function z = eicell(insig,fs,tau,ild,varargin)
%EICELL Excitation-inhibition cell computation for the Breebaart model
% Usage: y = eicell(insig,fs,tau,ild)
%
% Input parameters:
% insig : input signal, must be an [n by 2] matrix
% fs : sampling rate of input signal
% tau : characteristic delay in seconds (positive: left is leading)
% ild : characteristic ILD in dB (positive: left is louder)
%
% Output parameters:
% y : EI-type cell output as a function of time
%
% EICELL(insig,fs,tau,ild) compute the excitation-inhibition model on
% the input signal insig. The cell to be modelled responds to a delay
% tau (measured in seconds) and interaural-level difference ild*
% measured in dB.
%
% EICELL takes the following optional parameters:
%
% 'tc',tc Temporal smoothing constant. Default value is 30e-3.
%
% 'rc_a',rc_a Parameter a for dynamic range compression.
% Default value is a=.1.
%
% 'rc_b',rc_b Parameter b for dynamic range compression.
% Default value is b=0.00002.
%
% 'ptau',ptau Time constant for p(tau) function. Default value is 2.2e-3.
%
% See also: breebaart2001preproc
%
% Url: http://amtoolbox.sourceforge.net/data/amt-test/htdocs/amt-0.9.8/doc/general/eicell.php
% Copyright (C) 2009-2015 Piotr Majdak and Peter L. Søndergaard.
% This file is part of AMToolbox version 0.9.8
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% Author: Jeroen Breebaart and Peter L. Søndergaard
if nargin<4
error('%s: Too few input arguments.',upper(mfilename));
end;
definput.import={'eicell'};
[flags,kv]=ltfatarghelper({},definput,varargin);
% apply characteristic delay:
n = round( abs(tau) * fs );
l=insig(:,1);
r=insig(:,2);
if tau > 0,
l = [zeros(n,1) ; l(1:end-n)];
else
r = [zeros(n,1) ; r(1:end-n)];
end
% apply characteristic ILD:
l=gaindb(l, ild/2);
r=gaindb(r,-ild/2);
% compute instanteneous EI output:
x = (l - r).^2;
% temporal smoothing:
A=[1 -exp(-1/(fs*kv.tc))];
B=[1-exp(-1/(fs*kv.tc)) ];
y= filtfilt(B,A,x);% / ( (1-exp(-1/(fs*tc)))/2 );
% compressive I/O: Scale signal by 200. This approximately
% results in JNDs of 1 in the output
z = exp(-abs(tau)/kv.ptau) * kv.rc_a * log(kv.rc_b * y + 1);
% exp(-abs(tau)/0.0022) as in Larsen 2010
% 10^(-abs(tau)/0.005) as in Breebaart 2001a
% log10(kv.rc_b * y + 1) as in Davidson 2009