THE AUDITORY MODELING TOOLBOX
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EXP_BREEBAART2001 - Figures from Breebaart et al.(2001a and 2001b)
Program code:
function output=exp_breebaart2001(varargin)
%EXP_BREEBAART2001 Figures from Breebaart et al.(2001a and 2001b)
% Usage: output = exp_breebaart2001(flags)
%
% EXP_BREEBAART2001(flags) reproduces experiments from the
% Breebaart et al (2001a and 2001b) papers.
%
% The following flags can be specified;
%
% 'a_fig2' Reproduce Fig. 2 from Breebaart et al. (2001a):
% Output of the peripheral preprocessor for a 500-Hz tone
% (left panel) and a 4000-Hz tone(right panel) of 100-ms
% duration. The output is caluculated for a filter which
% is nearest to the frequency of the tone.
%
% 'a_fig6' Reproduce Fig. 6 from Breebaart et al. (2001a):
% Left panel: Idealized EI-activity for a wideband diotic
% noise (0-4000 Hz) with an overall level of 70 dB SPL
% (offset = default = 100 dB) for an auditory filter
% which is nearest to the frequency of the tone.
% Right panel: change in the activity pattern of the left
% pannel if a 500-Hz interaurally ou-of-phase signal
% (Spi) is added with a level of 50 dB (offset = default
% = 100 dB). Both signal and master have a duration of 1
% second and the figures show the mean output of the
% first 100 ms.
%
% 'b_fig3' Reproduce Fig. 3 from Breebaart et al. (2001b):
% N0Spi thresholds as a function of the masker bandwidth
% for a constant overall level of the masker. The six
% panels represent center frequencies of 125, 250, 500,
% 1000, 2000 and 4000 Hz, respectivly. The filled squares
% are model predictions from Breebaart et al. (2001b).
% The stars are model predictions calculated with our
% implementation of the Breebaart model for a combination
% of binaural and monaural decisions. The open squares
% are data adapted from van de Par and Kohlrausch (1999).
%
% 'b_fig6' Reproduce Fig. 6 from Breebaart et al. (2001b):
% NpiSo thresholds as a function of masker bandwidth for
% 125-Hz (upper-left panel), 250-Hz (upper-right-panel),
% 500-Hz (lower left panel), and 1000-Hz center frequency
% (lower-right panel). The open sybols are data adapted
% from van de Par and Kohlrausch (1999), the filled
% symbols are model preictions from Breebaart et al.
% (2001b) and the stars are model predicitons calculated
% with out implementation of the Breebaart model for a
% combination of binaural and monaural decisions.
%
% 'fig1_vandepar1999' Reproduce Fig.1 from van de Par and
% Kohlrausch for the N0S0 condition:
% N0S0 thresholds as a fuction of the
% masker bandwidth expressed in
% signal-to-overall-noise power ratio.
% The six panels show data at various
% center frequencies. The circels are
% experimental data and the stars are
% model predicitions calculated with our
% implemntation ot the Breebaart model
% using the monaural decision.
%
% Further, cache flags (see amt_cache) and plot flags can be specified:
%
% 'plot' Plot the output of the experiment. This is the default.
%
% 'no_plot' Don't plot, only return data.
%
% Figure 3 of Breebaart et al. (2001b) can also be calculated using the
% interface of the model initiative. Assuming a model interface
% waiting for binaural signals in a directory DIR, use
% EXP_BREEBAART2001('b_fig3','redo','ModelInitiative','directory',DIR);. The AMT
% will start the experiment, collect the responses from the model server,
% and plot the figure.
