sum(Ntargets(:));
sd_pe = (pred.pe-act.pe).^2; % squared differences
d.pe = sqrt(relfreq(:)' * sd_pe(:)); % weighted RMS diff.
sd_qe = (pred.qe-act.qe).^2;
d.qe = sqrt(relfreq(:)' * sd_qe(:));
amt_cache('set',cachename,r,d,s,act,pred);
else
data = data_majdak2013;
idpart = ismember({s.id},{data.id});
end
s = s(idpart);
d.total = (d.pe/90 + d.qe/100) /2;
if nargout >0; varargout{1} = d; end
if nargout >1; varargout{2} = r; end
if nargout >2; varargout{3} = s; end
if flags.do_plot
%% Measures
% Quartiles
quart_pe_part = quantile(pred.pe,[.25 .50 .75]);
quart_qe_part = quantile(pred.qe,[.25 .50 .75]);
quart_pe_exp = quantile(act.pe,[.25 .50 .75]);
quart_qe_exp = quantile(act.qe,[.25 .50 .75]);
% Chance performance
[qe0,pe0] = baumgartner2014_pmv2ppp('chance',...
'rang',-30-model.kv.mrsmsp:1:210+model.kv.mrsmsp);
%% Plots
dx = 0.15;
figure
subplot(121)
x=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-');
set(x,'MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
hold on
x=errorbar((1:3),quart_pe_exp(2,:),...
quart_pe_exp(2,:) - quart_pe_exp(1,:),...
quart_pe_exp(3,:) - quart_pe_exp(2,:),...
'ko-');
set(x,'MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','w');
plot([0,4],[pe0,pe0],'k--')
title(['e_{PE} = ' num2str(d.pe,'%0.1f') '\circ , r_{PE} = ' num2str(r.pe,'%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 57.5],...
'XTickLabel',{'BB';'LP';'W'},...
'YMinorTick','on','FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
l = legend('Model','Actual');
set(l,'Location','north','FontSize',kv.FontSize-1)
subplot(122)
x=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-');
set(x,'MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
hold on
x=errorbar((1:3),quart_qe_exp(2,:),...
quart_qe_exp(2,:) - quart_qe_exp(1,:),...
quart_qe_exp(3,:) - quart_qe_exp(2,:),...
'ko-');
set(x,'MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','w');
plot([0,4],[qe0 qe0],'k--')
title(['e_{QE} = ' num2str(d.qe,'%0.1f') '% , r_{QE} = ' num2str(r.qe,'%0.2f')],...
'FontSize',kv.FontSize)
ylabel('Quadrant Error (%)','FontSize',kv.FontSize)
set(gca,...
'XLim',[0.5 3.5],...
'XTick',1:3,...
'YLim',[0.1 49],...
'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
%% ------ NUMCHAN EXAMPLES -----------------------------------------------------------
if flags.do_numchan_ex
% Stimulus Level
SL = 50; % presentation level of stimuli
% Model Settings
model.kv.SPL = SL + kv.SL2SPL;
latdivision = 0; % lateral angle
dlat = 10;
% Experimental Settings
Conditions = {'BB','CL','N24','N9','N3'};
% Vocoder Settings
N = [inf,inf,24,9,3];
flow = 300; % lowest corner frequency
fhigh = 16000; % highest corner frequency
%% Computations
s = data_baumgartner2016('argimport',model.flags,model.kv);
s = s(ismember({s.id},'NH33'));
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 strcmp(Cond,'BB') || strcmp(Cond,'CL')
s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
else
n = N(C);
[syncrnfreq, GETtrain] = local_GETVocoder('',imp,n,flow,fhigh,0,100,stimPar);
corners = [syncrnfreq(1);syncrnfreq(:,2)];
ref = s(ll).spdtfs{ii};
cond = zeros(length(imp),size(ref,2),2);
for ch = 1:size(ref,3)
for ang = 1:size(ref,2)
cond(:,ang,ch) = local_channelize('', 0.5*ref(:,ang,ch), ref(:,1), imp, n, corners, [], ...
GETtrain, stimPar, 1, 0.01*s(ll).fs, 0.01*s(ll).fs);
end
end
s(ll).spdtfs_c{ii} = cond;
end
end
end
if flags.do_plot
if C==1; fp = figure; end
subplot(2,length(Conditions),C)
if strcmp(Cond,'CL')
stim = repmat([1;zeros(s(ll).fs/100-1,1)],17,1); % pulse train with 100pps
else
stim = noise(8e3,1,'white');
end
sig = lconv(stim,s(ll).spdtfs_c{ii});
[mp,fc] = baumgartner2016_spectralanalysis(sig,kv.SPL,...
'argimport',model.flags,model.kv,'target','ID',s(ll).id,'Condition',Cond);
pcolor(fc,s(ll).polang{ii},mp(:,:,1)')
shading flat
xlabel('Frequency (Hz)')
ylabel('Discharge rate')
title(Cond)
end
%% Run Model
for ll = 1:length(s)
clear qe pe qe_t pe_t
for ii = 1:length(latdivision)
if strcmp(Cond,'CL')
stim = repmat([1;zeros(s(ll).fs/100-1,1)],10,1); % pulse train with 100pps
else
stim = [];
end
[p,rang] = baumgartner2016(...
s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},'argimport',model.flags,model.kv,...
'ID',s(ll).id,'Condition',Cond,...
'stim',stim,'fsstim',s(ll).fs,...
'mrsmsp',s(ll).mrs,'S',s(ll).S,'lat',latdivision(ii),...
'polsamp',s(ll).polang{ii},'priordist',s(ll).priordist);
[ 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
subplot(2,length(Conditions),C+length(Conditions))
plot_baumgartner2014(p,s(ll).polang{ii},rang,...
s(ll).target{ii},s(ll).response{ii},...
'MarkerSize',kv.MarkerSize,'cmax',0.05,'no_colorbar');
title({['A: PE = ' num2str(s(ll).pe_exp(C,1),2) '\circ, QE = ' num2str(s(ll).qe_exp(C,1),2) '%'];...
['P: PE = ' num2str(s(ll).pe_part(C,1),2) '\circ, QE = ' num2str(s(ll).qe_part(C,1),2) '%']},'FontSize',kv.FontSize-1)
text(90,240,Cond,...
'FontSize',kv.FontSize,'Color','w','HorizontalAlignment','center')
xlabel('Target Angle (deg)','FontSize',kv.FontSize)
ylabel('Response Angle (deg)','FontSize',kv.FontSize)
set(gca,'FontSize',kv.FontSize-1)
set(gca,'XTickLabel',{[];[];0;[];60;[];120;[];180;[];[]})
set(gca,'YTickLabel',{-60;[];0;[];60;[];120;[];180;[];240})
end
end
end
end
varargout{1} = s;
end
%% ------ FIG NUMCHAN ----------------------------------------------------------
if flags.do_numchan
% Stimulus Level
SL = 50; % presentation level of stimuli
model.kv.SPL = SL + kv.SL2SPL;
cachename = ['numchan_tar' num2str(model.kv.SPL,'%u') 'dB_' cachename];
[N,r,d,s] = amt_cache('get',cachename,flags.cachemode);
if isempty(N)
% Model Settings
latdivision = 0; % lateral angle
dlat = 30;
% Experimental Settings
Conditions = {'BB','CL','N24','N18','N12','N9','N6','N3'};
% Vocoder Settings
N = fliplr([3,6,9,12,18,24,30,36]); % # of vocoder channels
flow = 300; % lowest corner frequency
fhigh = 16000; % highest corner frequency
%% Computations
s = data_baumgartner2016('argimport',model.flags,model.kv);
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 strcmp(Cond,'BB') || strcmp(Cond,'CL')
s(ll).spdtfs_c{ii} = s(ll).spdtfs{ii};
else
n = N(C);
cachenameGET = ['numchan_GET_N' num2str(n) '_' s(ll).id];
cond = amt_cache('get',cachenameGET);
if isempty(cond)
[syncrnfreq, GETtrain] = local_GETVocoder('',imp,n,flow,fhigh,0,100,stimPar);
corners = [syncrnfreq(1);syncrnfreq(:,2)];
ref = s(ll).spdtfs{ii};
cond = zeros(length(imp),size(ref,2),2);
for ch = 1:size(ref,3)
for ang = 1:size(ref,2)
cond(:,ang,ch) = local_channelize('', 0.5*ref(:,ang,ch), ref(:,1), imp, n, corners, [], ...
GETtrain, stimPar, 1, 0.01*s(ll).fs, 0.01*s(ll).fs);
end
end
amt_cache('set',cachenameGET,cond);
end
s(ll).spdtfs_c{ii} = cond;
end
end
end
%% Run Model
idpart = [];
for ll = 1:length(s)
clear qe pe qe_t pe_t
if sum(ismember({data.id},s(ll).id)) % if actual participant actual targets
for ii = 1:length(latdivision)
if strcmp(Cond,'CL')
stim = repmat([1;zeros(s(ll).fs/100-1,1)],17,1); % pulse train with 100pps
else
stim = [];
end
[p,rang] = baumgartner2016(...
s(ll).spdtfs_c{ii},s(ll).spdtfs{ii},...
'argimport',model.flags,model.kv,...
'ID',s(ll).id,'Condition',Cond,...
'stim',stim,'fsstim',s(ll).fs,...
'mrsmsp',s(ll).mrs,'S',s(ll).S,'lat',latdivision(ii),...
