function [y_est] = llado2022(ir,stim,fs,NN_pretrained)
%LLADO2022 Neural network localization
%   Usage: [y_est] = llado2022(ir);
%          [y_est] = llado2022(ir, stim);
%          [y_est] = llado2022(ir, stim, fs);
%          [y_est] = llado2022(ir, stim, fs, NN_pretrained);
%
%   Input parameters:
%     ir       : Impulse response according to the following matrix
%                dimensions [direction x time x channel/ear]
%
%   Output parameters:
%     y_est    : Estimated values for perceived direction and position uncertainty.
%
%   LLADO2022(...) is a model for estimating the effect of head-worn
%   devices on frontal horizontal localisation. A neural network (NN) was
%   trained using binaural features of a dummy head wearing different
%   head-worn devices to predict the data from a perceptual test using the
%   same devices. If you want to use your own data, please find in the
%   script 'demo_llado2022' the whole procedure.
%
%   Optional input parameters:
%
%     'stim'             stimulus. If empty, 250 ms of pink noise
%
%     'fs'               (DEFAULT = 48000)
%
%     'NN_pretrained'    if empty, a pretrained NN is used. 
%
%
%   To see details or to train a new NN, please see the script demo_LLADO2022
%
%   See also: exp_llado2022
%
%   Url: http://amtoolbox.org/amt-1.3.0/doc/models/llado2022.php

%   #StatusDoc: Good
%   #StatusCode: Perfect
%   #Verification: Verified
%   #Requirements: MATLAB
%   #Author: Pedro Llado (2022)
%   #Author: Petteri Hyvärinen (2022)
%   #Author: Ville Pulkki (2022)

% 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. 



    %%  DEFAULT OPTIONAL INPUTS
    if nargin<4; load('NN_pretrained.mat'); end % Find an example on how to train the network in 'demo_llado2022'
    if nargin<3; fs=48000; end
    if nargin<2; stim = pinknoise(0.25*fs); end
    
    %% EXTRACT BINAURAL FEATURES
    binauralFeatures = llado2022_binauralFeats(ir,stim,fs);
    
    %% EVALUATE PRETRAINED NETWORK
    y_est = llado2022_evaluateNN(binauralFeatures,NN_pretrained);
end