%
% See also: data_breebaart2001 data_vandepar1999 breebaart2001_centralproc breebaart2001 sig_breebaart2001
%
% Example:
% ---------
%
% To display Figure 2 of Breebaart et al. (2001a) use :
%
% out = exp_breebaart2001('a_fig2');
%
% To display Figure 6 of Breebaart et al. (2001a) use :
%
% out = exp_breebaart2001('a_fig6');
%
% To display Figure 3 of Breebaart et al. (2001b) use :
%
% out = exp_breebaart2001('b_fig3');
%
% To display Figure 6 of Breebaart et al. (2001b) use :
%
% out = exp_breebaart2001('b_fig6');
%
% To display Figure 1 of van de Par and Kohlrausch (1999) use :
%
% out = exp_breebaart2001('fig1_vandepar1999');
%
%
% References:
% J. Breebaart, S. van de Par, and A. Kohlrausch. Binaural processing
% model based on contralateral inhibition. I. Model structure. J. Acoust.
% Soc. Am., 110:1074--1088, August 2001.
%
% 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-1.5.0/doc/experiments/exp_breebaart2001.php
% #Author: Martina Kreuzbichler (2016)
% 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.
warning off;
%% ------ Check input options --------------------------------------------
definput.import={'amt_cache'};
definput.flags.type = {'missingflag','a_fig2','a_fig6','b_fig3','b_fig6',...
'fig1_vandepar1999'};
definput.flags.plot = {'plot','no_plot'};
definput.flags.interface = {'AMT', 'ModelInitiative'};
definput.keyvals.directory=tempdir;
% Parse input options
[flags,kv] = ltfatarghelper({},definput,varargin);
if flags.do_missingflag
flagnames=[sprintf('%s, ',definput.flags.type{2:end-2}),...
sprintf('%s or %s',definput.flags.type{end-1},definput.flags.type{end})];
error('%s: You must specify one of the following flags: %s.',upper(mfilename),flagnames);
end;
% Breebaart et al. (2001a) fig. 2
if flags.do_a_fig2
testsigadlo = amt_cache('get','a_fig2',flags.cachemode);
if isempty(testsigadlo)
definput.import = {'auditoryfilterbank','ihcenvelope','adaptloop','breebaart2001_eicell'};
definput.importdefaults={'fhigh',8000,'ihc_breebaart2001','adt_breebaart2001'};
[flags,keyvals,~,~,~] = ltfatarghelper({'flow', 'fhigh', ...
'basef'},definput,varargin);
testsig(:,1) = sin(2*pi*(0:(0.1*48000)-1)'*500/48000);
testsig(:,2) = sin(2*pi*(0:(0.1*48000)-1)'*4000/48000);
testsig = scaletodbspl(testsig,70, 100);
testsig = [testsig; zeros(0.1*48000,2)];
for counter = 1:2
testsigoutmiddle(:,counter) = ...
breebaart2001_outmiddlefilter(testsig(:,counter),48000);
[testsigaudfilt(:,:,counter), ~] = ...
auditoryfilterbank(testsigoutmiddle(:,counter),48000,'argimport',flags,keyvals);
testsigihc(:,:,counter) = ihcenvelope(testsigaudfilt(:,:,counter),...
48000,'argimport',flags,keyvals);
testsigadlo(:,:,counter) = adaptloop(testsigihc(:,:,counter),48000,...
'argimport',flags,keyvals);
end
amt_cache('set','a_fig2',testsigadlo)
end
output = testsigadlo;
% Breebaart et al. (2001a) fig. 6
elseif flags.do_a_fig6
[ei_map_n_mean,ei_map_diff_mean] = ...
amt_cache('get','a_fig6',flags.cachemode);
if isempty(ei_map_n_mean)
% do computation
definput.import = {'auditoryfilterbank','ihcenvelope','adaptloop','breebaart2001_eicell'};
definput.importdefaults={'fhigh',8000,'ihc_breebaart2001','adt_breebaart2001'};
[flags,keyvals,flow,fhigh,basef] = ltfatarghelper({'flow', 'fhigh', ...
