THE AUDITORY MODELING TOOLBOX
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DATA_PAUSCH2022 - - Results from Pausch et al. (2022)
Program code:
function [data,itd] = data_pausch2022(varargin)
%DATA_PAUSCH2022 - Results from Pausch et al. (2022)
%
% Usage: [data,itd] = data_pausch2022(type_stf,type_mod,fig,plot)
%
% Input parameters:
% type_stf : flag that may be used to choose between one of the following:
%
% - hrtf load individual HRTF datasets
% - hartf_front load invidual front HARTF datasets
% - hartf_rear load invidual rear HARTF datasets
%
% Output parameters:
% data : data struct
%
% - (if type_stf=={'hrtf','hartf_front','hartf_rear'})
% Individual spatial transfer functions, with fields id, the participant ID
% and sofa, the sofa file
% - (if weights=='weights')
% Polynomial regression weights (polynomial degree of P=4),
% to be applied on a subset of M=5 individual features
% - if type_mod=={'kuhn','woodworth','woodworth_ext'}:
% vector of (M*P+1) x 1 weights,
% if type_mod=='pausch': matrix of (M*P+1) x 4 weights
% - (if features=='features')
% Individual features as published in Pausch et al. (2022)
%
% itd : itd struct (if type_stf=={'hrtf','hartf_front','hartf_rear'})
% Estimated ITDs for directions in the horizontal plane
%
% - itd_hor: ITDs in s [double]
% - itd_max: maximum ITD in s [s]
% - itd_arg_max_idx: index of the argument of the
% the maximum ITD [double]
% - itd_arg_max_phi: argument of the the maximum ITD
% in deg [double]
% - itd_hor_mean: direction-dependent mean ITDs
% across participants in s [double]
% - itd_hor_std: direction-dependent standard
% deviation of ITDs across participants in s [double]
% - bp_fc_low: lower cut-off frequency (Hz)
% of the bandpass filter applied
% before estimating the ITDs [double]
% - bp_fc_high: upper cut-off frequency (Hz)
% of the bandpass filter applied
% before estimating the ITDs [double]
%
% The type_mod flag may be used to select the ITD model for the ITD
% predictions (only required if weights=='weights'):
%
% 'pausch' hybrid ITD model by Pausch et al. (2022) (default)
% 'kuhn' analytic ITD model by Kuhn (1977)
% 'woodworth' analytic ITD model by Woodworth and Schlosberg (1954)
% 'woodworth_ext' analytic ITD model by Woodworth and Schlosberg (1954), extended
% by Aaronson and Hartmann (2014)
%
% The weights flag may be one of the following:
%
% 'no_weights' do not load polynomial regression weights (default)
% 'weights' load polynomial regression weights
%
% The features flag may be one of the following:
%
% 'no_features' do not the individual features (default)
% 'features' load the individual features
%
% Additional key/value pairs include:
%
% 'bp_fc_low' lower cut-off frequency (Hz) of the bandpass filter
% applied before estimating the ITDs (default: []) [double]
% 'bp_fc_high' upper cut-off frequency (Hz) of the bandpass filter
% applied before estimating the ITDs (default: []) [double]
%
%
% Requirements:
% -------------
%
% 1) SOFA API v0.4.3 or higher from http://sourceforge.net/projects/sofacoustics
% for Matlab (in e.g. thirdparty/SOFA)
%
% 2) Data in auxdata/pausch2022 (downloaded on the fly)
%
%
% References:
% F. Pausch, S. Doma, and J. Fels. Hybrid multi-harmonic model for the
% prediction of interaural time differences in individual behind-the-ear
% hearing-aid-related transfer functions. Acta Acust., 6:34, 2022.
% [1]http ]
%
% G. F. Kuhn. Model for the interaural time differences in the azimuthal
% plane. The Journal of the Acoustical Society of America,
% 62(1):157--167, 1977. [2]arXiv | [3]http ]
%
% R. S. Woodworth and H. Schlosberg. Experimental psychology, Rev. ed.
% Holt, Oxford, England, 1954.
%
% N. L. Aaronson and W. M. Hartmann. Testing, correcting, and extending
% the Woodworth model for interaural time difference. The Journal of the
% Acoustical Society of America, 135(2):817--823, 2014. [4]arXiv |
% [5]http ]
%
% References
%
% 1. https://doi.org/10.1051/aacus/2022020
% 2. http://arxiv.org/abs/https://doi.org/10.1121/1.381498
% 3. https://doi.org/10.1121/1.381498
% 4. http://arxiv.org/abs/https://doi.org/10.1121/1.4861243
% 5. https://doi.org/10.1121/1.4861243
%
%
% Url: http://amtoolbox.org/amt-1.4.0/doc/data/data_pausch2022.php
% #Author: Florian Pausch (2022): Integration in the AMT
% 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.