'polsamp',s(ll).polang{ii},'priordist',s(ll).priordist);
[ qe_t(ii),pe_t(ii) ] = baumgartner2014_pmv2ppp( ...
p , s(ll).polang{ii} , rang , s(ll).target{ii} );
end
idpart = [idpart,ll];
% 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
end
end
disp(['Condition ' Cond ' completed.'])
end
act.qe = [s(idpart).qe_exp];
act.pe = [s(idpart).pe_exp];
pred.qe = [s(idpart).qe_part];
pred.pe = [s(idpart).pe_part];
r.qe = local_corrcoeff(act.qe,pred.qe);
disp(['QE: r = ' num2str(r.qe,'%0.2f')]);
r.pe = local_corrcoeff(act.pe,pred.pe);
disp(['PE: r = ' num2str(r.pe,'%0.2f')]);
% RMS Differences
% individual:
Ntargets = [s.Nt]; % # of targets
relfreq = Ntargets/sum(Ntargets(:));
sd_pe = (pred.pe-act.pe).^2; % squared differences
d.pe = sqrt(relfreq(:)' * sd_pe(:)); % weighted RMS diff.
sd_qe = (pred.qe-act.qe).^2;
d.qe = sqrt(relfreq(:)' * sd_qe(:));
s = rmfield(s,{'spdtfs','spdtfs_c','Obj','itemlist'});
amt_cache('set',cachename,N,r,d,s)
end
data = data_goupell2010;
idpart = ismember({s.id},{data.id});
s = s(idpart);
d.total = (d.pe/90 + d.qe/100) /2;
if nargout >0; varargout{1} = d; end
if nargout >1; varargout{2} = r; end
if nargout >2; varargout{3} = s; end
if nargout >3; varargout{4} = N; end
if flags.do_plot
%% Quartiles
quart_pe_part = fliplr(quantile([s(idpart).pe_part]',[.25 .50 .75]));
quart_qe_part = fliplr(quantile([s(idpart).qe_part]',[.25 .50 .75]));
quart_pe_exp = fliplr(quantile([s(idpart).pe_exp]',[.25 .50 .75]));
quart_qe_exp = fliplr(quantile([s(idpart).qe_exp]',[.25 .50 .75]));
[qe0,pe0] = baumgartner2014_pmv2ppp('chance',...
'rang',-30-model.kv.mrsmsp:1:210+model.kv.mrsmsp);
%% Plot
dx = 0.7;
figure
%% PE
subplot(121)
x=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-');
set(x,'MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
hold on
x=errorbar(fliplr(N),quart_pe_exp(2,:),...
quart_pe_exp(2,:) - quart_pe_exp(1,:),...
quart_pe_exp(3,:) - quart_pe_exp(2,:),...
'ko-');
set(x,'MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','w');
plot([0,2*max(N)],[pe0,pe0],'k--')
xlabel('Num. of channels','FontSize',kv.FontSize)
ylabel('RMS of local errors (deg)','FontSize',kv.FontSize)
title(['e = ' num2str(d.pe,'%0.1f') '\circ , r = ' num2str(r.pe,'%0.2f')],...
'FontSize',kv.FontSize,'FontWeight','normal')
set(gca,'XLim',[1 max(N)+2],'XTick',fliplr(N),...%[3 6 9 12 18 24 30],...
'XTickLabel',{3;6;'';12;18;24;'CL';'BB'},...
'YLim',[27 57.5],...
'YMinorTick','on','FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
l = legend('Model','Actual');
set(l,'Location','northeast','FontSize',kv.FontSize)
%% QE
subplot(122)
x=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-');
set(x,'MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','k');
hold on
x=errorbar(fliplr(N),quart_qe_exp(2,:),...
quart_qe_exp(2,:) - quart_qe_exp(1,:),...
quart_qe_exp(3,:) - quart_qe_exp(2,:),...
'ko-');
set(x,'MarkerSize',kv.MarkerSize,...
'MarkerFaceColor','w');
plot([0,2*max(N)],[qe0,qe0],'k--')
title(['e = ' num2str(d.qe,'%0.1f') '% , r = ' num2str(r.qe,'%0.2f')],...
'FontSize',kv.FontSize,'FontWeight','normal')
xlabel('Num. of channels','FontSize',kv.FontSize)
ylabel('% Quadrant errors','FontSize',kv.FontSize)
set(gca,'XLim',[1 max(N)+2],'XTick',fliplr(N),...%[3 6 9 12 18 24 30],...
'XTickLabel',{3;6;'';12;18;24;'CL';'BB'},...
'YLim',[0.1 49],...
'YMinorTick','on',...
'YAxisLocation','left','FontSize',kv.FontSize,...
'TickLength',2*get(gca,'TickLength'))
set(gcf,'PaperPosition',[1,1,10,3.5])
end
end
%% SPLtem evaluation based on SpatStrat and NumChan
if flags.do_evalSPLtem || flags.do_fig6
SL2SPL = kv.SL2SPL;
SPLtem = 40:10:80;
SPLtemR = [40,80];
for ll = 1:length(SPLtem)
% [bl(ll).d,bl(ll).r,bl(ll).s] = exp_baumgartner2016('baseline','no_plot',...
% 'ModelSettings',{'SPLtem',SPLtem(ll)},'SL2SPL',SL2SPL);
[nc(ll).d,nc(ll).r,nc(ll).s] = exp_baumgartner2016('numchan','no_plot',...
'ModelSettings',{'argimport',model.flags,model.kv,'SPLtem',SPLtem(ll)},...
'SL2SPL',SL2SPL);
[ss(ll).d,ss(ll).r,ss(ll).s] = exp_baumgartner2016('spatstrat','no_plot',...
'ModelSettings',{'argimport',model.flags,model.kv,'SPLtem',SPLtem(ll)},...
'SL2SPL',SL2SPL);
end
ll = length(SPLtem)+1;
[nc(ll).d,nc(ll).r,nc(ll).s] = exp_baumgartner2016('numchan','no_plot',...
'ModelSettings',{'argimport',model.flags,model.kv,'SPLtem',SPLtemR},'SL2SPL',SL2SPL);
[ss(ll).d,ss(ll).r,ss(ll).s] = exp_baumgartner2016('spatstrat','no_plot',...
'ModelSettings',{'argimport',model.flags,model.kv,'SPLtem',SPLtemR},'SL2SPL',SL2SPL);
%% Number of listener-specific data points (#subjects * #conditions)
N.nc = length(nc(1).s)*length(nc(1).s(1).pe_exp);
N.ss = length(ss(1).s)*length(ss(1).s(1).pe_exp);
N.all = N.nc + N.ss;
%% Pool residues across experiments (averaged acc. to #data)
for ll = 1:length(SPLtem)+1
dtotal(ll) = (N.nc*nc(ll).d.total + N.ss*ss(ll).d.total) / N.all;
dpe(ll) = (N.nc*nc(ll).d.pe + N.ss*ss(ll).d.pe) / N.all;
dqe(ll) = (N.nc*nc(ll).d.qe + N.ss*ss(ll).d.qe) / N.all;
end
%% Prediction residuum obtained by chance prediction
chance.dpe = 1;
chance.dqe = 1;
chance.dtotal = 1;
%% Plots
XLim = [SPLtem(1),SPLtem(end)]+[-1,1]*3;
figure
hax = local_tightsubplot(1,1,kv.gap,kv.marg_h,kv.marg_w);
axes(hax(1))
% single-SPL
hi(1,1) = plot(SPLtem,dqe(1:length(SPLtem))/chance.dqe,'d-');
hold on
hi(2,1) = plot(SPLtem,dpe(1:length(SPLtem))/chance.dpe,'s-');
% multiple-SPL
hi(3,1) = plot(SPLtemR,dqe(end)/chance.dqe*[1,1],'--');
hi(5,1) = plot(mean(SPLtemR),dqe(end)/chance.dqe,'d');
hi(4,1) = plot(SPLtemR,dpe(end)/chance.dpe*[1,1],'--');
hi(6,1) = plot(mean(SPLtemR),dpe(end)/chance.dpe,'s');
% general
set(gca,'YLim',[3.1,10.9],'XLim',XLim)
% xlabel('Template SPL (dB)')
ylabel('Prediction residuum')
amt_disp('Predictive power for SpatStrat and NumChan.');
xlabel('Template SPL (dB)')
htmp = plot([0,0],[0,0],'k-');
leg = legend([hi(5:6,1);htmp;hi(3,1)],...
'% Quadrant errors','Local RMS error (deg)','Single SPL','Multiple SPLs');
set(leg,'Location','northoutside','Orientation','vertical')
set(hi,'LineWidth',1,'MarkerSize',kv.MarkerSize,'Color',zeros(1,3))
set(hi,'MarkerFaceColor',zeros(1,3))
set(hi(3:4,:),'LineStyle','--')
end
%% Modeling Sabin et al. (2005)
if flags.do_sabin2005
% Presentation level of baseline stimuli (for calibration)
SL = 50;
model.kv.SPL = SL + kv.SL2SPL;
cachename = ['sabin2005_calibtar' num2str(model.kv.SPL,'%u') 'dB_' cachename];
cachename = [cachename '_SL2SPL' num2str(kv.SL2SPL) 'dB'];
if model.kv.lat ~= 0
cachename = [cachename '_lat' num2str(model.kv.lat)];
end
if model.flags.do_SPLtemAdapt
cachename = [cachename '_SPLtemAdapt'];
end
if model.flags.do_gammatone
cachename = [cachename '_minSPL' num2str(model.kv.GT_minSPL)];
end
if kv.sabin2005_Sdivisor ~= 1
cachename = [cachename '_Sdiv' num2str(kv.sabin2005_Sdivisor*10)];
end
if flags.do_nomrs
cachename = [cachename '_nomrs'];
end
pred = amt_cache('get',cachename,flags.cachemode);
if isempty(pred)
s = data_baumgartner2016('argimport',model.flags,model.kv);
SPL = [0:5:20,30:10:70]+kv.SL2SPL;
if flags.do_nomrs
model.kv.mrsmsp = 0;
end
pred.pvfront = nan(length(SPL),length(s));
pred.pvrear = nan(length(SPL),length(s));
pred.gfront = nan(length(SPL),length(s));
pred.grear = nan(length(SPL),length(s));
pred.precfront = nan(length(SPL),length(s));
pred.precrear = nan(length(SPL),length(s));
pred.qe = nan(length(SPL),length(s));
pred.pe = nan(length(SPL),length(s));
pred.prob = cell(length(SPL),length(s));
for ii = 1:length(s)
for ll = 1:length(SPL)
if flags.do_nostat
[pred.qe(ll,ii),pred.prob{ll,ii},m] = baumgartner2016(...
s(ii).Obj,s(ii).Obj,...
'argimport',model.flags,model.kv,...
'ID',s(ii).id,'S',s(ii).S/kv.sabin2005_Sdivisor,'SPL',SPL(ll),...
'priordist',s(ii).priordist,'QE');
pred.pvfront(ll,ii) = nan;
pred.pvrear(ll,ii) = nan;
pred.gfront(ll,ii) = nan;
pred.grear(ll,ii) = nan;
pred.precfront(ll,ii) = nan;
pred.precrear(ll,ii) = nan;
else
[pred.pvfront(ll,ii),pred.prob{ll,ii},m] = baumgartner2016(...
s(ii).Obj,s(ii).Obj,...