'basef'},definput,varargin);
fs = 32000;
insig(:,1) = sig_bandpassnoise(2000,fs,1,70,4000);
insig(:,2) = insig(:,1);
insig = scaletodbspl(insig,70, 100);
insig(:,3) = sin(2*pi*(0:(1*fs)-1)'*500/fs);
insig(:,4) = sin(2*pi*(0:(1*fs)-1)'*500/fs-pi);
insig(:,3:4) = scaletodbspl(insig(:,3:4),50, 100);
insig(:,3:4) = insig(:,3:4) + insig(:,1:2);
signalcounter = 1;
for counter = 1:2
sig = insig(:,signalcounter:signalcounter+1);
for earnumber = 1:2
outsig(:,earnumber) = breebaart2001_outmiddlefilter(sig(:,earnumber),fs);
end
[outsig, ~] = auditoryfilterbank(outsig,fs,'argimport',flags,keyvals);
outsig = ihcenvelope(outsig,fs,'argimport',flags,keyvals);
outsig = adaptloop(outsig,fs,'argimport',flags,keyvals);
outsig = squeeze(outsig(:,9,:)); % 9 is filter at ~ 520 Hz
ei_map{counter} = zeros(41,41,length(outsig));
taucount = 1;
ildcount = 1;
for tau = -0.002:0.0001:0.002
for ild = -10:0.5:10
n = round( abs(tau) * fs );
l=outsig(:,1);
r=outsig(:,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*30e-3))];
B=[1-exp(-1/(fs*30e-3))];
y= filtfilt(B,A,x);
z =0.1*log(0.00002*y+1);
ei_map{counter}(taucount,ildcount,:) = z;
ildcount = ildcount + 1;
end
ildcount = 1;
taucount = taucount + 1;
end
signalcounter = signalcounter + 2;
end
ei_map_n_mean = mean(ei_map{1}(:,:,1:3200,1),3); % mean of first 100 ms
ei_map_sn_mean = mean(ei_map{2}(:,:,1:3200,1),3); % mean of first 100 ms
ei_map_diff_mean = ei_map_sn_mean-ei_map_n_mean;
amt_cache('set','a_fig6',ei_map_n_mean,ei_map_diff_mean);
end
output = struct('ei_map_noise',ei_map_n_mean,'ei_map_diff',ei_map_diff_mean);
% Breebaart et al. (2001b) fig. 3
elseif flags.do_b_fig3
[N0Spi125,N0Spi250,N0Spi500,N0Spi1000,N0Spi2000,N0Spi4000] = ...
amt_cache('get','b_fig3',flags.cachemode);
if isempty(N0Spi125)
% do computation
parout = [];
switch flags.interface
case 'AMT'
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',65};
case 'ModelInitiative'
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',65, ...
'interface','ModelInitiative','directory',kv.directory,'fs',32000};
end
parout = amt_emuexp('expinit',parout,expset);
% 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);
decisionset = {'name','breebaart2001_centralproc','input1',...
'modelout','input2','modelout', 'input3','modelout',...
'input4','lbr'};
parout = amt_emuexp('decisioninit',parout,decisionset);
fc = [125 250 500 1000 2000 4000];
bw = [5 10 25 50 100 250 500 1000 2000 4000];
%bwadd = [500 1000 2000 4000];
nl = 65;
% loop for all center freq.
for fccount = 1:length(fc)
%loop for all bandwidths
for bwcount = 1:length(bw)
% calculate only for bandwidths max. twice as large as fc
if bw(bwcount) <= 2*fc(fccount)
%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',fc(fccount),'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 six experimental runs
resultbwvec = zeros(6,1);
for runcounter = 1:6
amt_disp(['Calculating: center frequency = ', num2str(fc(fccount)), ...
