function data = data_middlebrooks1999
%DATA_MIDDLEBROOKS1999 Statistics about non-individualized HRTFs
% Usage: data = data_middlebrooks1999
%
% DATA_MIDDLEBROOKS1999 returns statistics summary from Fig. 13
% (Middlebrooks, 1999b) showing the effect of non-individualized HRTFs.
%
% Statistics of those parameters are stored as .mean and .quantiles*
% representing the arithmetic mean and {0,5,25,50,75,95,100} quantiles,
% respectively.
%
% The data struct comprises the following fields:
%
% 'le_own' local lateral RMS error (LE) when localizing with own
% HRTFs
% 'le_other' LE when localizing with others' HRTFs
% 'lb_own' magnitude of lateral bias (LB) when localizing with own
% HRTFs; upper-rear quadrant excluded from analysis
% 'lb_other' LB when localizing with others' HRTFs
% 'qe_own' quadrant error rate (QE) when localizing with own
% HRTFs
% 'qe_other' QE when localizing with others' HRTFs
% 'pe_own' local polar RMS error (PE) when localizing with own
% HRTFs
% 'pe_other' PE when localizing with others' HRTFs
% 'pb_own' magnitude of polar bias (PB) when localizing with own
% HRTFs; upper-rear quadrant excluded from analysis
% 'pb_other' PB when localizing with others' HRTFs
%
%
% References:
% J. C. Middlebrooks. Virtual localization improved by scaling
% nonindividualized external-ear transfer functions in frequency. J.
% Acoust. Soc. Am., 106:1493--1510, 1999.
%
%
% Url: http://amtoolbox.org/amt-1.3.0/doc/data/data_middlebrooks1999.php
% #Author: Robert Baumgartner
% #Author: Roberto Barumerli
% 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.
% Quantiles: {0,5,25,50,75,95,100}%
% LE
data.le_own.quantiles = [10.93, 12.12, 13.07, 14.19, 15.61, 17.06, 19.66];
data.le_own.mean = 14.54;
data.le_other.quantiles = [12.25, 14.70, 16.06, 16.88, 19.46, 22.55, 25.45];
data.le_other.mean = 17.04;
% LB
data.lb_own.quantiles = [-0.01, 0.15, 2.43, 3.80, 5.81, 7.11, 8.39];
data.lb_own.mean = 3.85;
data.lb_other.quantiles = [0.12, 0.42, 1.71, 4.23, 11.82, 13.39, 14.17];
data.lb_other.mean = 6.01;
% QE
data.qe_own.quantiles = [0,0,1,3.5,5,13,17];
data.qe_own.mean = 4.5;
data.qe_other.quantiles = [7.5,8,12.5,19,27.5,38,39];
data.qe_other.mean = 21;
% PE
data.pe_own.quantiles = [21,23,25,27,30,34,36];
data.pe_own.mean = 28;
data.pe_other.quantiles = [23,33,38,42,48,54,55];
data.pe_other.mean = 42.5;
% EB
data.pb_own.quantiles = [1,2.5,6,10,13,20,25.5];
data.pb_own.mean = 10;
data.pb_other.quantiles = [0.5,2,7,18,29,42,52.5];
data.pb_other.mean = 19;
end