THE AUDITORY MODELING TOOLBOX
Go to function
DEMO_BREEBAART2001 - Demo for binaural processingmodel from Breebaart et al. (2001)
Program code:
function demo_breebaart2001(varargin)
%DEMO_BREEBAART2001 Demo for binaural processingmodel from Breebaart et al. (2001)
%
% DEMO_BREEBAART2001(flags) demonstrates how to compute N0S0, N0Spi and
% NpiS0 thresholds for different combinations of the monaural and
% binaural central processor for a center frequency of 1000 or 4000 Hz
% using the model from Breebaart et al. (2014).
%
%
% The N0Spi condition is default. The following combinations for 4000 Hz
% are calculated:
%
% lbr Compute decision from combination of left, binaural and
% right result. If the binaural result is zero, don't use it.
% In this condition lbr is equal to lBr, because the binaural
% result is not zero.
%
% b Compute decision from binaural result only.
%
% lr Compute decision from left and right result.
%
%
% Set flag to the following flags to shows other conditions:
%
% 'N0S0' N0S0 thresholds for differnt combinations of the monaural and
% binaural processor for a center frequency of 4000 Hz.
% The following combinations are calculated:
%
% lbr Compute decision from combination of left, binaural and
% right result. If the binaural result is zero, don't use
% it. In this condition lbr is equal to lr.
%
% lBr Compute decision from combination of left, binaural and
% right result even if the binaural result is zero.
%
% 'NpiS0' NpiS0 thresholds for differnt combinations of the monaural and
% binaural processor for a center frequency of 1000 Hz.
% The following combinations are calculated:
%
% 'lbr' Compute decision from combination of left, binaural and
% right result. If the binaural result is zero, don't use it.
% In this condition lbr is equal to lBr, because the binaural
% result is not zero.
%
% 'b' Compute decision from binaural result only.
%
% 'lr' Compute decision from left and right result.
%
% 'exact' The threshold is computed as a mean value of 6 repetitions per
% bandwidth. To get results faster the computational results are
% stored in cache and can be downloaded.
%
% 'fast' The threshold as the result of one experimental run per bandwidth.
% The number of bandwidth values is half the number of values at the
% 'exact' computation. The fast computation is default.
%
%
% demo_breebaart2001('N0Spi') shows :
%
% demo_breebaart2001('N0Spi');
%
% N0Spi thresholds as a function of the masker bandwidth for a center
% frequency of 4000 Hz and lbr, b and lr conditions in comparison with
% the model results of Breebaart et al. (2001b).
%
%
% demo_breebaart2001('N0S0') shows :
%
% demo_breebaart2001('N0S0');
%
% N0S0 thresholds as a function of the masker bandwidth for a center
% frequency of 4000 Hz and lbr and lBr conditions in comparison with
% experimental results of van de Par and Kohlrausch (1999).
%
%
% demo_breebaart2001('NpiS0') shows :
%
% demo_breebaart2001('NpiS0');
%
% NpiS0 thresholds as a function of the masker bandwidth for a center
% frequency of 4000 Hz and lbr, b and lr conditions in comparison with
% the model results of Breebaart et al. (2001b).
%
% See also: breebaart2001_centralproc breebaart2001
% sig_breebaart2001
%
% References:
% J. Breebaart, S. van de Par, and A. Kohlrausch. Binaural processing
% model based on contralateral inhibition. II. Dependence on spectral
% parameters. J. Acoust. Soc. Am., 110:1089--1104, August 2001.
%
% S. van de Par and A. Kohlrausch. Dependence of binaural masking level
% differences on center frequency, masker bandwidth, and interaural
% parameters. J. Acoust. Soc. Am., 106(4):1940--1947, 1999.
%
%
% Url: http://amtoolbox.org/amt-1.1.0/doc/demos/demo_breebaart2001.php
% Copyright (C) 2009-2021 Piotr Majdak, Clara Hollomey, and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 1.1.0
%
% 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 : Martina Kreuzbichler
definput.import={'amt_cache'};
definput.flags.type = {'N0Spi','N0S0','NpiS0'};
definput.flags.speed = {'fast','exact'};
definput.flags.interface = {'AMT', 'BInit'};
definput.keyvals.directory=tempdir;
% Parse input options
[flags,kv] = ltfatarghelper({},definput,{});
if flags.do_fast
if flags.do_N0Spi
% set experimental paramters
parout = [];
switch flags.interface
case 'AMT'
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',65};
case 'BInit'
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',65, ...