' Hz, bandwidth = ' num2str(bw(bwcount)) ' Hz, run #' num2str(runcounter)]);
result = amt_emuexp('run',parout);
resultbwvec(runcounter) = result(1)-nl;
end
resultvec(bwcount) = mean(resultbwvec);
resultvecstd(bwcount) = std(resultbwvec,1);
end
end
N0Spi_temp =sprintf('N0Spi%i',fc(fccount));
output.(N0Spi_temp) = [resultvec; resultvecstd]';
%if fccount <= length(bwadd)
% bw(end+1) = bwadd(fccount);
%end
end
N0Spi125 = output.N0Spi125;
N0Spi250 = output.N0Spi250;
N0Spi500 = output.N0Spi500;
N0Spi1000 = output.N0Spi1000;
N0Spi2000 = output.N0Spi2000;
N0Spi4000 = output.N0Spi4000;
switch flags.interface
case 'AMT'
amt_cache('set','b_fig3',N0Spi125,N0Spi250,N0Spi500,N0Spi1000,...
N0Spi2000,N0Spi4000);
end
else
output = struct('N0Spi125',N0Spi125,'N0Spi250',N0Spi250,...
'N0Spi500',N0Spi500,'N0Spi1000',N0Spi1000,'N0Spi2000',...
N0Spi2000,'N0Spi40000',N0Spi4000);
end
elseif flags.do_b_fig6
[NpiS0125,NpiS0250,NpiS0500,NpiS01000] = ...
amt_cache('get','b_fig6',flags.cachemode);
if isempty(NpiS0125)
% do computation
parout = [];
switch flags.interface
case 'AMT'
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',65};
case 'ModelInitiative'
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',65, ...
'interface','ModelInitiative','directory',kv.directory,'fs',32000};
end
parout = amt_emuexp('expinit',parout,expset);
decisionset = {'name','breebaart2001_centralproc','input1',...
'modelout','input2','modelout', 'input3','modelout',...
'input4','lbr'};
parout = amt_emuexp('decisioninit',parout,decisionset);
fc = [125 250 500 1000];
bw = [5 10 25 50 100 250];
bwadd = [500 1000 2000];
tau = [0.0039 0.002 0.001 0.0005];
nl = 70;
% loop for all center freq.
for fccount = 1:length(fc)
% input2 = fs; input3 = tau; input4 = ild;
modelset = {'name','breebaart2001','input1',...
'expsignal','input2',32000,'input3',tau(fccount),...
'input4',0,'outputs',[1 3 4]};
parout = amt_emuexp('modelinit',parout,modelset);
%loop for all bandwidths
for bwcount = 1:length(bw)
%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',fc(fccount),'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 %i Hz center frequency: ' ...
num2str(round(bwcount/length(bw)*100)) ...
'%% calculated'],fc(fccount)));
end
NpiS0_temp =sprintf('NpiS0%i',fc(fccount));
output.(NpiS0_temp) = [resultvec; resultvecstd]';
if fccount <= length(bwadd)
bw(end+1) = bwadd(fccount);
end
end
NpiS0125 = output.NpiS0125;
NpiS0250 = output.NpiS0250;
NpiS0500 = output.NpiS0500;
NpiS01000 = output.NpiS01000;
switch flags.interface
case 'AMT'
amt_cache('set','b_fig6',NpiS0125,NpiS0250,NpiS0500,NpiS01000);
end
else
output = struct('NpiS0125',NpiS0125,'NpiS0250',NpiS0250,'NpiS0500',...
NpiS0500,'NpiS01000',NpiS01000);
end
elseif flags.do_fig1_vandepar1999
[N0S0125,N0S0250,N0S0500,N0S01000,N0S02000,N0S04000] = ...
amt_cache('get','fig1_vandepar1999',flags.cachemode);
if isempty(N0S0125)
parout = [];
switch flags.interface
case 'AMT'
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',90};
case 'ModelInitiative'
expset = {'intnum',3,'rule',[2 1],'expvarstepstart',8,...
'expvarsteprule',[0.5 2],'stepmin',[1 8],'expvarstart',90, ...
'interface','ModelInitiative','directory',kv.directory,'fs',32000};
end
parout = amt_emuexp('expinit',parout,expset);
% 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);
decisionset = {'name','breebaart2001_centralproc','input1',...