'argimport',model.flags,model.kv,...
'ID',s(ii).id,'S',s(ii).S/kv.sabin2005_Sdivisor,'SPL',SPL(ll),...
'priordist',s(ii).priordist,'pVeridicalPfront');
pred.pvrear(ll,ii) = localizationerror(m,'pVeridicalPrear');
pred.gfront(ll,ii) = localizationerror(m,'gainPfront');
pred.grear(ll,ii) = localizationerror(m,'gainPrear');
pred.precfront(ll,ii) = localizationerror(m,'precPregressFront');
pred.precrear(ll,ii) = localizationerror(m,'precPregressRear');
pred.qe(ll,ii) = localizationerror(m,'querrMiddlebrooks');
pred.pe(ll,ii) = localizationerror(m,'rmsPmedianlocal');
end
end
amt_disp([num2str(ii,'%u') ' of ' num2str(length(s),'%u') ' done']);
end
pred.SPL = SPL;
amt_cache('set',cachename,pred)
end
data = data_sabin2005;
data.SPL = data.SL + kv.SL2SPL; % assumption on SL
%% Gain conversion: slope in deg -> limited range and equidistant
if flags.do_gainInDeg
pred.gfront = local_gain2slope(pred.gfront);
pred.grear = local_gain2slope(pred.grear);
data.gain.f.m = local_gain2slope(data.gain.f.m);
data.gain.r.m = local_gain2slope(data.gain.r.m);
data.gain.f.sd = local_gain2slope(data.gain.f.sd);
data.gain.r.sd = local_gain2slope(data.gain.r.sd);
end
%% Restriction to reliable data (at least 75% audible trials in Sabin2005)
dvar = {'gain','pqv','var'}; % data variable names
mvar = {'g','pv','prec'}; % modeled variable names
SL = pred.SPL-kv.SL2SPL; % assumption on SL
SPL = pred.SPL;
minSL = 15;
iddata = data.SL >= minSL;
idpred = SL >= minSL;
% Remove
for ii = 1:length(mvar)
eval(['data.' dvar{ii} '.f.m = data.' dvar{ii} '.f.m(iddata);'])
eval(['data.' dvar{ii} '.f.sd = data.' dvar{ii} '.f.sd(iddata);'])
eval(['pred.' mvar{ii} 'front = pred.' mvar{ii} 'front(idpred,:);'])
eval(['data.' dvar{ii} '.r.m = data.' dvar{ii} '.r.m(iddata);'])
eval(['data.' dvar{ii} '.r.sd = data.' dvar{ii} '.r.sd(iddata);'])
eval(['pred.' mvar{ii} 'rear = pred.' mvar{ii} 'rear(idpred,:);'])
end
data.SL = data.SL(iddata);
data.SPL = data.SPL(iddata);
SL = SL(idpred);
SPL = SPL(idpred);
pred.SPL = SPL;
pred.SL = SL;
%% Pool data for 20 dB
id20 = find(data.SL == 20,2,'first');
data20p = data; % init
for ii = 1:length(mvar)
eval(['data20p.' dvar{ii} '.f.m = [data.' dvar{ii} '.f.m(1:' num2str(id20(1)-1) '),mean(data.' dvar{ii} '.f.m(' num2str(id20(1)) ':' num2str(id20(2)) ')),data.' dvar{ii} '.f.m(' num2str(id20(2)+1) ':end)];']) % 20dB SL pooled
eval(['data20p.' dvar{ii} '.r.m = [data.' dvar{ii} '.r.m(1:' num2str(id20(1)-1) '),mean(data.' dvar{ii} '.r.m(' num2str(id20(1)) ':' num2str(id20(2)) ')),data.' dvar{ii} '.r.m(' num2str(id20(2)+1) ':end)];'])
end
%% Prediction deviation score
limits = {'90','100','45'}; % for normalization
idc = ismember(SL,data.SL);
d = zeros(length(mvar),2);
for ii = 1:length(mvar)
if flags.do_nostat
eval(['d(ii,1) = sqrt(mean((mean(pred.' mvar{ii} 'front(idc,:)),2) - transpose(data20p.' dvar{ii} '.f.m)).^2))/' limits{ii} ';'])
eval(['d(ii,2) = sqrt(mean((mean(pred.' mvar{ii} 'rear(idc,:),2) - transpose(data20p.' dvar{ii} '.r.m)).^2))/' limits{ii} ';'])
else
eval(['d(ii,1) = sqrt(nanmean((nanmean(pred.' mvar{ii} 'front(idc,:),2) - transpose(data20p.' dvar{ii} '.f.m)).^2))/' limits{ii} ';'])
eval(['d(ii,2) = sqrt(nanmean((nanmean(pred.' mvar{ii} 'rear(idc,:),2) - transpose(data20p.' dvar{ii} '.r.m)).^2))/' limits{ii} ';'])
end
end
d = mean(d(:));
amt_disp(['Prediction deviation score: ' num2str(d)]);
%% Correlation coefficient
r = zeros(length(mvar),2);
for ii = 1:length(mvar)
if flags.do_stat
eval(['r(ii,1) = corrcoeff( nanmean(pred.' mvar{ii} 'front(idc,:),2) , transpose(data20p.' dvar{ii} '.f.m) );'])
eval(['r(ii,2) = corrcoeff( nanmean(pred.' mvar{ii} 'rear(idc,:),2) , transpose(data20p.' dvar{ii} '.r.m) );'])
end
end
r = mean(r(:));
amt_disp(['Correlation coefficient: ' num2str(r)]);
%% Output
varargout{1}=d;
varargout{2}=r;
varargout{3}=pred;
varargout{4}=data;
%% Plot
if flags.do_plot
minSPLf = minSL + kv.SL2SPL; % minSLf
minSPLr = minSL + kv.SL2SPL; % minSLr
maxSPL = 70 + kv.SL2SPL;
marSPL = 4.9; % margin of SL
if flags.do_gainInDeg
ylim = {[-2,60],[-5,105],[8,42]}; % ylimits acc. to Sabin et al. (2005)
elabel = {'Slope (deg)','% Quasi-veridical','Variability (deg)'}; % plot ylabels
else
ylim = {[-0.2,1.6],[-5,105],[8,42]}; % ylimits acc. to Sabin et al. (2005)
elabel = {'Gain','% Quasi-veridical','Variability (deg)'}; % plot ylabels
end
figure;
ha = local_tightsubplot(3,2,kv.gap,kv.marg_h,kv.marg_w);
for ii = 1:length(mvar)
axes(ha(1+(ii-1)*2))
if ii == 1
plot([-10,100],[45,45],'k--') % ideal slope
hold on
end
eval(['p1 = errorbar(SPL-0.3,nanmean(pred.' mvar{ii} 'front,2),nanstd(pred.' mvar{ii} 'front,1,2));'])
hold on
eval(['p2 = errorbar(data.SPL+0.3,data.' dvar{ii} '.f.m,data.' dvar{ii} '.f.sd);'])
set(p1,'MarkerFaceColor','k','Marker','^','Color','k','MarkerSize',kv.MarkerSize)
set(p2,'MarkerFaceColor','w','Marker','^','Color','k','MarkerSize',kv.MarkerSize)
axis([minSPLf-marSPL,maxSPL+marSPL,ylim{ii}])
ylabel(elabel{ii},'FontSize',kv.FontSize)
set(gca,'XTick',round(minSPLf/10)*10:10:maxSPL)
set(gca,'TickLength',2*get(gca,'TickLength'),'FontSize',kv.FontSize)
if ii == 1
title('Front','FontSize',kv.FontSize)
elseif ii == 3
xlabel('SPL (dB)','FontSize',kv.FontSize)
end
if ii<3
set(gca,'XTickLabel',[])
end
axes(ha(2+(ii-1)*2))
if ii == 1
plot([-10,100],[45,45],'k--') % ideal slope
hold on
end
eval(['p1 = errorbar(SPL-0.3,nanmean(pred.' mvar{ii} 'rear,2),nanstd(pred.' mvar{ii} 'rear,1,2));'])
hold on
eval(['p2 = errorbar(data.SPL+0.3,data.' dvar{ii} '.r.m,data.' dvar{ii} '.r.sd);'])
set(p1,'MarkerFaceColor','k','Marker','o','Color','k','MarkerSize',kv.MarkerSize)
set(p2,'MarkerFaceColor','w','Marker','o','Color','k','MarkerSize',kv.MarkerSize)
axis([minSPLr-marSPL,maxSPL+marSPL,ylim{ii}])
set(gca,'XTick',round(minSPLr/10)*10:10:maxSPL,'YTickLabel',[])
set(gca,'TickLength',2*get(gca,'TickLength'),'FontSize',kv.FontSize)
if ii == 1
title('Rear','FontSize',kv.FontSize)
elseif ii == 3
xlabel('SPL (dB)','FontSize',kv.FontSize)
end
end
end
end
if flags.do_impairment
if isempty(errorflag)
errorflag = 'QE';
amt_disp('Localization performance measure not chosen -> QE used.');
end
cohc = sort(kv.cOHCset,'descend'); % default: [1,0.4,0.1,0];
ft = kv.FTset; % default: {1:3;1;2;3};
SPL = sort(kv.SPLset,'ascend'); % default: [80, 50]
cachename = ['impairment_' cachename '_' errorflag];
if length(SPL) == 1
cachename = [cachename '_SPL' num2str(SPL)];
elseif length(SPL) == 2
cachename = [cachename '_SPLs' num2str(SPL(1)) 'and' num2str(SPL(2))];
end
if model.flags.do_SPLtemAdapt
cachename = [cachename '_SPLtemAdapt'];
end
if model.flags.do_NHtem
cachename = [cachename '_NHtem'];
end
cachename = strrep([cachename '_cohc' num2str(cohc)],' ','');cachename = strrep(cachename,'.','p');
s = amt_cache('get',cachename,flags.cachemode);
if isempty(s)
s = data_baumgartner2016('argimport',model.flags,model.kv);
if not(isempty(kv.subjects))
s = s(kv.subjects);
end
for ii=1:length(s)
for cc=1:length(cohc)
for ll=1:length(SPL)
for ff=1:length(ft)
err = baumgartner2016(s(ii).Obj,s(ii).Obj,...