%% Parse flags and keyvals
definput.import={'pausch2022','amt_cache'};
definput.flags.weights = {'no_weights','weights'};
definput.flags.features = {'no_features','features'};
[flags,kv] = ltfatarghelper({},definput,varargin);
%% Load the polynomials regression weights
if flags.do_weights
switch flags.type_mod
case {'kuhn','woodworth','woodworth_ext'}
if strcmp(flags.type_stf,'hrtf')
[data,flag_mod] = amt_cache('get', 'cache_data_pausch2022_weights_hrtf_mod1_2plus');
else % {hartf_front, hartf_rear}
[data,flag_mod] = amt_cache('get', 'cache_data_pausch2022_weights_hartf_mod1_2plus');
end
otherwise
if strcmp(flags.type_stf,'hrtf')
[data,flag_mod] = amt_cache('get', 'cache_data_pausch2022_weights_hrtf_mod3');
elseif strcmp(flags.type_stf,'hartf_front')
[data,flag_mod] = amt_cache('get', 'cache_data_pausch2022_weights_hartf_front_mod3');
else % hartf_rear
[data,flag_mod] = amt_cache('get', 'cache_data_pausch2022_weights_hartf_rear_mod3');
end
end
if isempty(flag_mod)
if strcmp(flags.type_mod,'kuhn') || strcmp(flags.type_mod,'woodworth') || strcmp(flags.type_mod,'woodworth_ext')
switch flags.type_stf
case 'hrtf'
data = amt_load('pausch2022','weights.mat','weights_hrtf_mod1_2plus');
case {'hartf_front','hartf_rear'}
data = amt_load('pausch2022','weights.mat','weights_hartf_mod1_2plus');
end
else % strcmp(flags.type_mod,'pausch')
switch flags.type_stf
case 'hrtf'
data = amt_load('pausch2022','weights.mat','weights_hrtf_mod3');
case 'hartf_front'
data = amt_load('pausch2022','weights.mat','weights_fhartf_mod3');
case 'hartf_rear'
data = amt_load('pausch2022','weights.mat','weights_rhartf_mod3');
end
end
amt_disp([mfilename,': Loaded polynomial regression weights for model ',flags.type_mod,...
' (type_mod) and ',flags.type_stf,' (type_stf).'])
end
switch flags.type_mod
case {'kuhn','woodworth','woodworth_ext'}
if strcmp(flags.type_stf,'hrtf')
amt_cache('set', 'cache_data_pausch2022_weights_hrtf_mod1_2plus',data,flags.type_mod);
else % {hartf_front, hartf_rear}
amt_cache('set', 'cache_data_pausch2022_weights_hartf_mod1_2plus',data,flags.type_mod);
end
otherwise
if strcmp(flags.type_stf,'hrtf')
amt_cache('set', 'cache_data_pausch2022_weights_hrtf_mod3',data,flags.type_mod);
elseif strcmp(flags.type_stf,'hartf_front')
amt_cache('set', 'cache_data_pausch2022_weights_hartf_front_mod3',data,flags.type_mod);
else % hartf_rear
amt_cache('set', 'cache_data_pausch2022_weights_hartf_rear_mod3',data,flags.type_mod);
end
end
end
%% Neither weights nor features: Load the individual directional datasets as per flag type_stf
if ~flags.do_weights && ~flags.do_features
switch flags.type_stf
case 'hrtf'
[itd,flag_stf,bp_fc_low,bp_fc_high] = amt_cache('get', ...
['cache_data_pausch2022_hrtf_bp_',...
num2str(kv.bp_fc_low),'_',num2str(kv.bp_fc_high),'_Hz'], flags.cachemode);
case 'hartf_front'
[itd,flag_stf,bp_fc_low,bp_fc_high] = amt_cache('get', ...
['cache_data_pausch2022_hartf_front_bp_',...
num2str(kv.bp_fc_low),'_',num2str(kv.bp_fc_high),'_Hz'], flags.cachemode);
otherwise
[itd,flag_stf,bp_fc_low,bp_fc_high] = amt_cache('get', ...
