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. J. Acoust. Soc. Am.,
% 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. J. Acoust. Soc. Am., 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-1.4.0/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));
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)));
% 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