'interface','BInit','directory',kv.directory,'fs',32000};
end
parout = amt_emuexp('expinit',parout,expset);
% set model parameters
% input2 = fs; input3 = tau; input4 = ild;
modelset = {'name','breebaart2001','input1',...
'expsignal', 'input2',32000,'input3',0,'input4',0,...
'outputs',[1 3 4]};
parout = amt_emuexp('modelinit',parout,modelset);
% run model
centerfreq = 4000;
bw = [5 25 100 500 2000];
centralprocstring = {'lbr','b','lr'};
nl = 65;
amt_disp('demo_breebaart2001 will calculate 15 thresholds');
output = amt_cache('get','N0Spi_fast',flags.cachemode);
if isempty(output)
runcounter=1;
% loop for all centralprocstring conditions.
for stringcount = 1:length(centralprocstring)
% set decision parameters
decisionset = {'name','breebaart2001_centralproc',...
'input1','modelout','input2','modelout','input3',...
'modelout','input4',centralprocstring{stringcount}};
parout = amt_emuexp('decisioninit',parout,decisionset);
resultbwvec = zeros(1,1);
resultvec = zeros(5,1);
%loop for all bandwidths
for bwcount = 1:length(bw)
% set signal parameters
%input3 = signallevel; input4 = signalduration;
%input5 = signalphase; input7 = noiselevel;
%input8 = noiseduration; input9 = noisephase;
%input10 = hanning ramp duration; input11 = fs;
signalset = {'name','sig_breebaart2001','input1',...
'inttyp', 'input2',centerfreq,'input3',...
'expvar','input4',0.15,'input5',pi,'input6',...
bw(bwcount),'input7',nl,'input8',0.2,'input9',0,...
'input10',0.025,'input11', 32000};
parout = amt_emuexp('signalinit',parout,signalset);
amt_disp(['Threshold #' num2str(runcounter) ': for bw=' num2str(bw(bwcount)) 'Hz and decision=' centralprocstring{stringcount} '.']);
result = amt_emuexp('run',parout);
resultbwvec(1) = result(1)-nl;
resultvec(bwcount) = resultbwvec;
resultbwvec = zeros(1,1);
amt_disp(sprintf(['Progress for central processor condition ' ...
centralprocstring{stringcount} ':' num2str(round(bwcount/length(bw)*100)) ...
'%% calculated']));
runcounter=runcounter+1;
result_temp =sprintf(['N0Spi4000' centralprocstring{stringcount}]);
output.(result_temp) = resultvec;
end
end
amt_cache('set','N0Spi_fast',output);
end
N0Spi4000lbr = output.N0Spi4000lbr;
N0Spi4000b = output.N0Spi4000b;
N0Spi4000lr = output.N0Spi4000lr;
% plot
%get model data
N0Spimodeldata4000 = data_breebaart2001('fig3','nfc4000');
bw_model = [5 10 25 50 100 250 500 1000 2000 4000];
bw_demo = [5 25 100 500 2000];
figure
plot(bw_demo,N0Spi4000lbr,'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 20])
hold on;
plot(bw_demo,N0Spi4000b,'-o','MarkerSize',10,'Color',[0,0.7,1]);
plot(bw_demo,N0Spi4000lr,'-dc','MarkerSize',10);
plot(bw_model,N0Spimodeldata4000,'-sr','MarkerSize',10,'MarkerFaceColor','r');
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
legend('N0Spi condition lbr','N0Spi condition b',...
'N0Spi condition lr','model data from Breebaart (2001)')
title('N0Spi 4000 Hz thresholds: monaural factor = 0.0003 - fast computation')
elseif flags.do_N0S0
% set experimental paramters
parout = [];
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',90};
parout = amt_emuexp('expinit',parout,expset);
% set model parameters
% input2 = fs; input3 = tau; input4 = ild;
modelset = {'name','breebaart2001','input1',...
'expsignal', 'input2',32000,'input3',0,'input4',0,...