['cache_data_pausch2022_hartf_rear_bp_',...
num2str(kv.bp_fc_low),'_',num2str(kv.bp_fc_high),'_Hz'], flags.cachemode);
end
fileNames = struct();
num_part = 30;
phi_vec = 0:180;
if flags.do_hrtf
for id=1:num_part
fileNames(id).name = ['id',num2str(id,'%02d'),'_HRTF.sofa'];
end
elseif flags.do_hartf_front
for id=1:num_part
fileNames(id).name = ['id',num2str(id,'%02d'),'_HARTF_front.sofa'];
end
elseif flags.do_hartf_rear
for id=1:num_part
fileNames(id).name = ['id',num2str(id,'%02d'),'_HARTF_rear.sofa'];
end
end
amt_disp([mfilename,': Loading ',upper(flags.type_stf),' datasets...'])
data = struct('id',{},'sofa',{});
for idx = 1:length(fileNames)
data(idx).id = idx;
data(idx).sofa = amt_load('pausch2022',fileNames(idx).name);
end
amt_disp([mfilename,': Finished loading of ',upper(flags.type_stf),' datasets.'])
if isempty(flag_stf) || ~isequal(bp_fc_low,kv.bp_fc_low) || ~isequal(bp_fc_high,kv.bp_fc_high)
% Estimate the ITDs for directions in the horizontal plane, and calculate mu+/-std
idx_dir_hor = data(1).sofa.SourcePosition(:,2)==0;
num_dir_hor = sum(idx_dir_hor);
itd = struct('itd_hor',{},'itd_max',{},'itd_arg_max_idx',{},...
'itd_arg_max_phi',{},'itd_hor_mean',{},'itd_hor_std',{});
itd(idx).itd_hor = zeros(num_dir_hor,1);
amt_disp([mfilename,': Estimating ITDs between ',num2str(kv.bp_fc_low),...
'-',num2str(kv.bp_fc_high),' Hz in ',upper(flags.type_stf),' datasets...'])
itd_hor = zeros(num_dir_hor,num_part);
fs = data(1).sofa.Data.SamplingRate;
for idx=1:num_part
data_hor = data(idx).sofa.Data.IR(idx_dir_hor,:,:);
if idx==8 && strcmp(flags.type_stf,'hrtf')
itd(idx).itd_hor = itd_MaxIACCe(data_hor,fs,...
kv.bp_fc_low,kv.bp_fc_high,1);
else
itd(idx).itd_hor = itd_MaxIACCe(data_hor,fs,...
kv.bp_fc_low,kv.bp_fc_high);
end
% store max(itd)
[itd(idx).itd_max,itd(idx).itd_arg_max_idx] = max(itd(idx).itd_hor);
% store arg_max_phi(itd)
itd(idx).itd_arg_max_phi = phi_vec(itd(idx).itd_arg_max_idx);
% temporarily store ITD data for the calculation of mean/std
itd_hor(:,idx) = itd(idx).itd_hor;
end
[itd.itd_hor_mean] = deal(mean(itd_hor,2));
[itd.itd_hor_std] = deal(std(itd_hor,0,2));
amt_disp([mfilename,': Finished ITD estimations between ',num2str(kv.bp_fc_low),...
'-',num2str(kv.bp_fc_high),' Hz in ',upper(flags.type_stf),' datasets.'])
switch flags.type_stf
case 'hrtf'
amt_cache('set', ['cache_data_pausch2022_hrtf_bp_',...
num2str(kv.bp_fc_low),'_',num2str(kv.bp_fc_high),'_Hz'], ...
itd, flags.type_stf, kv.bp_fc_low, kv.bp_fc_high);
case 'hartf_front'
amt_cache('set', ['cache_data_pausch2022_hartf_front_bp_',...
num2str(kv.bp_fc_low),'_',num2str(kv.bp_fc_high),'_Hz'], ...
itd, flags.type_stf, kv.bp_fc_low, kv.bp_fc_high);
otherwise
amt_cache('set', ['cache_data_pausch2022_hartf_rear_bp_',...
num2str(kv.bp_fc_low),'_',num2str(kv.bp_fc_high),'_Hz'], ...
itd, flags.type_stf, kv.bp_fc_low, kv.bp_fc_high);
end
end
end
%% Load the individual features
if flags.do_features
data = amt_cache('get', 'cache_data_pausch2022_features');
if isempty(data)
amt_disp('The features are not available when offline.')
end
% amt_disp([mfilename,': Downloading individual features...'])
% if ~exist(fullfile(amt_auxdatapath,'pausch2022'),'dir'); mkdir(fullfile(amt_auxdatapath,'pausch2022')); end
% websave(fullfile(amt_auxdatapath,'pausch2022','anthropometrics_hearing_aid_features.xlsx'),...
% 'https://publications.rwth-aachen.de/record/844866/files/anthropometrics_hearing_aid_features.xlsx');
% amt_disp([mfilename,': Finished downloading individual features.'])
%
% [~,~,data] = xlsread(fullfile(amt_auxdatapath,'pausch2022','anthropometrics_hearing_aid_features.xlsx'),3);
%
% amt_cache('set', 'cache_data_pausch2022_features', data);
% end
end
end
%% ------------------------------------------------------------------------
% ---- INTERNAL FUNCTIONS ------------------------------------------------
% ------------------------------------------------------------------------
function itd = itd_MaxIACCe(data,fs,bp_fc_low,bp_fc_high,rm_outliers)
%%ITD_MAXIACCE - Function to estimate interaural time differences (ITDs)
% based on the maximum of interaural cross correlation of
% energy envelopes in head-related impulse responses [5].
