function varargout = exp_llado2022(varargin)
%EXP_LLADO2022 Experiments of Llado et al. (2022)
%
% Usage: [] = exp_llado2022(flag)
%
% EXP_LLADO2022(flag) reproduces figures and results of the study
% from LLado et al. (2022).
%
%
% To display Fig.5 use :
%
% exp_llado2022('fig5');
%
% To display Fig.6 use :
%
% exp_llado2022('fig6');
%
%
% See also: llado2022
%
% Url: http://amtoolbox.org/amt-1.3.0/doc/experiments/exp_llado2022.php
% #Author: Pedro Lladó (2021)
% #Author: Petteri Hyvärinen (2021)
% #Author: Ville Pulkki (2021)
% #Author: Clara Hollomey (2022): adaptations for AMT
% 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.
definput.flags.type = {'missingflag', 'fig5', 'fig6'};
[flags,~] = ltfatarghelper({},definput,varargin);
if flags.do_missingflag
flagnames=[sprintf('%s, ',definput.flags.type{2:end-2}),...
sprintf('%s or %s',definput.flags.type{end-1},...
definput.flags.type{end})];
error('%s: You must specify one of the following flags: %s.', ...
upper(mfilename),flagnames);
end
%% Load precomputed binaural estimates
% Load pretrained model
x = amt_load('llado2022', 'NN_pretrained.mat');
NN_pretrained = x.NN_pretrained;
if flags.do_fig5
% Load extracted binaural features itd and ild features
x_input = [NN_pretrained.x_itd;NN_pretrained.x_ild];
%% Training set: all devices but the test device
testDevice = 'F-Gecko';
% Getting the test subset
angle_id = NN_pretrained.angle_id;
nAngles = NN_pretrained.nAngles;
device_id = NN_pretrained.device_id;
nDevices = NN_pretrained.nDevices;
y_output = NN_pretrained.y';
testDevice_id = find(device_id == testDevice);
testDevicePos = nAngles*(testDevice_id-1)+1:nAngles*(testDevice_id);
x_test = x_input(:,testDevicePos);
y_test = y_output(testDevicePos,:);
%% evaluate pretrained model
y_hat = llado2022_evaluatenn(x_test,NN_pretrained);
y_est_dir = y_hat(:,1);
y_est_uncertainty = y_hat(:,2);
if (isvector(y_est_dir) == 1 )
y_est_dir = y_est_dir;
y_est_uncertainty = y_est_uncertainty;
else
y_est_dir = mean(y_est_dir);
y_est_uncertainty = mean(y_est_uncertainty);
end
plot_llado2022(y_est_dir,y_est_uncertainty,angle_id,y_test);
end
if flags.do_fig6
llado2022_weightsanalysis(NN_pretrained);
end