'outputs',[1 3 4]};
parout = amt_emuexp('modelinit',parout,modelset);
% run model
centerfreq = 4000;
bw = [5 25 100 500 2000];
centralprocstring = {'lbr', 'lBr'};
nl = 70;
output = amt_cache('get','N0S0_fast',flags.cachemode);
if isempty(output)
% loop for all centralprocstring conditions.
for stringcount = 1:length(centralprocstring)
% set decision parameters
decisionset = {'name','breebaart2001_centralproc',...
'input1','modelout','input2','modelout','input3',...
'modelout','input4',centralprocstring{stringcount}};
parout = amt_emuexp('decisioninit',parout,decisionset);
resultbwvec = zeros(1,1);
resultvec = zeros(5,1);
%loop for all bandwidths
for bwcount = 1:length(bw)
% set signal parameters
%input3 = signallevel; input4 = signalduration;
%input5 = signalphase; input7 = noiselevel;
%input8 = noiseduration; input9 = noisephase;
%input10 = hanning ramp duration; input11 = fs;
signalset = {'name','sig_breebaart2001','input1',...
'inttyp', 'input2',centerfreq,'input3',...
'expvar','input4',0.15,'input5',0,'input6',...
bw(bwcount),'input7',nl,'input8',0.2,'input9',0,...
'input10',0.025,'input11', 32000};
parout = amt_emuexp('signalinit',parout,signalset);
result = amt_emuexp('run',parout);
resultbwvec(1) = result(1)-nl;
resultvec(bwcount) = resultbwvec;
resultbwvec = zeros(1,1);
amt_disp(sprintf(['Progress for central processor condition ' ...
centralprocstring{stringcount} ': ' ...
num2str(round(bwcount/length(bw)*100)) ...
'%% calculated']));
result_temp =sprintf(['N0S04000' centralprocstring{stringcount}]);
output.(result_temp) = resultvec;
end
end
amt_cache('set','N0S0_fast',output);
end
N0S04000lbr = output.N0S04000lbr;
N0S04000lBr = output.N0S04000lBr;
%plot
N0S0expdata4000 = data_vandepar1999('fig1_N0S0','nfc4000');
bw_model = [5 10 25 50 100 250 500 1000 2000 4000];
bw_demo = [5 25 100 500 2000];
figure
plot(bw_demo,N0S04000lbr,'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -15 10])
hold on;
plot(bw_demo,N0S04000lBr,'-o','MarkerSize',10,'Color',[0,0.7,1]);
plot(bw_model,N0S0expdata4000,'-sg','MarkerSize',10,'MarkerFaceColor','g');
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
legend('N0S0 condition lbr','N0S0 condition lBr','experimental data from van de Par and Kohlrausch (1999)')
title('N0S0 4000 Hz thresholds: monaural factor = 0.0003 - fast computation')
elseif flags.do_NpiS0
% set experimental paramters
parout = [];
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',85};
parout = amt_emuexp('expinit',parout,expset);
% set model parameters
% input2 = fs; input3 = tau; input4 = ild;
modelset = {'name','breebaart2001','input1',...
'expsignal', 'input2',32000,'input3',0.0005,'input4',0,...
'outputs',[1 3 4]};
parout = amt_emuexp('modelinit',parout,modelset);
% run model
centerfreq = 1000;
bw = [5 25 100 500 2000];
centralprocstring = {'lbr','b','lr'};
nl = 70;
output = amt_cache('get','NpiS0_fast',flags.cachemode);
if isempty(output)
% loop for all centralprocstring conditions.
for stringcount = 1:length(centralprocstring)
% set decision parameters
decisionset = {'name','breebaart2001_centralproc',...
'input1','modelout','input2','modelout','input3',...
'modelout','input4',centralprocstring{stringcount}};
parout = amt_emuexp('decisioninit',parout,decisionset);
resultbwvec = zeros(1,1);
resultvec = zeros(5,1);
%loop for all bandwidths
for bwcount = 1:length(bw)
% set signal parameters
%input3 = signallevel; input4 = signalduration;
%input5 = signalphase; input7 = noiselevel;
%input8 = noiseduration; input9 = noisephase;
%input10 = hanning ramp duration; input11 = fs;
signalset = {'name','sig_breebaart2001','input1',...