'argimport',model.flags,model.kv,'ID',s(ii).id,errorflag,'fiberTypes',ft{ff},...
'S',s(ii).S,'cohc',cohc(cc),'SPL',SPL(ll),'priordist',s(ii).priordist);
s(ii).err(cc,ff,ll) = err;
s(ii).cohc(cc,ff,ll) = cohc(cc);
s(ii).SPL(cc,ff,ll) = SPL(ll);
s(ii).ft{cc,ff,ll} = ft{ff};
end
end
end
amt_disp([num2str(ii,'%u') ' of ' num2str(length(s),'%u') ' done']);
end
s = rmfield(s,{'Obj','itemlist'});
amt_cache('set',cachename,s);
end
varargout{1} = s;
err = cat(4,s.err);
Ncohc = size(err,1);
Nft = size(err,2);
Nspl = size(err,3);
Ncond = length(s(1).cohc(:)); % #conditions
Nsub = length(s); % #subjects
mtx.err = reshape(shiftdim(err,3),[Nsub,Ncond]);
% labels
cohcstrA = num2str(s(1).cohc(:),'%2.1f');
cohcstr = cell(length(cohcstrA),1);
for ii = 1:length(cohcstrA)
cohcstr{ii} = strrep(cohcstrA(ii,:),'1.0','1');
cohcstr{ii} = strrep(cohcstr{ii},'0.0','0');
end
SPLstr = num2str(s(1).SPL(:),'%2.0f');
ftnum = s(1).ft(:);
ftstr = cell(length(ftnum),1);
XTickLabel = ftstr;
for ii = 1:length(ftnum)
lab = ['cohc: ' cohcstr(ii,:) ', ' SPLstr(ii,:) 'dB, '];
if ftnum{ii} == 1
ftstr{ii} = 'low-SR';
elseif ftnum{ii} == 2
ftstr{ii} = 'med-SR';
elseif ftnum{ii} == 3
ftstr{ii} = 'high-SR';
else % ft{ii} == 1:3
ftstr{ii} = 'all SRs';
end
XTickLabel{ii} = [lab ftstr{ii}];
end
% sort data acc. to ascending SPL
[tmp,idSPLsort] = sort(s(1).SPL(:));
ftstr = ftstr(idSPLsort);
cohcstr = cellstr(cohcstr(idSPLsort,:));
SPLstr = cellstr(SPLstr(idSPLsort,:));
mtx.err = mtx.err(:,idSPLsort,:);
% Output meta data
meta(1).name = 'Listener';
meta(2).name = 'Condition';
meta(2).data = {ftstr(:),cohcstr(:),SPLstr(:)};
if flags.do_plot
% interaction plots
% merr = mean(err,4); % average across subjects; dims: [cohc,ft,SPL]
% fig(1) = figure('Name','Interaction Plots');
% if length(kv.SPLset) > 1
% % OHC-SPL
% subplot(1,3,1)
% err_OHC_SPL = squeeze(mean(merr,2));
% plot(cohc,err_OHC_SPL)
% legend(num2str(SPL(:)))
% xlabel('C_{OHC}')
% ylabel(errorflag)
% % OHC-FT
% subplot(1,3,2)
% err_OHC_FT = squeeze(mean(merr,3));
% plot(cohc,err_OHC_FT)
% legend('All','LSR','MSR','HSR')
% xlabel('C_{OHC}')
% % SPL-FT
% subplot(1,3,3)
% err_FT_SPL = squeeze(mean(merr,1));
% plot(SPL,err_FT_SPL)
% legend('All','LSR','MSR','HSR')
% xlabel('SPL')
% else % only OHC-FT
% plot(cohc,merr)
% legend('All','LSR','MSR','HSR')
% xlabel('C_{OHC}')
% end
emax = max(mtx.err(:))+0.5;
emin = min(mtx.err(:))-0.5;
De = emax-emin;
[qe0,pe0] = baumgartner2014_pmv2ppp('chance',...
'rang', -30-model.kv.mrsmsp : 1 : 210+model.kv.mrsmsp );
if flags.do_splitSPL
colors = {(1-1/length(kv.SPLset))*ones(1,3),zeros(1,3)};
for ll = length(kv.SPLset):-1:1
NcondPlot = Ncond/length(kv.SPLset);
fig(ll+1) = figure;
id = SPL(ll)==str2double(SPLstr);
b = boxplot(mtx.err(:,id),{ftstr(id),cohcstr(id),SPLstr(id)},...
'plotstyle','compact','medianstyle','line','colors',colors{ll},...
'factorgap',[],'labelverbosity','all','symbol','');
hold on
% chance performance
if strcmp(errorflag,'QE')
echance = qe0;
elseif strcmp(errorflag,'PE')
echance = pe0;
end
h = plot([0.5,NcondPlot+0.5],[echance,echance],'k--');uistack(h,'bottom')
if flags.do_FTlabel
for ii = 1:Nft
jj = 1+(ii-1)*Ncohc;
yftlbl = 1.05*max(emax,echance);
text(jj,yftlbl,ftstr{ii*Ncohc},'FontSize',kv.FontSize) % inside panel
end
end
axis([0.5,NcondPlot+0.5,emin-De/20,emax+De/8])
set(gca,'XTick',1:NcondPlot,'XTickLabel',cohcstr(1:NcondPlot),'FontSize',kv.FontSize);
xlabel({'OHC gain, C_{OHC}'},'FontSize',kv.FontSize,'FontWeight','bold')
ylabel(errorflag,'FontSize',kv.FontSize,'FontWeight','bold')
end
else % compriseSPL
fig(2) = figure;
b = boxplot(mtx.err,{ftstr(:),cohcstr(:),SPLstr(:)},...
'plotstyle','compact','colors',repmat([.5,.5,.5;0,0,0],Ncond/Nspl,1),'medianstyle','line',...
'factorgap',[],'labelverbosity','all','symbol','');
hold on
if flags.do_FTlabel
for ii = 1:Nft
jj = 1+(ii-1)*Nspl*Ncohc;
yftlbl = emax;
text(jj,yftlbl,ftstr{ii*Ncohc},'FontSize',kv.FontSize) % inside panel
end
end
% chance performance
if strcmp(errorflag,'QE')
h = plot([0.5,Ncond+0.5],[qe0,qe0],'k--');uistack(h, 'bottom')
elseif strcmp(errorflag,'PE')
h = plot([0.5,Ncond+0.5],[pe0,pe0],'k--');uistack(h, 'bottom')
end
axis([0.5,Ncond+0.5,emin-De/20,emax+De/8])
set(gca,'XTick',1.5:2:Ncond,'XTickLabel',cohcstr(1:Ncohc),'FontSize',kv.FontSize);
xlabel({'OHC gain, C_{OHC}'},'FontSize',kv.FontSize,'FontWeight','bold')
ylabel(errorflag,'FontSize',kv.FontSize,'FontWeight','bold')
end
else
fig = [];
end
if flags.do_stat && ~verLessThan('matlab','8.2')
s = rmfield(s,{'pe_exp','qe_exp','S','pe_exp_lat','qe_exp_lat','target','response','Ntar','mrs','fs'});
t = array2table(mtx.err);
if length(kv.SPLset) > 1 % 3-way repeated-measures ANOVA
within = table(SPLstr,cohcstr,ftstr,'VariableNames',{'SPL','Cohc','FT'});
rm = fitrm(t,['Var1-Var' num2str(Ncond) ' ~ 1'],'WithinDesign',within); % no between-subjects factors -> only intercept
[tbl.ranova,A,C,D] = ranova(rm,'WithinModel','Cohc*SPL*FT');
else % 2-way repeated-measures ANOVA
within = table(cohcstr,ftstr,'VariableNames',{'Cohc','FT'});
rm = fitrm(t,['Var1-Var' num2str(Ncond) ' ~ 1'],'WithinDesign',within); % no between-subjects factors -> only intercept
[tbl.ranova,A,C,D] = ranova(rm,'WithinModel','Cohc*FT');
end
tbl.ranova.Properties.RowNames = strrep(tbl.ranova.Properties.RowNames,'(Intercept):','');
% Mauchly's test for sphericity
tbl.mauchly = mauchly(rm,C);
tbl.mauchly.Properties.RowNames = tbl.ranova.Properties.RowNames(1:2:end);
% Sphericity corrections
tbl.eps = epsilon(rm,C);
tbl.eps.Properties.RowNames = tbl.ranova.Properties.RowNames(1:2:end);
% Add corrected DFs to ranova table
idrep = round(0.5:0.5:length(tbl.eps.GreenhouseGeisser)); % repeat iteratively
tbl.ranova.DFGG = tbl.ranova.DF .* tbl.eps.GreenhouseGeisser(idrep);
% Add effect sizes to ranova table
SSeffect = tbl.ranova.SumSq(1:2:end);
SSerror = tbl.ranova.SumSq(2:2:end);
eta_pSq = nan(2*length(SSerror),1);
eta_pSq(1:2:end) = SSeffect./(SSeffect+SSerror); % effect size per (eta_partial)^2
tbl.ranova.eta_pSq = eta_pSq;
% Post-hoc analysis
tbl.posthoc.Cohc = multcompare(rm,'Cohc');
tbl.posthoc.FT = multcompare(rm,'FT');
% Display results
amt_disp(['Repeated-measures ANOVA for ' errorflag],'documentation');
amt_disp(tbl.ranova);
amt_disp('Mauchly test and sphericity corrections');
amt_disp([tbl.mauchly,tbl.eps]);
amt_disp('Posthoc analysis');
amt_disp(tbl.posthoc.Cohc);
amt_disp(tbl.posthoc.FT);
amt_disp('Reported in publication:');
amt_disp(tbl.ranova(3:end,[9,4,6,10]),'documentation');
else
tbl = [];
end
varargout = {tbl;fig;mtx.err;meta};
end
if flags.do_fig7
errorflag = {...
'QE','% Quadrant errors';...
'PE','Local RMS error (deg)';...
};
SPLset = 60;
NHtemflag = '';%'NHtem';
for ii = 1:length(errorflag)
tbl{ii} = exp_baumgartner2016('impairment',errorflag{ii,1},'SPLset',SPLset,...