'modelout','input2','modelout', 'input3','modelout',...
'input4','lbr'};
parout = amt_emuexp('decisioninit',parout,decisionset);
fc = [125 250 500 1000 2000 4000];
bw = [5 10 25 50 100 250];
bwadd = [500 1000 2000 4000];
nl = 70;
% loop for all center freq.
for fccount = 1:length(fc)
%loop for all bandwidths
for bwcount = 1:length(bw)
%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',fc(fccount),'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
amt_disp(['Calculating: center frequency = ', num2str(fc(fccount)), ...
' Hz, bandwidth = ' num2str(bw(bwcount)) ' Hz, run #' num2str(runcounter)]);
result = amt_emuexp('run',parout);
resultbwvec(runcounter) = result(1)-nl;
end
resultvec(bwcount) = mean(resultbwvec);
resultvecstd(bwcount) = std(resultbwvec,1);
resultbwvec = zeros(6,1);
end
N0S0_temp =sprintf('N0S0%i',fc(fccount));
output.(N0S0_temp) = [resultvec; resultvecstd]';
if fccount <= length(bwadd)
bw(end+1) = bwadd(fccount);
end
end
N0S0125 = output.N0S0125;
N0S0250 = output.N0S0250;
N0S0500 = output.N0S0500;
N0S01000 = output.N0S01000;
N0S02000 = output.N0S02000;
N0S04000 = output.N0S04000;
switch flags.interface
case 'AMT'
amt_cache('set','fig1_vandepar1999',N0S0125,N0S0250,N0S0500,...
N0S01000,N0S02000,N0S04000);
end
else
output = struct('N0S0125',N0S0125,'N0S0250',N0S0250,...
'N0S0500',N0S0500,'N0S01000',N0S01000,'N0S02000',N0S02000,...
'N0S040000',N0S04000);
end
end
if flags.do_plot
if flags.do_a_fig2
fax = 0:0.2/9599:0.2;
hafig2 = figure;
set(hafig2, 'Position', [400 400 1300 450])
subplot(1,2,1)
plot(fax,output(:,9,1))
axis([0 0.2 -1000 8500])
set(gca,'XTick',[0 0.05 0.1 0.15 0.2]);
xlabel('time [s]')
ylabel('Output [MU')
text('Position',[0.1 7000],'string','500 Hz tone')
subplot(1,2,2)
plot(fax,output(:,25,2))
axis([0 0.2 -1000 8500])
set(gca,'XTick',[0 0.05 0.1 0.15 0.2]);
xlabel('time [s]')
ylabel('Output [MU]')
text('Position',[0.1 7000],'string','4000 Hz tone')
set(gcf,'NextPlot','add');
axes;
h = title('Fig. 2 Output of the peripheral preprocessor');
set(gca,'Visible','off');
set(h,'Visible','on');
elseif flags.do_a_fig6
[x1,y1] = meshgrid(-10:0.5:10, -2:0.1:2);
hfig1=figure;
set(hfig1, 'Position', [100 100 1400 500])
subplot(1,2,1)
surf(x1,y1,ei_map_n_mean)
xlabel('\alpha [dB]')
ylabel('\tau [ms]')
zlabel('Activity [MU]')
axis([-10 10 -2 2 0 0.4])
set(gca,'XTick',[-10 0 10]);
set(gca,'YDir','reverse')
set(gca,'YTick',[-2 0 2]);
set(gca,'ZTick',[0 0.2 0.4]);
hcb1=colorbar;
caxis([0 0.4]);
set(hcb1,'YTick',[0:0.1:0.4]);
grid off
view(-53,28)
subplot(1,2,2)
surf(x1,y1,ei_map_diff_mean)
xlabel('\alpha [dB]')
ylabel('\tau [ms]')
zlabel('Activity [MU]')
axis([-10 10 -2 2 -0.05 0.211])
set(gca,'XTick',[-10 0 10]);
set(gca,'YDir','reverse')
set(gca,'YTick',[-2 0 2]);
set(gca,'ZTick',[0 0.1 0.2]);
hcb2 = colorbar;
caxis([0 0.2]);
set(hcb2,'YTick',[0:0.05:0.2]);
grid off
view(-53,28)
set(gca,'LooseInset',get(gca,'TightInset'))
set(gcf,'NextPlot','add');
axes;
h = title('Fig. 6');
set(gca,'Visible','off');
set(h,'Visible','on');
elseif flags.do_b_fig3
% get experimental data
