THE AUDITORY MODELING TOOLBOX
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DATA_BAUMGARTNER2016 - Data from Baumgartner et al. (2016)
Program code:
function data = data_baumgartner2016(varargin)
%DATA_BAUMGARTNER2016 Data from Baumgartner et al. (2016)
% Usage: data = data_baumgartner2016(flag)
%
% Input parameters:
%
% Output parameters:
% data : data structure contains the following fields:
%
% .id listener ID
%
% .S listener-specific sensitivity parameter
%
% .mrs listener-specific task-induced response scatter (derived
% from central lateral response precision in baseline condition)
%
% .Obj DTF data in SOFA Format
%
% .pe_exp experimental local polar RMS error
%
% .qe_exp experimental quadrant error rate
%
% .target experimental target angles
%
% .response experimental response angles
%
% .itemlist experimental item list. Columns denote:
% 1:4 ... azi_target,ele_target,azi_response,ele_response
% 5:8 ... lat_target,pol_target,lat_response,pol_response
% 9 ... F/B-Confusion resolved pol_response
%
% If the 'model'-falg is set the output contains also the following fields
%
% .S listener-specific sensitivity parameter.
%
% .Obj DTF data in SOFA Format.
%
% .pe_exp experimental local polar RMS error in baseline condition.
%
% .qe_exp experimental quadrant error rate in baseline condition.
%
% .target experimental target angles.
%
% .response experimental response angles.
%
% .stim target stimulus.
%
% .fsstim sampling rate of target stimulus.
%
% DATA_BAUMGARTNER2016(flag) returns data from Baumgartner et al. (2016)
% describing a model for sound localization in sagittal planes (SPs)
% on the basis of listener-specific directional transfer functions (DTFs).
%
% DATA_BAUMGARTNER2016 accepts the following flags:
%
% 'baumgartner2014' data of the pool from Baumgartner et al. (2014). This is the default.
% 'Long' 300ms at 50+-5dB SL.
% '10dB' 3ms at 10+-5dB SL.
% '20dB' 3ms at 20+-5dB SL.
% '30dB' 3ms at 30+-5dB SL.
% '40dB' 3ms at 40+-5dB SL.
% '50dB' 3ms at 50+-5dB SL.
% '60dB' 3ms at 60+-5dB SL.
% '70dB' 3ms at 70+-5dB SL.
% 'all' All conditions stated above. Itemlists in cell array.
% 'ConditionNames' To receive cell array with all condition names.
%
% 'model' DTFs, sensitivities and test stimuli necessary for model
% predictions. Sensitivity paramters will be calibrated if
% calibration data does not exist or does not match the
% current setting of baumgartner2016.
%
%
% Requirements:
% -------------
%
% 1) SOFA API from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)
%
% 2) Data in hrtf/baumgartner2014 and hrtf/baumgartner2016
%
% 3) Data in auxdata/baumgartner2016
%
% Examples:
% ---------
%
% To get all listener-specific data of the pool from Baumgartner et al. (2014), use:
%
% data_baumgartner2016;
%
% To get all listener-specific data of the LocaLevel study, use:
%
% data_baumgartner2016('Long');
%
% See also: baumgartner2016, exp_baumgartner2016
%
% Url: http://amtoolbox.sourceforge.net/amt-0.10.0/doc/data/data_baumgartner2016.php
% Copyright (C) 2009-2020 Piotr Majdak and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 0.10.0
%
% 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 : Robert Baumgartner
% TODO: explain Data in description;
%% ------ Check input options --------------------------------------------
definput.import={'baumgartner2016'};
definput.flags.condition = {'baumgartner2014';'Long';'10dB';'20dB';'30dB';'40dB';'50dB';'60dB';'70dB';'all'};
definput.flags.conditionNames = {'';'ConditionNames'};
definput.flags.modeldata = {'','model'};
% Parse input options
[flags,kv] = ltfatarghelper({},definput,varargin);
if flags.do_recalib
flags.cachemode = 'redo';
else
flags.cachemode = 'normal';
end
%% Output Only Condition Names of LocaLevel
if flags.do_ConditionNames
data = definput.flags.condition(2:end-1);
return
end
%% Listener pool (listener-specific SP-DTFs) from Baumgartner et al. (2014)
if flags.do_baumgartner2014
listeners = {'NH12';'NH15';'NH21';'NH22';'NH33';'NH39';'NH41';'NH42';... % NumChan
'NH43';'NH46';'NH53';'NH55';'NH58';'NH62';'NH64';'NH68';...
'NH71';'NH72';'NH14';'NH16';'NH17';'NH18';'NH57';...
};
data=cell2struct(listeners,'id',2);
for ii = 1:length(data)
data(ii).S = 0.5; % default sensitivity
filename = fullfile(SOFAdbPath,'baumgartner2014',...
