function scalib = baumgartner2014_calibration(s,kv,TolX)
%baumgartner2014_calibration - Calibration of the model
% Usage: scalib = baumgartner2014_calibration(s)
%
% Input parameters:
% s : strucure containing subject's data. It must include the
% fields Obj, pe_exp, and qe_exp, representing the
% listener's HRTF as SOFA object, the baseline local
% polar RMS error, and the baseline quadrant error rate,
% respectively. Optionally, the structure can include the
% field target, a cell array representing the polar target
% angles for each lateral segment.
%
% Output parameters:
% scalib : strucure containing subject's data with calibrated S
%
% BAUMGARTNER2014_CALIBRATION returns listener data with
% listener-specific sensitivity thresholds calibrated by joint
% optimization of PE and QE to minimize mismatch between experimental
% and predicted results.
%
% BAUMGARTNER2014_CALIBRATION accepts the following optional parameters:
%
% 'kv' Key-value pairs according to baumgartner2014
%
% 'TolX' Optimization tolerance of listener-specific sensitivity.
% Default is 1e-3.
%
% See also: baumgartner2014
%
% Url: http://amtoolbox.sourceforge.net/amt-0.10.0/doc/modelstages/baumgartner2014_calibration.php
% Copyright (C) 2009-2020 Piotr Majdak and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 1.0.0
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR : Robert Baumgartner
if not(exist('kv','var'))
definput.import={'baumgartner2014'};
[~,kv]=ltfatarghelper({},definput,{});
end
if not(isfield(kv,'latseg'))
kv.latseg = [-20,0,20];
end
if not(isfield(s,'target'))
amt_disp('Calibration accuracy could be enhanced by providing the target polar-angles.');
end
if not(exist('TolX','var'))
TolX = 0.001;
end
scalib = s;
for ss = 1:length(s)
scalib(ss).S = fminsearch(@(S) local_evaldist(s(ss),S,kv),s(ss).S,...
optimset('MaxIter',50,'TolX',TolX)...
);
% [~,scalib(ss).qe_pred,scalib(ss).pe_pred] = evaldist(s(ss),S,kv);
amt_disp([num2str(ss,'%2.0u') ' of ' num2str(length(s),'%2.0u') ' calibrated.']);
end
end
function [distmetric,qeM,peM] = local_evaldist(s,S,kv)
if S <= 0
distmetric = Inf;
return
end
%% LocaMo
qeM = zeros(length(s),1);
peM = zeros(length(s),1);
for ll = 1:length(s)
for ii = 1:length(kv.latseg)
s(ll).p{ii} = 0; % init
[s(ll).p{ii},respangs,polang] = baumgartner2014(...
s(ll).Obj,s(ll).Obj,s(ll).Obj.Data.SamplingRate,...
'S',S,'lat',kv.latseg(ii),...
'mrsmsp',kv.mrsmsp,'gamma',kv.gamma,'do',kv.do);
if isfield(s,'target')
[ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , polang , respangs , s(ll).target{ii});
latweight = length(s(ll).target{ii})/length(cat(1,s(ll).target{:}));
qeM(ll) = qeM(ll) + qe(ii)*latweight;
peM(ll) = peM(ll) + pe(ii)*latweight;
else
[ qe(ii),pe(ii) ] = baumgartner2014_pmv2ppp( ...
s(ll).p{ii} , polang , respangs);
qeM(ll) = qeM(ll) + qe(ii)*1/length(kv.latseg);
peM(ll) = peM(ll) + pe(ii)*1/length(kv.latseg);
end
end
dQE(ll) = s(ll).qe_exp - qeM(ll);
dPE(ll) = s(ll).pe_exp - peM(ll);
end
[qe_chance,pe_chance] = baumgartner2014_pmv2ppp(ones(49,44));
distmetric = (dQE/qe_chance).^2 + (dPE/pe_chance).^2; % Joint distance metric of QE and PE (standardized scatter)
end