'inttyp', 'input2',centerfreq,'input3',...
'expvar','input4',0.15,'input5',0,'input6',...
bw(bwcount),'input7',nl,'input8',0.2,'input9',pi,...
'input10',0.025,'input11', 32000};
parout = amt_emuexp('signalinit',parout,signalset);
result = amt_emuexp('run',parout);
resultbwvec(1) = result(1)-nl;
resultvec(bwcount) = resultbwvec;
resultbwvec = zeros(1,1);
amt_disp(sprintf(['Progress for central processor condition ' ...
centralprocstring{stringcount} ': ' ...
num2str(round(bwcount/length(bw)*100)) ...
'%% calculated']));
result_temp =sprintf(['NpiS01000' centralprocstring{stringcount}]);
output.(result_temp) = resultvec;
end
end
amt_cache('set','NpiS0_fast',output);
end
NpiS01000lbr = output.NpiS01000lbr;
NpiS01000b = output.NpiS01000b;
NpiS01000lr = output.NpiS01000lr;
% plot
%get model data
NpiS0modeldata1000 = data_breebaart2001('fig6','nfc1000');
bw_model = [5 10 25 50 100 250 500 1000 2000];
bw_demo = [5 25 100 500 2000];
figure
plot(bw_demo,NpiS01000lbr,'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -30 10])
hold on;
plot(bw_demo,NpiS01000b,'-o','MarkerSize',10,'Color',[0,0.7,1]);
plot(bw_demo,NpiS01000lr,'-dc','MarkerSize',10);
plot(bw_model,NpiS0modeldata1000,'-sr','MarkerSize',10,'MarkerFaceColor','r');
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
legend({'NpiS0 condition lbr','NpiS0 condition b',...
'NpiS0 condition lr','model data from Breebaart (2001)'},...
[290,170,0.1,0.05]);
title('NpiS0 1000 Hz thresholds: monaural factor = 0.0003 - fast computation')
end
elseif flags.do_exact
if flags.do_N0Spi
[N0Spi4000lbr,N0Spi4000b,N0Spi4000lr] = amt_cache('get','N0Spi',flags.cachemode);
if isempty(N0Spi4000lbr)
% do computation
% set experimental paramters
parout = [];
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',65};
parout = amt_emuexp('expinit',parout,expset);
% set model parameters
% input2 = fs; input3 = tau; input4 = ild;
modelset = {'name','breebaart2001','input1',...
'expsignal', 'input2',32000,'input3',0,'input4',0,...
'outputs',[1 3 4]};
parout = amt_emuexp('modelinit',parout,modelset);
% run model
centerfreq = 4000;
bw = [5 10 25 50 100 250 500 1000 2000 4000];
centralprocstring = {'lbr','b','lr'};
nl = 65;
% loop for all centralprocstring conditions.
for stringcount = 1:length(centralprocstring)
% set decision parameters
decisionset = {'name','breebaart2001_centralproc',...
'input1','modelout','input2','modelout','input3',...
'modelout','input4',centralprocstring{stringcount}};
parout = amt_emuexp('decisioninit',parout,decisionset);
%loop for all bandwidths
for bwcount = 1:length(bw)
% set signal parameters
%input3 = signallevel; input4 = signalduration;
%input5 = signalphase; input7 = noiselevel;
%input8 = noiseduration; input9 = noisephase;
%input10 = hanning ramp duration; input11 = fs;
signalset = {'name','sig_breebaart2001','input1',...
'inttyp', 'input2',centerfreq,'input3',...
'expvar','input4',0.3,'input5',pi,'input6',...
bw(bwcount),'input7',nl,'input8',0.4,'input9',0,...
'input10',0.05,'input11', 32000};
parout = amt_emuexp('signalinit',parout,signalset);
% loop for experimental runs
for runcounter = 1:6
result = amt_emuexp('run',parout);
resultbwvec(runcounter) = result(1)-nl;
end
resultvec(bwcount) = mean(resultbwvec);
resultvecstd(bwcount) = std(resultbwvec,1);
resultbwvec = zeros(6,1);
amt_disp(sprintf(['Progress for central processor condition ' ...
centralprocstring{stringcount} ':' num2str(round(bwcount/length(bw)*100)) ...