['ARI_' data(ii).id '_hrtf_M_dtf 256.sofa']);
data(ii).Obj = SOFAload(filename);
data(ii).fs = data(ii).Obj.Data.SamplingRate;
end
data = loadBaselineData(data,kv.latseg,kv.dlat);
%% prior distribution
data = addpriordist(data);
%% Calibration of S
if flags.do_SPLtemAdapt
kv.SPLtem = kv.SPL;
end
%%% Define cache name according to settings for auditory periphery model
cachename = ['calibration_g' num2str(kv.gamma,'%u') ...
'_mrs' num2str(kv.mrsmsp,'%u') ...
'_do' num2str(kv.do,'%u') ...
'_tar' num2str(kv.SPL,'%u') 'dB_tem' num2str(kv.SPLtem,'%u') 'dB_'...
flags.fbank];
if flags.do_gammatone
cachename = [cachename '_' num2str(1/kv.space,'%u') 'bpERB'];
if flags.do_middleear; cachename = [cachename '_middleear']; end
if flags.do_ihc; cachename = [cachename '_ihc']; end
else % zilany
cachename = [cachename '_' flags.fibertypeseparation];
end
if kv.prior > 0
cachename = [cachename '_prior' num2str(kv.prior,'%u')];
end
if kv.tiwin < 0.5
cachename = [cachename '_tiwin' num2str(kv.tiwin*1e3) 'ms'];
end
cachename = [cachename '_mgs' num2str(kv.mgs)];
c = amt_cache('get',cachename,flags.cachemode);
if isempty(c) %|| not(isequal(c.kv,kv))
% reset listener-specific MRS to default
for ii = 1:length(data)
data(ii).mrs = kv.mrsmsp;
end
amt_disp('Calibration procedure started. Please wait!','progress')
data = baumgartner2016_calibration(data,'argimport',flags,kv);
c.data = rmfield(data,{'Obj','fs','itemlist','target','response'}); % reduce filesize
c.kv = kv;
amt_cache('set',cachename,c)
else
for ss = 1:length(data)
for ii = 1:length(c.data)
if strcmp(data(ss).id,c.data(ii).id)
data(ss).S = c.data(ii).S;
data(ss).mrs = c.data(ii).mrs;
if isfield(c.data,'prior')
data(ss).prior = c.data(ii).prior;
else
data(ss).prior = kv.prior;
end
end
end
end
end
else % Loca Level
%% Extract localization data
d = amt_load('baumgartner2016','data.mat');
if flags.do_all
for ll = 1:length(d.subject)
data(ll).condition = d.condition;
data(ll).id = d.subject(ll).id;
data(ll).SL = [50,10:10:70];
data(ll).SPL = data(ll).SL + d.subject(ll).SPLtoSLoffset;
data(ll).SPL(2:end) = data(ll).SPL(2:end) + d.subject(ll).LongToShortOffset;
for C = 1:length(d.condition)
data(ll).itemlist{C} = real(d.subject(ll).expData{C}(:,1:8));
data(ll).pe_exp(C) = localizationerror(data(ll).itemlist{C},'rmsPmedianlocal');
data(ll).qe_exp(C) = localizationerror(data(ll).itemlist{C},'querrMiddlebrooks');
end
end
else
C = find(ismember(d.condition,flags.condition));
for ll = 1:length(d.subject)
data(ll).itemlist = real(d.subject(ll).expData{C}(:,1:8));
data(ll).id = d.subject(ll).id;
if flags.do_Long
data(ll).SL = 50;
data(ll).SPL = data(ll).SL + d.subject(ll).SPLtoSLoffset;
else % Short
data(ll).SL = str2num(flags.condition(1:2));
data(ll).SPL = data(ll).SL + d.subject(ll).SPLtoSLoffset + d.subject(ll).LongToShortOffset;
end
data(ll).pe_exp = localizationerror(data(ll).itemlist,'rmsPmedianlocal');
data(ll).qe_exp = localizationerror(data(ll).itemlist,'querrMiddlebrooks');
end
end
%% Listener-specific SP-DTFs
if flags.do_model
for ii = 1:length(data)
filename = fullfile(SOFAdbPath,'baumgartner2016',...
['ARI_' data(ii).id '_hrtf_M_dtf 256.sofa']);
data(ii).Obj = SOFAload(filename);
data(ii).fs = data(ii).Obj.Data.SamplingRate;
if flags.do_Long
data(ii).stim = d.subject(ii).stim.long;
else % short
data(ii).stim = d.subject(ii).stim.short;
end
data(ii).fsstim = d.subject(ii).stim.fs;
end
%% prior districution
data = addpriordist(data);
%% Calibration of S
if flags.do_SPLtemAdapt
kv.SPLtem = kv.SPL;
end
cachename = ['calibration_localevel_g' num2str(kv.gamma,'%u') ...
'_mrs' num2str(kv.mrsmsp,'%u') ...
'_do' num2str(kv.do,'%u') ...
