function output = takanen2013mso(ipsilateral, contralateral, fs, fc, printfigs)
%TAKANEN2013MSO Model of the medial superior olive
% Usage: output = takanen2013mso(ipsilateral, contralateral, fs, fc, printfigs)
%
% Input parameters:
% ipsilateral : The ipsilateral "where" stream output from the
% model of the periphery
% contralateral : The contralateral "where" stream output from the
% model of the periphery
% fs : sampling rate
% printFigs : boolean value that defines whether several figures
% illustrating the processing steps in the model are
% plotted or not. As default, no figures are
% plotted.
%
% Output parameters:
% output : Spatial cues for separate narrow bandwidths
%
% This function models the medial superior olive (MSO) by processing the
% output of the periphery model with the following steps:
%
% 1) Delay and convolution is applied to the contralateral signal and
% the ipsilateral signal is limited and normalized.
%
% 2) Coincidence detection between the ipsilateral and contralateral
% signals.
%
% 3) Weighted and self-weighted moving average filters are applied to
% the outputs of the coincidence detection and contralateral signal,
% respectively, and the output is limited.
%
% See also: takanen2013, takanen2013periphery, weightedaveragefilter
%
% References:
% V. Pulkki and T. Hirvonen. Functional count-comparison model for
% binaural decoding. Acta Acustica united with Acustica, 95(5):883 - 900,
% Sept./Oct. 2009.
%
% M. Takanen, O. Santala, and V. Pulkki. Visualization of functional
% count-comparison-based binaural auditory model output. Manuscript in
% revision, 2013.
%
%
% Url: http://amtoolbox.sourceforge.net/amt-0.9.5/doc/modelstages/takanen2013mso.php
% Copyright (C) 2009-2014 Peter L. Søndergaard.
% This file is part of AMToolbox version 1.0.0
%
% 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: Marko Takanen, Olli Santala, Ville Pulkki
%
% COPYRIGHT (C) 2013 Aalto University
% School of Electrical Engineering
% Department of Signal Processing and Acoustics
% Espoo, Finland
nrows = size(contralateral,1);
t=(0:(nrows-1))./fs;
% If desired, the computations are illustrated at two characteristic
% frequencies
band=[8,10];
%% ------ The contralateral ear input is delayed by 0.4 ms ----------------
contraDelay = round(0.0004*fs);
contralateral = [zeros(contraDelay,size(contralateral,2));...
contralateral(1:size(contralateral,1)-contraDelay,:)];
if(printfigs)
figure(91);
g(1)=subplot(2,1,1);plot(t,ipsilateral(:,band(1)),'-b',t,contralateral(:,band(1)),'--r');
g(2)=subplot(2,1,2);plot(t,ipsilateral(:,band(2)),'-b',t,contralateral(:,band(2)),'--r');
linkaxes(g,'x');title('Ipsi and contra inputs');
end
%% ------ Convolution with the contra response ----------------------------
limit2 = find(fc>=1500,1,'first');
for freqind = 1:length(fc)
if freqind<limit2
n = (0:1:(fs/fc(freqind)))';
f =0.25*(cos(2*pi*(fc(freqind)*n/fs).^.25-pi)+1).^2;
end
convolved = conv(contralateral(:,freqind),f)/sum(f);
contralateral(:,freqind) = convolved(1:nrows);
end
if(printfigs)
figure(92);
g(1)=subplot(2,1,1);plot(t,ipsilateral(:,band(1)),'-b',t,contralateral(:,band(1)),'--r');
g(2)=subplot(2,1,2);plot(t,ipsilateral(:,band(2)),'-b',t,contralateral(:,band(2)),'--r');
linkaxes(g,'x');title('Ipsi and contra after the contra response');
end
%% ------ Limiting of the ipsilateral input -------------------------------
% Limit values are determined for each frequency based on white noise at 30
% dB SPL
load takanen2013_msolimits.mat -mat
limited = ipsilateral./(ones(nrows,1)*limits);
limited(limited>1) =1;
%% ------ Coincidence detection -------------------------------------------
output = (limited.*contralateral);
if(printfigs)
figure(93);
plot(t,output(:,band(2)),t,contralateral(:,band(2)),'-r');
title('Coincidence and contralateral');
end
%% ------ Weighted and self-weighted moving averages of 4 ms --------------
output = weightedaveragefilter(output,contralateral,fs,0.004) ./ (weightedaveragefilter(contralateral,contralateral,fs,0.004)+1e-30);
% tau = 0.004;
% B = 1-exp(-1/(tau*fs));
% A = [1 -exp(-1/(tau*fs))];
% self_weighted = filter(B,A,(contralateral.^3));
% weighted = filter(B,A,(output.*(contralateral.^2)));
% output = weighted./(self_weighted+1e-30);
output(output>1) = 1;