'noFTlabel','FontSize',kv.FontSize,flags.cachemode,'ModelSettings',{NHtemflag});
ylabel(errorflag{ii,2},'FontSize',kv.FontSize)
fig(ii) = gcf;
ax(ii) = gca;
end
% Combine panels and add FT labels
N.cohc = 4;
N.ft = 4;
labels = {'all SRs','low-SR','med-SR','high-SR'};
figC = figure;
marg = [.11,.06;.11,.03];
ha = local_tightsubplot(length(errorflag),length(SPLset),0,marg(1,:),marg(2,:));
for ii = 1:length(ha)
axes(ha(ii));
copyobj(allchild(ax(ii)),ha(ii))
set(ha(ii),'XTick',get(ax(ii),'XTick'))
set(ha(ii),'XTickLabel',get(ax(ii),'XTickLabel'))
set(ha(ii),'YTick',get(ax(ii),'YTick'))
set(ha(ii),'YTickLabel',get(ax(ii),'YTickLabel'))
if ii <= length(SPLset) % add SPL and FT labels at top panels
if length(SPLset) > 1
title([num2str(SPLset(ii)) ' dB SPL'])
end
if length(SPLset) == 1
yy = 49; % like title
else
yy = 55;
end
set(gca,'YLim',[-2,yy])
for ff = 1:N.ft
jj = (ff-0.5)*N.cohc+.5;
text(jj,52,labels{ff},'FontWeight','bold','HorizontalAlignment','center')
end
else
if length(SPLset) == 1
yy = 51.5; % like title
else
yy = 54;
end
set(gca,'YLim',[17,yy])
end
if ii == 1 % show ylabel at left panels
ylabel(errorflag{1,2},'FontWeight','bold')
elseif ii == length(SPLset)+1
ylabel(errorflag{2,2},'FontWeight','bold')
else
set(gca,'YTickLabel','')
ylabel('')
end
if ii > length(SPLset) % show ylabel at bottom panels
xlabel({' ';'OHC gain, C_{OHC}'},'FontWeight','bold')
end
end
set(findall(figC,'-property','FontSize'),'FontSize',kv.FontSize)
close(fig)
varargout = {tbl,errorflag};
end
if flags.do_cOHCvsSens
[sens,meta.sens] = exp_baumgartner2016('dynrangecheck','dprime','no_plot',...
'ModelSettings',{'argimport',model.flags,model.kv});
idSPL = meta.sens(1).data == model.kv.SPL;
Sensitivity = squeeze(sens(idSPL,:,:))'; % cOHC x SR
Sensitivity = circshift(Sensitivity,1,2); % all SRs first
errorlabel = {'QE';'PE'};
for ee = 1:length(errorlabel)
[~,~,errorPrediction,meta.err] = exp_baumgartner2016('impairment',errorlabel{ee},...
'no_plot','nostat','ModelSettings',{'argimport',model.flags,model.kv});
[r,p]=corrcoef(mean(errorPrediction,1)',Sensitivity(:));
if not(isscalar(r))
r = r(2);
end
amt_disp(['Correlation between average ',errorlabel{ee},...
' and intensity discriminability:'],'documentation');
amt_disp([' r = ',num2str(r),' ( p = ',num2str(p),' )'],'documentation');
end
end
if flags.do_effectOnCues || flags.do_fig9
sid = 10; % listener No.
s = data_baumgartner2016('argimport',model.flags,model.kv);
[dtf,polang] = extractsp(0,s(sid).Obj);
tmp = lconv(noise(8e3,1,'white'),dtf);
sig = reshape(tmp,[size(tmp,1),size(dtf,2),size(dtf,3)]);
amt_disp(['Exemplary listener: ' s(sid).id]);
ymin = 0;
ymax = model.kv.mgs*pi;
spl = kv.SPLset;
if flags.do_FT
% Effect of FT
FT = {1:3,1,2,3};
mp_ft = cell(length(FT),length(spl));
gp_ft = cell(length(FT),length(spl));
for ll = 1:length(spl)
for ft = 1:length(FT)
[mp_ft{ft,ll},fc] = baumgartner2016_spectralanalysis(sig,spl(ll),...
'target','ID',s(sid).id ,'Condition','baseline','fiberTypes',FT{ft},flags.cachemode);
[gp_ft{ft,ll},gfc] = baumgartner2016_gradientextraction(mp_ft{ft,ll},fc,'mgs',kv.mgs);
end
end
if flags.do_plot
figure
ha = local_tightsubplot(length(FT),length(cohc),kv.gap,kv.marg_h,kv.marg_w);
colormap gray
colormap(flipud(colormap))
labels = {'LMH','L','M','H'};
for ll = 1:length(spl)
for ft = 1:length(FT)
axes(ha(ft+(ll-1)*length(FT)))
pcolor(gfc,polang,gp_ft{ft,ll}.m(:,:,1)')
shading flat
caxis([ymin,ymax])
title(labels{ft})
xlabel('Frequency (kHz)','FontWeight','bold')
ylabel('Polar angle (deg)','FontWeight','bold')
set(gca,'XScale','log','FontSize',kv.FontSize)
set(gca,'layer','top',...
'XTick',[1,2,4,8,16]*1e3,...
'XTickLabel',[1,2,4,8,16],...
'YTick',-30:30:180,...
'FontSize',kv.FontSize)
end
end
end
else
% Effect of C_OHC
cohc = kv.cOHCset;
gp_cohc = cell(length(cohc),length(spl));
for ll = 1:length(spl)
for cc = 1:length(cohc)
[mp,fc] = baumgartner2016_spectralanalysis(sig,spl(ll),'target',...
'argimport',flags,kv,'ID',s(sid).id,'Condition','baseline','cohc',cohc(cc));
[gp_cohc{cc,ll},gfc] = baumgartner2016_gradientextraction(mp,fc,'mgs',model.kv.mgs);
end
end
if flags.do_plot
figure
ha = local_tightsubplot(length(spl),length(cohc),[.02,.01],kv.marg_h,kv.marg_w);
colormap gray
colormap(flipud(colormap))
labels = {'C_{OHC} = 1',['C_{OHC} = ' num2str(cohc(2),'%1.1f')],['C_{OHC} = ' num2str(cohc(3),'%1.1f')],'C_{OHC} = 0'};
for ll = 1:length(spl)
for cc = 1:length(cohc)
axes(ha(cc+(ll-1)*length(cohc)))
pcolor(gfc,polang,gp_cohc{cc,ll}.m(:,:,1)')
shading flat
caxis([ymin,ymax])
% xlabel and COHC
set(gca,'XTick',[1,2,4,8,16]*1e3,'XTickLabel',[1,2,4,8,16])
if ll == 1
title(labels{cc},'FontSize',kv.FontSize)
end
if ll == length(spl)
if cc == 2
xlabel([' ',...
'Frequency (kHz)'],'FontSize',kv.FontSize,'FontWeight','bold')
end
else
set(gca,'XTickLabel',[])
end
% ylabel and SPL
if cc == 1
ylabel('Polar angle (deg)','FontSize',kv.FontSize,'FontWeight','bold')
if length(spl) > 1
text(180,200,[num2str(spl(ll)) ' dB'],'FontWeight','bold','FontSize',kv.FontSize)
end
else
set(gca,'YTickLabel',[])
end
set(gca,'XScale','log','FontSize',kv.FontSize)
set(gca,'layer','top',...
'TickLength',2*get(gca,'TickLength'),...
'YTick',-30:30:180,...
'FontSize',kv.FontSize)
end
end
c = colorbar;
set(c,'Position',[.93,.2,.02,.6])
set(get(c,'Title'),'String','Spikes/s/ERB','FontSize',kv.FontSize)
end
end
end
if flags.do_evalSpectralContrast
sid = 10; % listener No.
s = data_baumgartner2016('argimport',model.flags,model.kv);
[dtf,polang] = extractsp(0,s(sid).Obj);
tmp = lconv(noise(8e3,1,'white'),dtf);
sig = reshape(tmp,[size(tmp,1),size(dtf,2),size(dtf,3)]);
amt_disp(['Exemplary listener: ' s(sid).id]);
spl = kv.SPLset;
FT = {1:3,1,2,3};
mp_ft = cell(length(FT),length(spl));
for ll = 1:length(spl)
for ft = 1:length(FT)
[mp_ft{ft,ll},fc] = baumgartner2016_spectralanalysis(sig,spl(ll),...
'target','ID',s(sid).id ,'Condition','baseline','fiberTypes',FT{ft},flags.cachemode);
spectRange(ft,ll) = range(mp_ft{ft,ll}(:));
end
spectralContrast(:,ll) = spectRange(2:end,ll)/spectRange(1,ll);
end
FTlabels = {'low-SR','med-SR','high-SR'};
tab = table(spectralContrast,'RowNames',FTlabels);
amt_disp(tab,'documentation');
varargout{1} = tab;
end
if flags.do_ratelevelcurves || flags.do_fig2
splmax = 130; % dB
splminplot = 5;
fc = 4000; % Hz
sig = noise(0.5*48e3,1,'white'); % 100-ms Gaussian white noise burst
spl = 0:10:splmax;
mp = zeros(1,length(spl),2,3);
for ii = 1:length(spl)
mp(:,ii,:,:) = baumgartner2016_spectralanalysis(cat(3,sig,sig),spl(ii),...
'target','ID','','Condition','ratelevelcurves','fiberTypes',1:3,...