N0Spiexpdata125 = data_vandepar1999('fig1_N0Spi','nfc125');
N0Spiexpdata250 = data_vandepar1999('fig1_N0Spi','nfc250');
N0Spiexpdata500 = data_vandepar1999('fig1_N0Spi','nfc500');
N0Spiexpdata1000 = data_vandepar1999('fig1_N0Spi','nfc1000');
N0Spiexpdata2000 = data_vandepar1999('fig1_N0Spi','nfc2000');
N0Spiexpdata4000 = data_vandepar1999('fig1_N0Spi','nfc4000');
% get model data
N0Spimodeldata125 = data_breebaart2001('fig3','nfc125');
N0Spimodeldata250 = data_breebaart2001('fig3','nfc250');
N0Spimodeldata500 = data_breebaart2001('fig3','nfc500');
N0Spimodeldata1000 = data_breebaart2001('fig3','nfc1000');
N0Spimodeldata2000 = data_breebaart2001('fig3','nfc2000');
N0Spimodeldata4000 = data_breebaart2001('fig3','nfc4000');
hfig2=figure;
set(hfig2, 'Position', [100 100 1100 700])
bw = [5 10 25 50 100 250];
subplot(3,2,1);
errorbar(bw,N0Spi125(:,1),N0Spi125(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 -5])
hold on;
plot(bw,N0Spimodeldata125,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,N0Spiexpdata125,'-sg','MarkerSize',10)
text('Position',[1000 -10],'string','125 Hz')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
bw = [5 10 25 50 100 250 500];
subplot(3,2,2);
errorbar(bw,N0Spi250(:,1),N0Spi250(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 -5])
hold on;
plot(bw,N0Spimodeldata250,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,N0Spiexpdata250,'-sg','MarkerSize',10)
text('Position',[1000 -10],'string','250 Hz')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
bw = [5 10 25 50 100 250 500 1000];
subplot(3,2,3);
errorbar(bw,N0Spi500(:,1),N0Spi500(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 -5])
hold on;
plot(bw,N0Spimodeldata500,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,N0Spiexpdata500,'-sg','MarkerSize',10)
text('Position',[1000 -10],'string','500 Hz')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
bw = [5 10 25 50 100 250 500 1000 2000];
subplot(3,2,4);
errorbar(bw,N0Spi1000(:,1),N0Spi1000(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 -5])
hold on;
plot(bw,N0Spimodeldata1000,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,N0Spiexpdata1000,'-sg','MarkerSize',10)
text('Position',[1000 -10],'string','1000 Hz')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
bw = [5 10 25 50 100 250 500 1000 2000 4000];
subplot(3,2,5);
errorbar(bw,N0Spi2000(:,1),N0Spi2000(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 -5])
hold on;
plot(bw,N0Spimodeldata2000,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,N0Spiexpdata2000,'-sg','MarkerSize',10)
text('Position',[1000 -10],'string','2000 Hz')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
subplot(3,2,6);
errorbar(bw,N0Spi4000(:,1),N0Spi4000(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 5])
hold on;
plot(bw,N0Spimodeldata4000,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,N0Spiexpdata4000,'-sg','MarkerSize',10)
text('Position',[1000 0],'string','4000 Hz')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
legend({'modeled data SSL = 65 dB, NL 65 dB - monaural factor = 0.0003',...