% Parameters settings as used in [1].
%
% Usage: itd = itd_MaxIACCe(data, phi_vec)
%
% Input parameters (required):
% data : matrix containing the directional impulse responses (IRs),
% size(data) = num_dir_azimuth x 2 x filter length, that is
% (144 x 2 x 256) [double]
% fs : sampling frequency (Hz) [double]
% bp_fc_low : lower cut-off frequency (Hz) of the bandpass filter
% applied before estimating the ITDs (default: 500) [double]
% bp_fc_high : upper cut-off frequency (Hz) of the bandpass filter
% applied before estimating the ITDs (default: 1.5e3) [double]
% rm_outl : flag to remove ITD outliers in HRTF datasets measured
% from participant 8
%
% Note: Parts of the code used in this function were adapted from the
% ITA-Toolbox [6].
%
% [5] Areti Andreopoulou and Brian F. G. Katz, "Identification of perceptually
% relevant methods of inter-aural time difference estimation", The Journal
% of the Acoustical Society of America 142, 588-598 (2017)
% https://doi.org/10.1121/1.4996457
% [6] Berzborn, M., Bomhardt, R., Klein, J., Richter, J. G., & Vorländer, M.
% (2017, March). The ITA-Toolbox: An open source MATLAB toolbox for acoustic
% measurements and signal processing. In 43th Annual German Congress on
% Acoustics, Kiel (Germany) (Vol. 6, pp. 222-225).
% #Author: Florian Pausch, Institute for Hearing Technology and
% Acoustics, RWTH Aachen University
if nargin<5
rm_outliers=false;
end
%% apply low-pass filter on data
filter_order = 10;
f_cutoff = 1.6e3;
h_lp = fdesign.lowpass('n,f3db',filter_order,f_cutoff,fs);
Filter.Hd = design(h_lp,'butter');
data_filt = filter(Filter.Hd,data,3);
%% apply band-pass filter on pre-filtered data
if ~isempty(bp_fc_low) || ~isempty(bp_fc_high)
if isempty(bp_fc_low)
h_bp = fdesign.lowpass('n,f3dB',filter_order,bp_fc_high,fs);
elseif isempty(bp_fc_high)
h_bp = fdesign.bandpass('n,f3dB1,f3dB2',filter_order,bp_fc_low,fs/2,fs);
else
h_bp = fdesign.bandpass('n,f3dB1,f3dB2',filter_order,bp_fc_low,bp_fc_high,fs);
end
Filter.Hd = design(h_bp,'butter');
data_filt_itd = filter(Filter.Hd,data_filt,3);
else
data_filt_itd = data_filt;
amt_disp([mfilename,': No additional band-pass filtering applied before estimating the ITDs.'])
end
%% upsample and interpolate data
data_time_len = size(data,3)/fs;
data_time_vec = (1:size(data,3))./fs;
fac_upsample = 5;
fs_upsampled = fac_upsample*fs;
time_interp = 0:1/fs_upsampled:data_time_len;
data_time_interp = interp1(data_time_vec,permute(data_filt_itd,[3,1,2]),time_interp,'spline');
data_time_interp_e = data_time_interp.^2;
%% estimate ITDs in the horizontal plane
corr_ir = zeros(2*size(data_time_interp_e,1)-1,size(data_time_interp_e,2));
for idx_dir = 1:size(data_time_interp_e,2)
corr_ir(:,idx_dir) = xcorr(data_time_interp_e(:,idx_dir,1),data_time_interp_e(:,idx_dir,2));
end
[~, idx_max] = max(corr_ir);
itd = ((numel(time_interp) - idx_max)/fs_upsampled).';
% remove ITD outliers in participant 8
if rm_outliers
itd(18:21) = NaN;
temp = itd;
x = 1:numel(temp);
itd(18:21) = pchip(x(~isnan(temp)), temp(~isnan(temp)), x(isnan(temp)));
end
end
% function data = local_dataload(instring)
%
% for ii = 1:30
% if numel(num2str(ii)) == 1
% jj = ([num2str(0),num2str(ii)]);
% else
% jj=ii;
% end
% data = amt_load('pausch2022',['id',num2str(jj),'_', instring, '.sofa']);
% end
% data = amt_load('pausch2022',['id',num2str(31),'a_', instring, '.sofa']);
% data = amt_load('pausch2022',['id',num2str(31),'b_', instring, '.sofa']);
% data = amt_load('pausch2022',['id',num2str(31),'c_', instring, '.sofa']);
% data = amt_load('pausch2022',['id',num2str(31),'d_', instring, '.sofa']);
% end
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