'ftopt','cohc',1,'flow',fc,'fhigh',fc,'numCF',1);
end
% Plot Rate-intensity curves
if flags.do_plot
figure
sty = {'kv-','ko-','k^-'};
for ft = 1:3
h(ft) = plot(spl,mean(mp(:,:,1,ft),1),sty{ft});
hold on
xlabel('SPL (dB)','FontSize',kv.FontSize)
ylabel('Firing rate (spikes/s)','FontSize',kv.FontSize)
end
set(h,'MarkerFaceColor','k')
leg = legend(h,{'low-SR','med-SR','high-SR'});
set(leg,'Location','northoutside','FontSize',kv.FontSize,'Orientation','horizontal')
axis([splminplot,splmax-5,-30,369])
XTick = round(splminplot/10)*10:10:splmax;
XTickLabel = num2cell(XTick);
XTickLabel(2:2:end) = {' '};
set(gca,'XTick',XTick,'XTickLabel',XTickLabel,'FontSize',kv.FontSize)
end
end
if flags.do_fig5
exp_baumgartner2016('numchan','FontSize',kv.FontSize,'MarkerSize',4,flags.cachemode);
singleFig(1) = gcf;
axNumChan = get(gcf,'Children');
exp_baumgartner2016('spatstrat','FontSize',kv.FontSize,'MarkerSize',4,flags.cachemode);
singleFig(2) = gcf;
axSpatStrat = get(gcf,'Children');
% Combined plot as shown in publication (commented out because code is not
% compatible with mat2doc)
% % Adjust marker symbols
% set([allchild(axNumChan(1)),allchild(axSpatStrat(1))],'Marker','d') % QE
% set([allchild(axNumChan(3)),allchild(axSpatStrat(3))],'Marker','s') % PE
%
% fig = figure;
% ha = local_tightsubplot(2,2,0,[.1,.05],[.11,.02]);
%
% % Data
% copyobj(allchild(axNumChan(1)),ha(1))
% copyobj(allchild(axNumChan(3)),ha(3))
% copyobj(allchild(axSpatStrat(1)),ha(2))
% copyobj(allchild(axSpatStrat(3)),ha(4))
%
% % Labels
% QElabel = '% Quadrant errors';
% PElabel = 'Local RMS error (deg)';
% title(ha(1),'Goupell et al. (2010)')
% title(ha(2),'Majdak et al. (2013)')
% ylabel(ha(1),QElabel,'FontWeight','bold')
% ylabel(ha(3),PElabel,'FontWeight','bold')
% xlabel(ha(3),'Num. of channels','FontWeight','bold')
% xlabel(ha(4),'Spectral modification','FontWeight','bold')
% legend(ha(3),{'Model','Actual'},'Position',[0.5 0.80 0.1147 0.0440]);
%
% % Limits
% set(ha([1,3]),'XLim',get(axNumChan(1),'XLim'));
% set(ha([2,4]),'XLim',get(axSpatStrat(1),'XLim'));
% set(ha([1,2]),'YLim',[1,45.9])
% set(ha([3,4]),'YLim',[26,54])
%
% % Ticks
% set(ha,'TickLength',[0.02,.01],'Box','on')
% set(ha(1:2),'YTick',get(axNumChan(1),'YTick'))
% set(ha(3:4),'YTick',get(axNumChan(3),'YTick'))
% set(ha(1),'YTickLabel',get(axNumChan(1),'YTickLabel'))
% set(ha(3),'YTickLabel',get(axNumChan(3),'YTickLabel'))
% set(ha([1,3]),'XTick',get(axNumChan(1),'XTick'))
% set(ha([2,4]),'XTick',get(axSpatStrat(1),'XTick'))
% set(ha(3),'XTickLabel',get(axNumChan(1),'XTickLabel'))
% set(ha(4),'XTickLabel',get(axSpatStrat(1),'XTickLabel'))
%
% close(singleFig)
end
if flags.do_sensitivity || flags.do_fig8
[data,meta] = exp_baumgartner2016('dynrangecheck','dprime','SPLset',60,...
'FontSize',kv.FontSize,flags.cachemode,'marg_w',[.15,.01],'marg_h',[.05,.05]);
dprime60dB = squeeze(data(meta(1).data == 60,[4,1:3],:))';
[~,~,qe,errmeta] = exp_baumgartner2016('impairment','QE','SPLset',60,'no_plot','nostat');
[correlation_QE.r,correlation_QE.p] = local_corrcoeff(mean(qe,1)',dprime60dB(:));
amt_disp('Correlation between dprime and quadrant errors:','documentation');
amt_disp(correlation_QE,'documentation');
[~,~,pe] = exp_baumgartner2016('impairment','PE','SPLset',60,'no_plot','nostat');
[correlation_PE.r,correlation_PE.p] = local_corrcoeff(mean(pe,1)',dprime60dB(:));
amt_disp('Correlation between dprime and local RMS errors:','documentation');
amt_disp(correlation_PE,'documentation');
end
if flags.do_dynrangecheck
splmax = 130; % dB
splHRTF = kv.SPLset; % dB (SPL range of targets)
splminplot = 15;
sig = noise(0.1*48e3,1,'white'); % 100-ms Gaussian white noise burst
spl = 0:10:splmax;
cohc = kv.cOHCset;
panels = {'C_{OHC} = 1',['C_{OHC} = ' num2str(cohc(2),'%1.1f')],['C_{OHC} = ' num2str(cohc(3),'%1.1f')],'C_{OHC} = 0'};
if flags.do_separate
labels = {'low-SR','med-SR','high-SR','all SRs'};
else
labels = {'LMH','MH','H'};
end
figure
ha = local_tightsubplot(4,1,kv.gap,kv.marg_h,kv.marg_w);
if flags.do_ratelevel
Y = nan(length(spl),4,length(cohc));
else
Y = nan(length(spl)-1,4,length(cohc));
end
for cc = 1:length(cohc)
mp = zeros(model.kv.numCF,length(spl),2,3);
if flags.do_separate
for ii = 1:length(spl)
[mp(:,ii,:,:),fc] = baumgartner2016_spectralanalysis(cat(3,sig,sig),spl(ii),...
'target','ID','','Condition','dynrangecheck','fiberTypes',1:3,'ftopt','cohc',cohc(cc));
end
else
fiberTypes = {1:3,2:3,3};
for ft = 1:length(fiberTypes)
for ii = 1:length(spl)
[mp(:,ii,:,ft),fc] = baumgartner2016_spectralanalysis(cat(3,sig,sig),spl(ii),...
'target','dynrangecheck','fiberTypes',fiberTypes{ft},'cohc',cohc(cc));
end
end
end
splplot = spl;
if flags.do_dynrangeDiff
mp = diff(mp,1,2)/mean(diff(spl));
splplot = spl(2:end);
end
if flags.do_dprime
sd = 2.6*mp.^0.34;
sdDiff = sqrt(sd(:,1:length(spl)-1,:,:).^2 + sd(:,2:length(spl),:,:).^2);
mp = diff(mp,1,2)./sdDiff;
splplot = spl(2:end);
end
axes(ha(cc))
% target HRTF range
color = {.8*ones(1,3)};
if length(splHRTF) == 2
color = {.9*ones(1,3),color{1}};
end
for ll = 1:length(splHRTF)
a(1) = area(splHRTF(ll)+[-10,10],[1e3,1e3],'EdgeColor',ones(1,3));
hold on
a(2) = area(splHRTF(ll)+[-10,10],-[1e3,1e3],'EdgeColor',ones(1,3));
set(a,'FaceColor',color{ll})
end
set(gca,'Layer','top')
% Rate-intensity curves
sty = {'kv-','ko-','k^-'};
for ft = 1:3
Y(:,ft,cc) = mean(mp(:,:,1,ft),1);
h(ft) = plot(splplot,Y(:,ft,cc),sty{ft});
hold on
if cc == length(cohc)
xlabel('SPL (dB)','FontSize',kv.FontSize,'FontWeight','bold')
end
if flags.do_dynrangeDiff
text(splminplot+5,9,panels{cc},'FontSize',kv.FontSize)
ylabel({'Rate difference (spikes/s/dB)'},'FontSize',kv.FontSize)
elseif flags.do_dprime
text(splminplot+5,5,panels{cc},'FontSize',kv.FontSize)
if cc==3
text(0,6,{'d'''},'FontSize',kv.FontSize,'FontWeight','bold')
end
else
text(splminplot+5,330,panels{cc},'FontSize',kv.FontSize)
ylabel('Firing rate (spikes/s)','FontSize',kv.FontSize)
end
end
% All SRs combined
ftd = [0.16,0.23,0.61]; % Liberman (1978)
Y(:,4,cc) = Y(:,1:3,cc) * ftd';
h(4) = plot(splplot,Y(:,4,cc),'k--');
set(h,'MarkerFaceColor','k')
if cc == 1%length(cohc)
leg = legend(h,labels);
set(leg,'Location','northoutside','FontSize',kv.FontSize,'Orientation','vertical')
set(leg,'Position',[.4,.96,.33,.03])
end
if flags.do_dynrangeDiff
plot([0,splmax],[0,0],'k--') % sensitivity threshold
axis([splminplot,splmax-5,-1,10.5])
elseif flags.do_dprime
axis([splminplot,splmax-5,-1.5,5.9])
else
axis([splminplot,splmax-5,-20,399])
end
XTick = 20:10:130;
XTickLabel = num2cell(XTick);
XTickLabel(2:2:end) = {' '};
set(gca,'XTick',XTick,'XTickLabel',XTickLabel,'FontSize',kv.FontSize)
end
meta(1).name = 'SPL';
meta(1).data = splplot;
meta(2).name = labels;
meta(2).data = {1,2,3,1:3};
meta(3).name = 'OHC gain';
meta(3).data = cohc;
varargout = {Y;meta};
end
if flags.do_localevel
dlat = 30; % deg
condition = data_baumgartner2015('ConditionNames');
cachename = ['localevel_' cachename];
if not(model.kv.gammashortfact == 1)
cachename = [cachename '_gsf' num2str(model.kv.gammashortfact,'%1.1f')];
end
if not(model.kv.Sshortfact == 1)
cachename = [cachename '_Ssf' num2str(model.kv.Sshortfact,'%1.1f')];
end
if not(model.kv.psgeshort == 1)
cachename = [cachename '_psgec' num2str(model.kv.psgeshort,'%1.1f')];
end
if model.flags.do_SPLtemAdapt
cachename = [cachename '_SPLtemAdapt'];
end
if model.flags.do_gammatone
cachename = [cachename '_minSPL' num2str(model.kv.GT_minSPL) '_maxSPL' num2str(model.kv.GT_maxSPL)];
end
[pred,ref] = amt_cache('get', cachename, flags.cachemode);
if isempty(pred)
pred.qe = nan(7,length(condition));
pred.pe = pred.qe;
ref.qe = pred.qe;
ref.pe = pred.qe;
for cc = 1:length(condition)
data = data_baumgartner2016(condition{cc},'model','argimport',model.flags,model.kv);
for ll = 1:length(data)
latresp = data(ll).itemlist(:,7);
idlat = latresp <= dlat & latresp > -dlat;
itemlist = data(ll).itemlist(idlat,:);
exptang = itemlist(:,6);
exprang = itemlist(:,8);
ref.pe(ll,cc) = real(localizationerror(itemlist,'rmsPmedianlocal'));
ref.qe(ll,cc) = real(localizationerror(itemlist,'querrMiddlebrooks'));
if strcmp(condition{cc},'Long')
clbl = condition{cc};
else % short
clbl = '';
end
[err,pmv] = baumgartner2016(data(ll).Obj,data(ll).Obj,...