'model data from Breebaart (2001)',...
'experimental data from van de Par and Kohlrausch (1999)'},...
'Position' ,[0.75,0.943,0.1,0.04]);
set(gcf,'NextPlot','add');
axes;
h = title('Fig. 3 N0Spi Thresholds');
set(gca,'Visible','off');
set(h,'Visible','on');
elseif flags.do_b_fig6
% get experimental data
NpiS0expdata125 = data_vandepar1999('fig1_NpiS0','nfc125');
NpiS0expdata250 = data_vandepar1999('fig1_NpiS0','nfc250');
NpiS0expdata500 = data_vandepar1999('fig1_NpiS0','nfc500');
NpiS0expdata1000 = data_vandepar1999('fig1_NpiS0','nfc1000');
% get model data
NpiS0modeldata125 = data_breebaart2001('fig6','nfc125');
NpiS0modeldata250 = data_breebaart2001('fig6','nfc250');
NpiS0modeldata500 = data_breebaart2001('fig6','nfc500');
NpiS0modeldata1000 = data_breebaart2001('fig6','nfc1000');
hfig3=figure;
set(hfig3, 'Position', [100 100 1100 700])
bw = [5 10 25 50 100 250];
subplot(2,2,1);
errorbar(bw,NpiS0125(:,1),NpiS0125(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 0])
hold on;
plot(bw,NpiS0modeldata125,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,NpiS0expdata125,'-sg','MarkerSize',10)
text('Position',[5 -30],'string','125 Hz; 3.9 ms')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
bw = [5 10 25 50 100 250 500];
subplot(2,2,2);
errorbar(bw,NpiS0250(:,1),NpiS0250(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 -5])
hold on;
plot(bw,NpiS0modeldata250,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,NpiS0expdata250,'-sg','MarkerSize',10)
text('Position',[5 -30],'string','250 Hz; 2 ms')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
bw = [5 10 25 50 100 250 500 1000];
subplot(2,2,3);
errorbar(bw,NpiS0500(:,1),NpiS0500(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 -5])
hold on;
plot(bw,NpiS0modeldata500,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,NpiS0expdata500,'-sg','MarkerSize',10)
text('Position',[5 -30],'string','500 Hz; 1 ms')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N');
bw = [5 10 25 50 100 250 500 1000 2000];
subplot(2,2,4);
errorbar(bw,NpiS01000(:,1),NpiS01000(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[5 10 25 50 100 250 1000 4000]);
axis([4 5000 -35 -5])
hold on;
plot(bw,NpiS0modeldata1000,'-sr','MarkerSize',10,'MarkerFaceColor','r');
plot(bw,NpiS0expdata1000,'-sg','MarkerSize',10)
text('Position',[5 -30],'string','1000 Hz; 0.5 ms')
xlabel('Masker Bandwidth [Hz]');
ylabel('Threshold S/N');
legend({'modeled data SSL = 85 dB, NL 70 dB - monaural factor = 0.0003',...
'model data from Breebaart (2001)',...
'experimental data from van de Par and Kohlrausch (1999)'},...