'%% calculated']));
result_temp =sprintf(['N0Spi4000' centralprocstring{stringcount}]);
output.(result_temp) = [resultvec; resultvecstd]';
end
end
N0Spi4000lbr = output.N0Spi4000lbr;
N0Spi4000b = output.N0Spi4000b;
N0Spi4000lr = output.N0Spi4000lr;
amt_cache('set','N0Spi',N0Spi4000lbr,N0Spi4000b,N0Spi4000lr);
end
% plot
%get model data
N0Spimodeldata4000 = data_breebaart2001('fig3','nfc4000');
bw = [5 10 25 50 100 250 500 1000 2000 4000];
figure
errorbar(bw,N0Spi4000lbr(:,1),N0Spi4000lbr(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 20])
hold on;
errorbar(bw,N0Spi4000b(:,1),N0Spi4000b(:,2),'-o','MarkerSize',10,'Color',[0,0.7,1]);
errorbar(bw,N0Spi4000lr(:,1),N0Spi4000lr(:,2),'-dc','MarkerSize',10);
plot(bw,N0Spimodeldata4000,'-sr','MarkerSize',10,'MarkerFaceColor','r');
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
legend('N0Spi condition lbr','N0Spi condition b',...
'N0Spi condition lr','model data from Breebaart (2001)')
title('N0Spi 4000 Hz thresholds: monaural factor = 0.0003')
elseif flags.do_N0S0
[N0S04000lbr,N0S04000lBr] = amt_cache('get','N0S0',flags.cachemode);
if isempty(N0S04000lbr)
% do computation
% set experimental paramters
parout = [];
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',90};
parout = amt_emuexp('expinit',parout,expset);
% set model parameters
% input2 = fs; input3 = tau; input4 = ild;
modelset = {'name','breebaart2001','input1',...
'expsignal', 'input2',32000,'input3',0,'input4',0,...
'outputs',[1 3 4]};
parout = amt_emuexp('modelinit',parout,modelset);
% run model
centerfreq = 4000;
bw = [5 10 25 50 100 250 500 1000 2000 4000];
centralprocstring = {'lbr', 'lBr'};
nl = 70;
% loop for all centralprocstring conditions.
for stringcount = 1:length(centralprocstring)
% set decision parameters
decisionset = {'name','breebaart2001_centralproc',...
'input1','modelout','input2','modelout','input3',...
'modelout','input4',centralprocstring{stringcount}};
parout = amt_emuexp('decisioninit',parout,decisionset);
%loop for all bandwidths
for bwcount = 1:length(bw)
% set signal parameters
%input3 = signallevel; input4 = signalduration;
%input5 = signalphase; input7 = noiselevel;
%input8 = noiseduration; input9 = noisephase;
%input10 = hanning ramp duration; input11 = fs;
signalset = {'name','sig_breebaart2001','input1',...
'inttyp', 'input2',centerfreq,'input3',...
'expvar','input4',0.3,'input5',0,'input6',...
bw(bwcount),'input7',nl,'input8',0.4,'input9',0,...
'input10',0.05,'input11', 32000};
parout = amt_emuexp('signalinit',parout,signalset);
% loop for experimental runs
for runcounter = 1:6
result = amt_emuexp('run',parout);
resultbwvec(runcounter) = result(1)-nl;
end
resultvec(bwcount) = mean(resultbwvec);
resultvecstd(bwcount) = std(resultbwvec,1);
resultbwvec = zeros(6,1);
amt_disp(sprintf(['Progress for central processor condition ' ...
centralprocstring{stringcount} ': ' ...
num2str(round(bwcount/length(bw)*100)) ...
