function [BinauralRatio, BetterEarSNR, BinauralUnmaskingAdvantage, BESNR_perTFFB, BUAdv_perTFFB] = ...
    vicente2020(FS, fc, MaskerSig, TargetSig, InternalNoise_L, InternalNoise_R, ceiling, HannWindowBE, HannWindowBU, WindowOverlap, weightings)
%VICENTE2020 Compute the effective SNR taking into account BU and BE
%   Usage: [predicted_SNR, BE, BU] = vicente2020(target_in,int_in,fs)
%
%   Input parameters:
%     target_in     : target
%     int_in        : interferer
%     fs            : sampling frequency [Hz]
%
%   Output parameters:
%     predicted_SNR : SNR predicted by the model
%     BE            : better-ear advantage
%     BU            : binaural masking level difference advantage
%
%   VICENTE2020 computes the effective SNR taking into account audibility (audiogram),
%   BU and BE by respective time frames, taking the target and interferer signals 
%   (sampled at fs) as inputs, along with the internal noise parameters computed 
%   using VICENTE2020_internalnoise
%
%   See also: lavandier2022 vicente2020nh vicente2020 prudhomme2020 leclere2015 exp_lavandier2022
%   jelfs2011
%
%   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.
%     
%     B. Collin and M. Lavandier. Binaural speech intelligibility in rooms
%     with variations in spatial location of sources and modulation depth of
%     noise interferers. The Journal of the Acoustical Society of America,
%     134(2):1146--1159, 2013.
%     
%     T. Vicente, M. Lavandier, and J. Buchholz. A binaural model
%     implementing an internal noise to predict the effect of hearing
%     impairment on speech intelligibility in non-stationary noises. The
%     Journal of the Acoustical Society of America, 148(5):3305--3317, 2020.
%     
%
%   Url: http://amtoolbox.org/amt-1.6.0/doc/models/vicente2020.php


%   #StatusDoc: Perfect
%   #StatusCode: Good
%   #Verification: Qualified
%   #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. 


% Compute target info on the long term signal
[TargetLeftSpectrum, TargetRightSpectrum, TargetInterauralPhase] = local_gettargetstats2(TargetSig,fc,FS);

% Compute Better-Ear SNR
L = length(HannWindowBE);
OverlapSample = WindowOverlap*length(HannWindowBE);
BufferedLeft = buffer(MaskerSig(:,1), L, OverlapSample, 'nodelay') .* HannWindowBE;
BufferedRight = buffer(MaskerSig(:,2), L, OverlapSample, 'nodelay') .* HannWindowBE;

weighted_BE_SNR_frames = zeros(size(BufferedLeft,2),1);
BESNR_perTFFB = zeros(size(BufferedLeft,2),length(fc));

for n = 1:size(BufferedLeft,2)
    [weighted_BE_SNR_frames(n),BESNR_perTFFB(n,:)] = ...
        vicente2020_betterearsnrframe(FS, fc, [BufferedLeft(:,n) BufferedRight(:,n)], TargetLeftSpectrum, TargetRightSpectrum, InternalNoise_L, InternalNoise_R, ceiling,weightings);
end

% Compute Binaural unmasking advantage
L = length(HannWindowBU);
OverlapSample = WindowOverlap*length(HannWindowBU);
BufferedLeft = buffer(MaskerSig(:,1), L, OverlapSample, 'nodelay') .* HannWindowBU;
BufferedRight = buffer(MaskerSig(:,2), L, OverlapSample, 'nodelay') .* HannWindowBU;

weighted_BUAdv_frames = zeros(size(BufferedLeft,2),1);
BUAdv_perTFFB = zeros(size(BufferedLeft,2),length(fc));

for n = 1:size(BufferedLeft,2)
    [weighted_BUAdv_frames(n),BUAdv_perTFFB(n,:)] = ...
        vicente2020_buadvantage(FS, fc, [BufferedLeft(:,n) BufferedRight(:,n)], TargetLeftSpectrum, TargetRightSpectrum, TargetInterauralPhase, InternalNoise_L, InternalNoise_R, weightings);
end

BetterEarSNR = mean(weighted_BE_SNR_frames);
BinauralUnmaskingAdvantage = mean(weighted_BUAdv_frames);
BinauralRatio = BetterEarSNR + BinauralUnmaskingAdvantage;
end

function [left_spectrum, right_spectrum, interaural_phase ] = local_gettargetstats2(sig,fc,fs)
%Compute the (left and right) spectrum and interaural phase of the input signal sig (stereo files=2-colum matrix) sampled at fs for
%each (gammatone) frequency band with center frequencies given by fc
%Computations are similar to those used in lavandier2022.m

interaural_phase = zeros(size(fc));
left_spectrum = zeros(size(fc));
right_spectrum = zeros(size(fc));

for n = 1:length(fc)
    % filter target
    sig_left = auditoryfilterbank(sig(:,1),fs,fc(n), 'lavandier2022');    
    sig_right = auditoryfilterbank(sig(:,2),fs,fc(n), 'lavandier2022');   
    [interaural_phase(n), ~] = local_do_xcorr(sig_left,sig_right,fs,fc(n)); % cross-correlate
    % spectrum in dB based on rms of the signals (independent of signal length but not of 0 padding) rms=10*Log10(mean(sig.*sig))
    left_spectrum(n) = 10*log10(mean(sig_left.^2));
    right_spectrum(n) = 10*log10(mean(sig_right.^2));        
end
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