'argimport',model.flags,model.kv,'ID',data(ll).id,'Condition',clbl,'S',data(ll).S,...
'stim',data(ll).stim,'fsstim',data(ll).fsstim,'SPL',data(ll).SPL,...
'QE_PE_EB','exptang',exptang,'priordist',data(ll).priordist);
pred.pmv{ll,cc} = pmv;
pred.qe(ll,cc) = err.qe;
pred.pe(ll,cc) = err.pe;
end
end
amt_cache('set',cachename,pred,ref);
else
data = data_baumgartner2016('all');
end
perrmtx = ref.pe';
pred.perrmtx = pred.pe';
qerrmtx = ref.qe';
pred.qerrmtx = pred.qe';
Nsub = length(data);
%% Prediction residues
mm = 1;
r_perr(mm) = local_corrcoeff([pred.pe],[ref.pe]);
e_perr(mm) = mean(rms([pred.pe]-[ref.pe]));
r_qerr(mm) = local_corrcoeff([pred.qe],[ref.qe]);
e_qerr(mm) = mean(rms([pred.qe]-[ref.qe]));
amt_disp(' e_PE r_PE e_QE r_QE','documentation');
amt_disp([num2str(e_perr(mm),'%2.1f') '\deg ' num2str(r_perr(mm),'%2.2f') ' ' num2str(e_qerr(mm),'%2.1f') '% ' num2str(r_qerr(mm),'%2.2f')],'documentation');
varargout{1} = 0.5* (e_perr(mm)/90 + e_qerr(mm)/100);
%% Plots
if flags.do_plot
if flags.do_performance
LineWidth = 1;
symb = {...
'rs-';...
'bd-';...
'gh-';...
'mv-';...
'c*-';...
};
symbExp = 'ko--';
name{mm} = 'Pred.';
% Legend
legendentry = {'Actual, long';'Actual, short'};
legendentry{2+2*mm-1} = [name{mm} ', long'];
legendentry{2+2*mm} = [name{mm} ', short'];
xval = 10:10:70;
xtext = 10;
% Local central RMS error
hfig = figure;
ha = local_tightsubplot(8,2,kv.gap,kv.marg_h,kv.marg_w);
for ii=1:Nsub
axes(ha(2*ii));
hlong = plot(50,perrmtx(1,ii),symbExp(1:2));
set(hlong,'MarkerFaceColor',symbExp(1),'LineWidth',LineWidth)
set(gca,'YAxisLocation','right','YMinorTick','on')
set(gca,'TickLength',kv.TickLength)
hold on
hshort = plot(xval,perrmtx(2:end,ii),symbExp);
set(hshort,'MarkerFaceColor','w','LineWidth',LineWidth)
for mm = 1:length(pred)
hlong = plot(50,pred(mm).perrmtx(1,ii),symb{mm}(1:2));
set(hlong,'MarkerFaceColor',symb{mm}(1),'LineWidth',LineWidth)
hshort = plot(xval,pred(mm).perrmtx(2:end,ii),symb{mm});
set(hshort,'MarkerFaceColor','w','LineWidth',LineWidth)
end
if ii==1
title('Local error (deg)','FontSize',kv.FontSize)
end
axis([5,75,26,54])
end
% Pooled
axes(ha(2*ii+2));
hlong = errorbar(50,mean(perrmtx(1,:)),std(perrmtx(1,:)),symbExp(1:2));
set(hlong,'MarkerFaceColor',symbExp(1),'LineWidth',LineWidth)
set(gca,'YAxisLocation','right','YMinorTick','on','TickLength',kv.TickLength)
hold on
hshort = errorbar(xval,mean(perrmtx(2:end,:),2),std(perrmtx(2:end,:),1,2),symbExp);
set(hshort,'MarkerFaceColor','w','LineWidth',LineWidth)
hlong = errorbar(50,mean(pred(mm).perrmtx(1,:),2),std(pred(mm).perrmtx(1,:),1,2),symb{mm}(1:2));
set(hlong,'MarkerFaceColor',symb{mm}(1),'LineWidth',LineWidth)
hshort = errorbar(xval,mean(pred(mm).perrmtx(2:end,:),2),std(pred(mm).perrmtx(2:end,:),1,2),symb{mm});
set(hshort,'MarkerFaceColor','w','LineWidth',LineWidth)
axis([5,75,26,54])
xlabel('SL (dB)','FontSize',kv.FontSize)
% Quadrant error
for ii=1:Nsub
axes(ha(2*ii-1));
hlong = plot(50,qerrmtx(1,ii),symbExp(1:2));
set(gca,'YMinorTick','on')
set(gca,'TickLength',kv.TickLength)
set(hlong,'MarkerFaceColor',symbExp(1),'LineWidth',LineWidth)
hold on
hshort = plot(xval,qerrmtx(2:end,ii),symbExp);
set(hshort,'MarkerFaceColor','w','LineWidth',LineWidth)
for mm = 1:length(pred)
hlong = plot(50,pred(mm).qerrmtx(1,ii),symb{mm}(1:2));
set(hlong,'MarkerFaceColor',symb{mm}(1),'LineWidth',LineWidth)
hshort = plot(xval,pred(mm).qerrmtx(2:end,ii),symb{mm});
set(hshort,'MarkerFaceColor','w','LineWidth',LineWidth)
end
if ii==1
title('Quadrant error (%)','FontSize',kv.FontSize)
end
axis([5,75,-4,54])
% Listener ID
ylabel([data(ii).id ' '],'FontSize',kv.FontSize,'Rotation',0,'FontWeight','bold');
end
% Pooled
axes(ha(2*ii+1));
hlong = errorbar(50,mean(qerrmtx(1,:)),std(qerrmtx(1,:)),symbExp(1:2));
set(hlong,'MarkerFaceColor',symbExp(1),'LineWidth',LineWidth)
set(gca,'YMinorTick','on','TickLength',kv.TickLength)
hold on
hshort = errorbar(xval,mean(qerrmtx(2:end,:),2),std(qerrmtx(2:end,:),1,2),symbExp);
set(hshort,'MarkerFaceColor','w','LineWidth',LineWidth)
hlong = errorbar(50,mean(pred(mm).qerrmtx(1,:),2),std(pred(mm).qerrmtx(1,:),1,2),symb{mm}(1:2));
set(hlong,'MarkerFaceColor',symb{mm}(1),'LineWidth',LineWidth)
hshort = errorbar(xval,mean(pred(mm).qerrmtx(2:end,:),2),std(pred(mm).qerrmtx(2:end,:),1,2),symb{mm});
set(hshort,'MarkerFaceColor','w','LineWidth',LineWidth)
axis([5,75,-4,54])
ylabel('Pooled ','FontSize',kv.FontSize,'Rotation',0,'FontWeight','bold');
xlabel('SL (dB)','FontSize',kv.FontSize)
end
if flags.do_pmv
conditions = data_baumgartner2015('ConditionNames');
idcond = [1,2,5,8]; %{'Long','10dB','30dB','50dB','70dB'};
Nc = length(idcond);
hfig = figure;
ha = local_tightsubplot(Nsub,Nc,kv.gap,kv.marg_h,kv.marg_w);
for cc=1:Nc
idc = idcond(cc);
data = data_baumgartner2015(conditions{idc});
for ll=1:Nsub
axes( ha(cc + Nc*(ll-1)) )
plot_baumgartner2014(pred.pmv{ll,idc}.p,pred.pmv{ll,idc}.tang,pred.pmv{ll,idc}.rang,...
data(ll).itemlist(:,6),data(ll).itemlist(:,8),...
'MarkerSize',kv.MarkerSize/2,'no_colorbar','cmax',0.05)
% Labels
set(gca,'FontSize',kv.FontSize-1)
if not(cc==1)
set(gca,'YTickLabel',[])
ylabel('')
elseif not(ll==4)
ylabel('')
end
if not(ll==Nsub)
set(gca,'XTickLabel',[])
xlabel('')
else
set(gca,'XTickLabelRotation',90)
xlabel('')
if cc==3
xlabel({' ';'Target Angle (deg) '},'FontSize',kv.FontSize-1)
end
end
if ll==1
title(conditions{idc},'FontSize',kv.FontSize)
end
if cc==Nc
set(gca,'YAxisLocation','right')
ylabel([' ' data(ll).id],'FontSize',kv.FontSize,'FontWeight','bold','Rotation',0);
end
end
end
end
end
end
end
%% ------------------------------------------------------------------------
% ---- INTERNAL FUNCTIONS ------------------------------------------------
% ------------------------------------------------------------------------
function hM_warped = local_warphrtf(hM,fs)
% warps HRTFs acc. to Walder (2010)
% Usage: hM_warped = warp_hrtf(hM,fs)
N = fs;
fu = 2800;
fowarped = 8500;
fo = 16000;
fscala = [0:fs/N:fs-fs/N]';
hM_warped = zeros(512,size(hM,2),size(hM,3));
fuindex = max(find(fscala <= fu)); % 2800
fowindex = min(find(fscala >= fowarped));
foindex = min(find(fscala >= fo));
for canal = 1:size(hM,3)
for el = 1:size(hM,2)
yi = ones(fs/2+1,1)*(10^-(70/20));
flin1 = [fscala(1:fuindex-1)];
flin2 = [linspace(fscala(fuindex),fscala(foindex),fowindex-fuindex+1)]';
fscalawarped = [flin1 ; flin2];
% interpolate
x = fscala(1:foindex);
H = fft(hM(:,el,canal),N);
Y = H(1:foindex);
xi = fscalawarped;
yi(1:length(xi),1) = interp1(x, Y, xi,'linear');
yges=([yi; conj(flipud(yi(2:end-1)))]);
hges = ifft([yges(1:end)],length(yges));
hges=fftshift(ifft(yges));
hwin=hges(fs/2-256:fs/2+768);
hwinfade = local_FWfade(hwin,512,24,96,192);
hM_warped(1:end,el,canal)=hwinfade;
end
end
end
function [syncrnfreq, GETtrain] = local_GETVocoder(filename,in,channum,lower,upper,alpha,GaussRate,stimpar)
warning('off')
% channel/timing parameters
srate=stimpar.SamplingRate;
nsamples=length(in); % length of sound record
duration=nsamples/srate; % duration of the signal (in s)
t=0:1/srate:duration;
t=t(1:nsamples);
extendedrange = 0;
if alpha == -1 % Log12ER case
extendedrange = 1;
alpha = 0.28;
elseif alpha == 0 % use predefined alphas
switch channum
case 3
alpha=1.42;
case 6
alpha=0.67;
case 9
alpha=0.45;
case 12
alpha=0.33;
case 18
alpha=0.22;
case 24
alpha=0.17;
otherwise
error(['Alpha not predefined for channels number of ' num2str(channu)]);
end
end
% Synthesis: These are the crossover frequencies that the output signal is mapped to
crossoverfreqs = logspace( log10(lower), log10(upper), channum + 1);
if extendedrange == 1
crossoverfreqs = [300,396,524,692,915,1209,1597,2110,2788,4200,6400,10000,16000];
end
syncrnfreq(:,1)=crossoverfreqs(1:end-1);
syncrnfreq(:,2)=crossoverfreqs(2:end);
for i=1:channum
cf(i) = sqrt( syncrnfreq(i,1)*syncrnfreq(i,2) );
end
% Pulse train parameters
Gamma = alpha*cf;
if extendedrange == 1
Gamma(9) = 1412;
Gamma(10) = 2200;
Gamma(11) = 3600;
Gamma(12) = 6000;
end
N = ceil(duration*GaussRate); % number of pulses
Genv=zeros(N,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(N,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,:),[N 1]);
for n=1:N
T = round((n)/N*nsamples);
Genv(n,:)=circshift(Genv(n,:),[1 T-1]);
end
GETtrain(i,:) = sum(Genv);
%normalize energy
Energy(i) = norm(GETtrain(i,:))/sqrt(length(t));
% Energy(i) = rms(GETtrain(i,:)); % !!!!!!!!!!!!