'%% calculated']));
result_temp =sprintf(['N0S04000' centralprocstring{stringcount}]);
output.(result_temp) = [resultvec; resultvecstd]';
end
end
N0S04000lbr = output.N0S04000lbr;
N0S04000lBr = output.N0S04000lBr;
amt_cache('set','N0S0',N0S04000lbr,N0S04000lBr);
end
% plot
% get experimental data
N0S0expdata4000 = data_vandepar1999('fig1_N0S0','nfc4000');
bw = [5 10 25 50 100 250 500 1000 2000 4000];
figure
errorbar(bw,N0S04000lbr(:,1),N0S04000lbr(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -15 10])
hold on;
errorbar(bw,N0S04000lBr(:,1),N0S04000lBr(:,2),'-o','MarkerSize',10,'Color',[0,0.7,1]);
plot(bw,N0S0expdata4000,'-sg','MarkerSize',10,'MarkerFaceColor','g');
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
legend('N0S0 condition lbr','N0S0 condition lBr','experimental data from van de Par and Kohlrausch (1999)')
title('N0S0 4000 Hz thresholds: monaural factor = 0.0003')
elseif flags.do_NpiS0
[NpiS01000lbr,NpiS01000b,NpiS01000lr] = amt_cache('get','NpiS0',flags.cachemode);
if isempty(NpiS01000lbr)
% do computation
% set experimental paramters
parout = [];
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',85};
parout = amt_emuexp('expinit',parout,expset);
% set model parameters
% input2 = fs; input3 = tau; input4 = ild;
modelset = {'name','breebaart2001','input1',...
'expsignal', 'input2',32000,'input3',0.0005,'input4',0,...
'outputs',[1 3 4]};
parout = amt_emuexp('modelinit',parout,modelset);
% run model
centerfreq = 1000;
bw = [5 10 25 50 100 250 500 1000 2000];
centralprocstring = {'lbr','b','lr'};
nl = 70;
% loop for all centralprocstring conditions.
for stringcount = 1:length(centralprocstring)
% set decision parameters
decisionset = {'name','breebaart2001_centralproc',...
'input1','modelout','input2','modelout','input3',...
'modelout','input4',centralprocstring{stringcount}};
parout = amt_emuexp('decisioninit',parout,decisionset);
%loop for all bandwidths
for bwcount = 1:length(bw)
% set signal parameters
%input3 = signallevel; input4 = signalduration;
%input5 = signalphase; input7 = noiselevel;
%input8 = noiseduration; input9 = noisephase;
%input10 = hanning ramp duration; input11 = fs;
signalset = {'name','sig_breebaart2001','input1',...
'inttyp', 'input2',centerfreq,'input3',...
'expvar','input4',0.3,'input5',0,'input6',...
bw(bwcount),'input7',nl,'input8',0.4,'input9',pi,...
'input10',0.05,'input11', 32000};
parout = amt_emuexp('signalinit',parout,signalset);
% loop for experimental runs
for runcounter = 1:6
result = amt_emuexp('run',parout);
resultbwvec(runcounter) = result(1)-nl;
end
resultvec(bwcount) = mean(resultbwvec);
resultvecstd(bwcount) = std(resultbwvec,1);
resultbwvec = zeros(6,1);
amt_disp(sprintf(['Progress for central processor condition ' ...
centralprocstring{stringcount} ':' num2str(round(bwcount/length(bw)*100)) ...
'%% calculated']));
result_temp =sprintf(['NpiS01000' centralprocstring{stringcount}]);
output.(result_temp) = [resultvec; resultvecstd]';
end
end
NpiS01000lbr = output.NpiS01000lbr;
NpiS01000b = output.NpiS01000b;
NpiS01000lr = output.NpiS01000lr;
amt_cache('set','NpiS0',NpiS01000lbr,NpiS01000b,NpiS01000lr);
end
% plot
%get model data
NpiS0modeldata1000 = data_breebaart2001('fig6','nfc1000');
bw = [5 10 25 50 100 250 500 1000 2000];
figure
errorbar(bw,NpiS01000lbr(:,1),NpiS01000lbr(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -30 10])
hold on;
errorbar(bw,NpiS01000b(:,1),NpiS01000b(:,2),'-o','MarkerSize',10,'Color',[0,0.7,1]);
errorbar(bw,NpiS01000lr(:,1),NpiS01000lr(:,2),'-dc','MarkerSize',10);
plot(bw,NpiS0modeldata1000,'-sr','MarkerSize',10,'MarkerFaceColor','r');
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
legend({'NpiS0 condition lbr','NpiS0 condition b',...
'NpiS0 condition lr','model data from Breebaart (2001)'},...
[290,170,0.1,0.05]);
title('NpiS0 1000 Hz thresholds: monaural factor = 0.0003')
end
end
Build with Bootstrap