THE AUDITORY MODELING TOOLBOX
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EXP_KELVASA2015 - Figures from Kelvasa and Dietz (2015)
Program code:
function [dataOut] = exp_kelvasa2015(varargin)
%EXP_KELVASA2015 Figures from Kelvasa and Dietz (2015)
%
% EXP_KELVASA2015(fig) computes data to produce corresponding figure
% number from the Kelvasa and Dietz 2015 paper.
%
% Usage:
% [dataOut] = exp_kelvasa2015('fig8a')
% [dataOut] = exp_kelvasa2015('fig8a','identifier','BTE','HRTFchannels',[3,4]);
% [dataOut] = exp_kelvasa2015('fig8a',varargin);
%
% The following flags can be specified;
%
% 'redo' Recomputes data for specified figure
%
% 'plot' Plot the output of the experiment. This is the default.
%
% 'no_plot' Don't plot, only return data.
%
% 'fig5' Reproduce Fig. 5.
%
% 'fig6' Reproduce Fig. 6.
%
% 'fig8a' Reproduce Fig. 8a.
%
% 'fig8b' Reproduce Fig. 8b.
%
% 'fig9a' Reproduce Fig. 9a.
%
% 'fig10' Reproduce Fig. 10.
%
% 'fig12' Reproduce Fig. 12.
%
% Examples:
% ---------
%
% To display Fig. 5 use :
%
% exp_kelvasa2015('fig5');
%
% To display Fig. 6 use :
%
% exp_kelvasa2015('fig6');
%
% To display Fig. 8a use :
%
% exp_kelvasa2015('fig8a');
%
% To display Fig. 8b use :
%
% exp_kelvasa2015('fig8b');
%
% To display Fig. 9a use :
%
% exp_kelvasa2015('fig9a');
%
% To display Fig. 10 use :
%
% exp_kelvasa2015('fig10');
%
% To display Fig. 12 use :
%
% exp_kelvasa2015('fig12');
%
% References:
% D. Kelvasa and M. Dietz. Auditory model-based sound direction
% estimation with bilateral cochlear implants. Trends in Hearing,
% 19:2331216515616378, 2015.
%
%
% Url: http://amtoolbox.sourceforge.net/amt-0.10.0/doc/experiments/exp_kelvasa2015.php
% Copyright (C) 2009-2020 Piotr Majdak and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 1.0.0
%
% 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/>.
% Authors:
% Daryl Kelvasa (daryl.kelvasa@uni-oldenburg.de) 2016
% Mathias Dietz (mdietz@uwo.ca) 2016
% Thomas Deppisch (thomas.deppisch@student.tugraz.at) 2017
% Piotr Majdak (piotr@majdak.com) 2017
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Retrieve and compute model paramters
% Set flags
definput.flags.type = {'missingflag','fig5','fig6','fig7','fig8a',...
'fig8b','fig9a','fig10','fig12'};
definput.flags.disp = {'no_debug','debug'};
definput.flags.plot = {'plot','no_plot'};
definput.flags.plot_stage_fig = {'no_plot_stage_fig','plot_stage_fig'};
% import default arguments from other functions
definput.import={'kelvasa2015','amt_cache'}; % defaults from arg_kelvasa2015
[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
%% Load HRTF data
HRTF = SOFAload(fullfile(SOFAdbPath,'kelvasa2015',...
kv.HRTFfile));
[~,ind_elev] = min(abs(HRTF.SourcePosition(:,2)-kv.HRTFelevation));
[~,ind_dist] = min(abs(HRTF.SourcePosition(:,3)-kv.HRTFsourceDistance));
ind = find(sum([HRTF.SourcePosition(:,2) == HRTF.SourcePosition(ind_elev,2),...
HRTF.SourcePosition(:,3) == HRTF.SourcePosition(ind_dist,3)],2)...
