function [twoears_benefit, weighted_bmld, weighted_better_ear] = lavandier2022(target_in,int_in,fs)
%LAVANDIER2022 Compute the binaural 'effective' target-to-interferer ratio
%   Usage: [twoears_benefit, weighted_bmld, weighted_better_ear] = lavandier2022(target_in,int_in,fs)
%
%   Input parameters:
%     target_in       : target
%     int_in          : interferer
%     fs              : sampling frequency [Hz]
%
%   Output parameters:
%     twoears_benefit       : effective target to interferer ratio
%     weighted_bmld         : weighted binaural masking level difference
%     weighted_better_ear   : weighted better ear advantage
%
%   LAVANDIER2022 computes the binaural 'effective' target-to-interferer ratio. 
%   target_in and int_in are signals produced at the ears: stereo files 
%   (2-column matrices) of the same sampling frequency fs
%
%   See also: lavandier2022 vicente2020nh vicente2020 prudhomme2020 leclere2015 jelfs2011 exp_lavandier2022
%
%   References:
%     M. Lavandier, T. Vicente, and L. Prud'homme. A series of snr-based
%     speech intelligibility models in the auditory modeling toolbox. Acta
%     Acustica, 2022.
%     
%     M. Lavandier, S. Jelfs, J. Culling, A. Watkins, A. Raimond, and
%     S. Makin. Binaural prediction of speech intelligibility in reverberant
%     rooms with multiple noise sources. The Journal of the Acoustical
%     Society of America, 131(1):218--231, 2012.
%     
%     M. Lavandier and J. Culling. Speech segregation in rooms: Monaural,
%     binaural and interacting effects of reverberation on target and
%     interferer. The Journal of the Acoustical Society of America,
%     123(4):2237--2248, 2008.
%     
%     S. Jelfs, J. Culling, and M. Lavandier. Revision and validation of a
%     binaural model for speech intelligibility in noise. Hearing Research,
%     2011.
%     
%
%   Url: http://amtoolbox.org/amt-1.6.0/doc/models/lavandier2022.php


%   #StatusDoc: Perfect
%   #StatusCode: Good
%   #Verification: Verified
%   #Requirements: MATLAB
%   #Author: Matthieu Lavandier
%   #Author: Clara Hollomey (2021)

% 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. 



nerbs = 1:0.5:round(f2erbrate(fs/2,'moore1983'));
fc = zeros(size(nerbs));
bmld_prediction = zeros(size(nerbs));
better_ear_prediction = zeros(size(nerbs));

for n = 1:length(nerbs)
    % get filter cf
    fc(n) = round(erbrate2f(nerbs(n),'moore1983'));    
    % filter target and interferer separately
    targ_left = auditoryfilterbank(target_in(:,1),fs,fc(n), 'lavandier2022');    
    targ_right = auditoryfilterbank(target_in(:,2),fs,fc(n), 'lavandier2022');   
    int_left = auditoryfilterbank(int_in(:,1),fs,fc(n), 'lavandier2022');       
    int_right = auditoryfilterbank(int_in(:,2),fs,fc(n), 'lavandier2022');  
    % BMLD
    [int_phase, int_coherence] = local_do_xcorr(int_left,int_right,fs,fc(n)); % cross-correlate
    [target_phase] = local_do_xcorr(targ_left,targ_right,fs,fc(n));    
    bmld_prediction(n) = bmld(int_coherence,target_phase,int_phase,fc(n));    
    % better-ear SNR in dB based on rms of the signals (independent of
    % signal length but not of 0 padding) rms=10*Log10(mean(sig.*sig))
    left_SNR = 10*log10(mean(targ_left.^2)/mean(int_left.^2));
    right_SNR = 10*log10(mean(targ_right.^2)/mean(int_right.^2));
    better_ear_prediction(n) = max(left_SNR,right_SNR);   
end

%integration accross frequency using SII weightings
weightings = f2siiweightings(fc);
weighted_bmld = sum(bmld_prediction.*weightings');
weighted_better_ear = sum(better_ear_prediction.*weightings');

twoears_benefit = weighted_better_ear + weighted_bmld;

end

function [phase, coherence] = local_do_xcorr(left, right, fs, fc)
    [iacc, lags] = xcorr(left,right,round(fs/(fc*2)),'coeff'); %round(fs/(fc*2)) is for conformity with Durlach's 1972 formulation which allows time delays up to 
                                                               %+/- half the period of the channel centre frequency.
    [coherence, delay_samp] = max(iacc);
    phase = fc*2*pi*lags(delay_samp)/fs;
end