'_tar' num2str(kv.SPL,'%u') 'dB_tem' num2str(kv.SPLtem,'%u') 'dB_'...
flags.fbank];
if flags.do_gammatone
cachename = [cachename '_' num2str(1/kv.space,'%u') 'bpERB'];
if flags.do_middleear; cachename = [cachename '_middleear']; end
if flags.do_ihc; cachename = [cachename '_ihc']; end
else % zilany
cachename = [cachename '_' flags.fibertypeseparation];
end
if kv.prior > 0
cachename = [cachename '_prior' num2str(kv.prior,'%u')];
end
if kv.tiwin < 0.5
cachename = [cachename '_tiwin' num2str(kv.tiwin*1e3) 'ms'];
end
c = amt_cache('get',cachename,flags.cachemode);
if isempty(c) %|| not(isequal(c.kv,kv))
c.SL = 50; % dB SL of targets
c.SPL = c.SL + [d.subject.SPLtoSLoffset];
for ii = 1:length(data)
c.stim{ii} = d.subject(ii).stim.long;
end
%% Baseline data for calibration
baseline = data_baumgartner2016('Long');
for ll = 1:length(data)
data(ll).pe_exp = localizationerror(baseline(ll).itemlist,'rmsPmedianlocal'); % s(ll).baseline.pe_exp
data(ll).qe_exp = localizationerror(baseline(ll).itemlist,'querrMiddlebrooks'); % s(ll).baseline.qe_exp
data(ll).mrs = localizationerror(data(ll).itemlist,'precLcentral');
for ii = 1:length(kv.latseg)
latresp = baseline(ll).itemlist(:,7);
idlat = latresp <= kv.latseg(ii)+kv.dlat & latresp > kv.latseg(ii)-kv.dlat;
mm2 = baseline(ll).itemlist(idlat,:);
data(ll).target{ii} = mm2(:,6); % polar angle of target
data(ll).response{ii} = mm2(:,8); % polar angle of response
data(ll).Nt{ii} = length(data(ll).target{ii});
end
end
%%
% reset listener-specific MRS to default
for ii = 1:length(data)
data(ii).mrs = kv.mrsmsp;
end
amt_disp('Calibration procedure started. Please wait!','progress')
data = baumgartner2016_calibration(data,'argimport',flags,kv,'c',c);
c.data = rmfield(data,{'Obj','fs','itemlist','target','response'}); % reduce filesize
c.kv = kv;
amt_cache('set',cachename,c)
else
for ii = 1:length(data)
idx = find(ismember({c.data.id},data(ii).id));
data(ii).S = c.data(idx).S;
end
end
end
end
end
function s = loadBaselineData(s,latseg,dlat)
% latseg = 0;%[-20,0,20];
% dlat = 30;%10;
% Experimental baseline data
numchan = data_goupell2010('BB');
methods = data_majdak2010('Learn_M');
spatstrat = data_majdak2013('BB');
ctcL = data_majdak2013ctc('Learn');
for ll = 1:length(s)
s(ll).itemlist = [];
s(ll).itemlist = [s(ll).itemlist ; numchan(ismember({numchan.id},s(ll).id)).mtx];
s(ll).itemlist = [s(ll).itemlist ; methods(ismember({methods.id},s(ll).id)).mtx];
s(ll).itemlist = [s(ll).itemlist ; spatstrat(ismember({spatstrat.id},s(ll).id)).mtx];
s(ll).itemlist = [s(ll).itemlist ; ctcL(ismember({ctcL.id},s(ll).id)).mtx];
s(ll).pe_exp = localizationerror(s(ll).itemlist,'rmsPmedianlocal');
s(ll).qe_exp = localizationerror(s(ll).itemlist,'querrMiddlebrooks');
s(ll).mrs = localizationerror(s(ll).itemlist,'precLcentral');
for ii = 1:length(latseg)
latresp = s(ll).itemlist(:,7);
idlat = latresp <= latseg(ii)+dlat & latresp > latseg(ii)-dlat;
mm2 = s(ll).itemlist(idlat,:);
s(ll).pe_exp_lat(ii) = localizationerror(mm2,'rmsPmedianlocal');
s(ll).qe_exp_lat(ii) = localizationerror(mm2,'querrMiddlebrooks');
s(ll).target{ii} = mm2(:,6); % polar angle of target
s(ll).response{ii} = mm2(:,8); % polar angle of response
s(ll).Ntar{ii} = length(s(ll).target{ii});
end
end
end
function data = addpriordist(data)
dang = 30; % angular width of segments
Tmin = 5; % min. # of targets to estimate prior distribution
edges = -90:dang:270;
for ii = 1:length(data)
try
T = histcounts(data(ii).itemlist(:,6),edges);
R = histcounts(data(ii).itemlist(:,8),edges);
catch
centers = edges(1:end-1)+diff(edges)/2;
T = hist(data(ii).itemlist(:,6),centers);
R = hist(data(ii).itemlist(:,8),centers);
end
T(T<Tmin) = nan;
RvT = R./T;
RvT(isnan(RvT)) = 1;
data(ii).priordist.y = RvT;
data(ii).priordist.x = edges(1:end-1)+dang/2;
end
end
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