'Position' ,[0.75,0.943,0.1,0.04]);
set(gcf,'NextPlot','add');
axes;
h = title('Fig. 6 NpiS0 Thresholds');
set(gca,'Visible','off');
set(h,'Visible','on');
elseif flags.do_fig1_vandepar1999
%get experimental data
N0S0expdata125 = data_vandepar1999('fig1_N0S0','nfc125');
N0S0expdata250 = data_vandepar1999('fig1_N0S0','nfc250');
N0S0expdata500 = data_vandepar1999('fig1_N0S0','nfc500');
N0S0expdata1000 = data_vandepar1999('fig1_N0S0','nfc1000');
N0S0expdata2000 = data_vandepar1999('fig1_N0S0','nfc2000');
N0S0expdata4000 = data_vandepar1999('fig1_N0S0','nfc4000');
hfig1=figure;
set(hfig1, 'Position', [100 100 700 700])
bw = [5 10 25 50 100 250];
s1 = subplot(3,2,1);
p1 = get(s1,'pos');
errorbar(bw,N0S0125(:,1),N0S0125(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
axis([4 5000 -30 10])
hold on;
plot(bw,N0S0expdata125,'-og')
text('Position',[1000 5],'string','125 Hz')
ylabel('Threshold S/N [dB]');
bw = [5 10 25 50 100 250 500];
s2 = subplot(3,2,2);
p2 = get(s2,'pos');
p2(1) = p1(1) + p1(3);
set(s2, 'pos', p2);
errorbar(bw,N0S0250(:,1),N0S0250(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
axis([4 5000 -30 10])
set(gca,'XTick',[]);
set(gca,'YTick',[]);
hold on;
plot(bw,N0S0expdata250,'-og')
text('Position',[1000 5],'string','250 Hz')
bw = [5 10 25 50 100 250 500 1000];
s3 = subplot(3,2,3);
p3 = get(s3,'pos');
p3(2) = p1(2) - p1(4) + 0.005;
set(s3, 'pos', p3);
errorbar(bw,N0S0500(:,1),N0S0500(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
axis([1 10000 -30 10])
set(gca,'XTick',[]);
set(gca,'YTick',[-30 -20 -10 0]);
hold on;
plot(bw,N0S0expdata500,'-og')
text('Position',[1000 5],'string','500 Hz')
ylabel('Threshold S/N [dB]');
bw = [5 10 25 50 100 250 500 1000 2000];
s4 = subplot(3,2,4);
p4 = get(s4,'pos');
p4(1) = p3(1) + p3(3);
p4(2) = p3(2);
set(s4, 'pos', p4);
errorbar(bw,N0S01000(:,1),N0S01000(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
axis([1 10000 -30 10])
set(gca,'XTick',[]);
set(gca,'YTick',[]);
hold on;
plot(bw,N0S0expdata1000,'-og')
text('Position',[1000 5],'string','1 kHz')
xlabel('Bandwidth [Hz]');
bw = [5 10 25 50 100 250 500 1000 2000 4000];
s5 = subplot(3,2,5);
p5 = get(s5,'pos');
p5(2) = p3(2) - p3(4) + 0.005;
set(s5, 'pos', p5);
errorbar(bw,N0S02000(:,1),N0S02000(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[1 10 100 1000 10000]);
axis([1 10000 -30 10])
set(gca,'YTick',[-30 -20 -10 0]);
hold on;
plot(bw,N0S0expdata2000,'-og')
text('Position',[1000 5],'string','2 kHz')
xlabel('Bandwidth [Hz]');
ylabel('Threshold S/N [dB]');
s6 = subplot(3,2,6);
p6 = get(s6,'pos');
p6(1) = p5(1) + p5(3);
p6(2) = p5(2);
set(s6, 'pos', p6);
errorbar(bw,N0S04000(:,1),N0S04000(:,2),'-x','MarkerSize',10);
set(gca,'xscal','log')
set(gca,'XTick',[ 10 100 1000 10000]);
axis([1 10000 -30 10])
set(gca,'YTick',[]);
hold on;
plot(bw,N0S0expdata4000,'-og')
text('Position',[1000 5],'string','4 kHz')
xlabel('Bandwidth [Hz]');
legend({'modeled data SSL = 90 dB, NL 70 dB - monaural factor = 0.0003',...
'experimental data from van de Par and Kohlrausch (1999)'},...
'Position' ,[p2(1)-p2(3)*0.2,0.15,0.1,0.05]);
set(gcf,'NextPlot','add');
axes;
h = title('Fig. 1 N_0S_0 Thresholds');
set(gca,'Visible','off');
set(h,'Visible','on');
end
end
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