GETtrain(i,:) = GETtrain(i,:)/Energy(i);
end
end
end
function out=local_channelize(fwavout, h, h0, in, channum, corners, syncrnfreq, ...
GETtrain, stimpar, amp, fadein, fadeout)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% *** v1.2.0 %
% GET Vocoder scaled by energy, not envelope %
% %
% *** v1.1.0 %
% Added Gaussian Envelope Tone (GET) Vocoder %
% GET pulse train is generated and passed to this function %
% M. Goupell, May 2008 %
% %
% *** v1.0.0 %
% Modified from ElecRang/matlab/makewav.m v1.3.1 %
% To be used with Loca by M. Goupell, Nov 2007 %
% Now program receives a sound rather than reading a speech file %
% Rewrote according to the specifications (PM, Jan. 2008) %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% h = hrtf
% h0 = hrtf for reference position
% in = reference noise
% noise = number of channels of noise vectors
srate=stimpar.SamplingRate;
N=length(in); % length of sound record
d=0.5*srate; % frequency scalar
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Read corner frequencies for channels %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Synthesis: These are the crossover frequencies that the output signal is mapped to
% calculated now in GETVocoder, passed to this function
% Analysis: These are the crossover frequencies that the input signal is subdivided by
if length(corners)<=channum
error('You need at least one more corner frequency than number of channels');
end
anacrnfreq(:,1)=corners(1:end-1);
anacrnfreq(:,2)=corners(2:end);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Analysis: Filter Signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
inX=fftfilt(h,in);
order=4; %order of butterworth filter
% out=zeros(1,N);
% filtX=zeros(channum,N);
for i=1:channum
[b, a]=butter(order, anacrnfreq(i,:)/d);
out=filter(b, a, inX); % bandpass-filtering
% E(i)=norm(out,1)/sqrt(N);
E(i) = rms(out);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Synthesis %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% each channel
for i=1:channum
outX(i,:) = GETtrain(i,:) * E(i);
end
% sum me up scotty
out=sum(outX,1);
% for i = 1:channum
% subplot(channum/3,3,i)
% plot(outX(i,(1:480)))
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Save file %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
out=out*(10^(amp/20))/sqrt(sum(out.^2))*sqrt(sum(in.^2))*sqrt(sum(h.^2))/sqrt(sum(h0.^2));
% 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=local_FWfade(out,0,fadein,fadeout);
% wavwrite(out,srate,stimpar.Resolution,fwavout);
end
function out = local_FWfade(inp, len, fadein, fadeout, offset)
% FW_FADE crop/extend and fade in/out a vector.
%
% OUT = FW_FADE(INP, LEN, FADEIN, FADEOUT, OFFSET) crops or extends with zeros the signal INP
% up to length LEN. Additionally, the result is faded in/out using HANN window with
% the length FADEIN/FADEOUT, respectively. If given, an offset can be added to show
% where the real signal begins.
%
% When used to crop signal, INP is cropped first, then faded out.
% When used to extend signal, INP is faded out first, then extended too provide fading.
%
% INP: vector with signal (1xN or Nx1)
% LEN: length of signal OUT (without OFFSET)
% FADEIN: number of samples to fade in, beginning from OFFSET
% FADEOUT: number of samples to fade out, ending at the end of OUT (without OFFSET)
% OFFSET: number of offset samples before FADEIN, optional
% OUT: cropped/extended and faded vector
%
% Setting a parameter to 0 disables corresponding functionality.
% ExpSuite - software framework for applications to perform experiments (related but not limited to psychoacoustics).
% Copyright (C) 2003-2010 Acoustics Research Institute - Austrian Academy of Sciences; Piotr Majdak and Michael Mihocic
% Licensed under the EUPL, Version 1.1 or ? as soon they will be approved by the European Commission - subsequent versions of the EUPL (the "Licence")
% You may not use this work except in compliance with the Licence.
% You may obtain a copy of the Licence at: http://ec.europa.eu/idabc/eupl
% Unless required by applicable law or agreed to in writing, software distributed under the Licence is distributed on an "AS IS" basis, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
% See the Licence for the specific language governing permissions and limitations under the Licence.
% 7.11.2003
% 22.08.2005: improvement: INP may be 1xN or Nx1 now.
% Piotr Majdak (piotr@majdak.com)
ss=length(inp); % get length of inp
% offset
if ~exist('offset','var')
offset = 0;
end
offset=round(offset);
len=round(len);
fadein=round(fadein);
fadeout=round(fadeout);
if offset>ss
error('OFFSET is greater than signal length');
end
% create new input signal discarding offset
inp2=inp(1+offset:end);
ss=length(inp2);
% fade in
if fadein ~= 0
if fadein > ss
error('FADEIN is greater than signal length');
end
han=hanning(fadein*2);
if size(inp2,1)==1
han=han';
end
inp2(1:fadein) = inp2(1:fadein).*han(1:fadein);
end
% fade out window
if len == 0
len = ss;
end
if len <= ss
% crop and fade out
out=inp2(1:len);
% fade out
if fadeout ~= 0
if fadeout > len
error('FADEOUT is greater than cropped signal length');
end
han = hanning(2*fadeout);
if size(out,1)==1
han=han';
end
out(len-fadeout+1:end)=out(len-fadeout+1:end).*han(fadeout+1:end);
end
else
% fade out and extend
if fadeout ~= 0
if fadeout > ss
error('FADEOUT is greater than signal length');
end
han = hanning(2*fadeout);
inp2(ss-fadeout+1:end)=inp2(ss-fadeout+1:end).*han(fadeout+1:end);
end
out = [zeros(offset,1); inp2; zeros(len-ss,1)];
end
end
function ha = local_tightsubplot(Nh, Nw, gap, marg_h, marg_w)
% tight_subplot creates "subplot" axes with adjustable gaps and margins
%
% ha = tight_subplot(Nh, Nw, gap, marg_h, marg_w)
%
% in: Nh number of axes in hight (vertical direction)
% Nw number of axes in width (horizontaldirection)
% gap gaps between the axes in normalized units (0...1)
% or [gap_h gap_w] for different gaps in height and width
% marg_h margins in height in normalized units (0...1)
% or [lower upper] for different lower and upper margins
% marg_w margins in width in normalized units (0...1)
% or [left right] for different left and right margins
%
% out: ha array of handles of the axes objects
% starting from upper left corner, going row-wise as in
% going row-wise as in
%
% Example: ha = tight_subplot(3,2,[.01 .03],[.1 .1],[.1 .1])
% for ii = 1:6; axes(ha(ii)); plot(randn(10,ii)); end
% set(ha(1:4),'XTickLabel',''); set(ha,'YTickLabel','')
% Pekka Kumpulainen 20.6.2010 @tut.fi
% Tampere University of Technology / Automation Science and Engineering
if nargin<3; gap = .02; end
if nargin<4 || isempty(marg_h); marg_h = .05; end
if nargin<5; marg_w = .05; end
if numel(gap)==1;
gap = [gap gap];
end
if numel(marg_w)==1;
marg_w = [marg_w marg_w];
end
if numel(marg_h)==1;
marg_h = [marg_h marg_h];
end
axh = (1-sum(marg_h)-(Nh-1)*gap(1))/Nh;
axw = (1-sum(marg_w)-(Nw-1)*gap(2))/Nw;
py = 1-marg_h(2)-axh;
ha = zeros(Nh*Nw,1);
ii = 0;
for ih = 1:Nh
px = marg_w(1);
for ix = 1:Nw
ii = ii+1;
ha(ii) = axes('Units','normalized', ...
'Position',[px py axw axh], ...
'XTickLabel','', ...
'YTickLabel','');
px = px+axw+gap(2);
end
py = py-axh-gap(1);
end
end
function s = local_gain2slope(g)
% s = gain2slope(g)
s = rad2deg(acos(1./sqrt(g.^2+1)));
end
function [r,p] = local_corrcoeff(x,y)
% internal function to evaluate correlation coefficients in order to avoid
% dependence on MATLAB statistics toolbox
%
% Usage: r = corrcoeff(x,y)
if not(size(x) == size(y))
error('corrcoeff: x and y must have same size!')
end
try
[r,p] = corrcoef(x,y);
r = r(2);
p = p(2);
catch
r = cov(x(:),y(:))/std(x(:))/std(y(:));
r = r(2);
n = length(y(:));
df = n-2;
t = r/sqrt((1-r^2)/df);
p = 2*(1-tcdf(t,df));
end
end
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