==2);
HRTFnew.SourcePosition = HRTF.SourcePosition(ind,:);
HRTFnew.Data.IR = HRTF.Data.IR(ind,kv.HRTFchannels,:);
HRTFnew.Data.SamplingRate = HRTF.Data.SamplingRate;
HRTF = HRTFnew;
%% Set dB SPL offset
ltfatsetdefaults('dbspl','dboffset',71.778);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Figure 5
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if flags.do_fig5
%Needed parameters
savename = ['Figure_5_data_',kv.identifier];
%Check for preprocessed calibration data
[dataOut] = amt_cache('get', savename, flags.cachemode);
if isempty(dataOut)
signals = {'SSN.wav','FrozenSpeech.wav','PinkNoise.wav','WhiteNoise.wav'};
levels = 35:70;
elecPos = [3 4; 7 8; 11 12; 15 16; 19 20];
%Main loop over all stimuli
n = 1; numLoops = numel(levels)*numel(signals)*2;
H = waitbar(0,'Computing data for Figure 5');
for ind = 1 : 8
[signal, fs] = amt_load('kelvasa2015',signals{ceil(ind./2)});
signal = signal(1:6*fs,:);
signal = resample(signal,kv.FS_ACE,fs);
if ismember(ind,2:4:8)
%HRTF filter signal and choose microphone channels
[signal] = HRTFfilter(signal,0,kv,HRTF);
end
spikeRatePerBinPerLevel = zeros(kv.numBin,numel(levels));
currentPerBinPerLevel = zeros(kv.numBin,numel(levels));
for level = 1 : numel(levels)
tic
temp = signal(:,1)./rms(signal(:,1));
scalor = scaletodbspl(levels(level));
scalor = rms(temp.*scalor)/rms(signal(:,1));
signal = signal.*scalor;
sigLengthSec = (size(signal,1)/fs);
[electrodogram, vTime] = ...
kelvasa2015_ciprocessing(signal,...
kv.FS_ACE,'argimport',flags,kv);
[APvec] = ...
kelvasa2015_anprocessing(electrodogram,...
vTime,'argimport',flags,kv);
[~,spikeRatePerBinPerLevelT] = ...
kelvasa2015_anbinning(APvec,...
sigLengthSec,'argimport',flags,kv);
spikeRatePerBinPerLevel(:,level) = ...
mean(spikeRatePerBinPerLevelT,2);
currentPerBinPerLevel(:,level) = ...
(sum(electrodogram(elecPos(:,2),:),2) + ...
sum(electrodogram(elecPos(:,1),:),2)).* ...
25e-6.*1e-6.*1e9;
a = toc; timeLeft = round((a*(numLoops - n))/60);
H = waitbar(n/numLoops,H,['Computing Figure 5. Time left (min):',...
num2str(timeLeft)]); n = n+1;
end
dataOut(ind).spikeRatePerBinPerLevel = spikeRatePerBinPerLevel;
dataOut(ind).currentPerBinPerLevel = currentPerBinPerLevel;
end
delete(H)
amt_cache('set',savename,dataOut);
end
if flags.do_plot; plot_kelvasa2015(dataOut,'argimport',flags,kv); end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Figure 6
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if flags.do_fig6
%Needed parameters
azis = 0:5:90;
levels = [45,55,65];
savename = ['Figure_6_data_',kv.identifier];
%Check for preprocessed calibration data
[dataOut] = amt_cache('get', savename, flags.cachemode);
if isempty(dataOut)
signalName = 'SSN.wav';
[signal, fs] = amt_load('kelvasa2015',signalName);
signal = signal(1:6*fs,:);
signal = resample(signal,kv.FS_ACE,fs);
SpkDiffPerBin = zeros(numel(azis),kv.numBin);
SpkSumPerBin = zeros(numel(azis),kv.numBin);
%Main loop over all levels
n = 1; numLoops = numel(levels)*numel(azis);
H = waitbar(0,'Computing data for Figure 6');
for level = 1 : numel(levels)
for ang = 1 : numel(azis)
tic
%HRTF filter signal and choose microphone channels
[HRIR] = HRTFfilter(signal,azis(ang),kv,HRTF);
if azis(ang) == 0
temp = HRIR(:,1)./rms(HRIR(:,1));
scalor = scaletodbspl(levels(level));
scalor = rms(temp.*scalor)/rms(HRIR(:,1));
end
HRIR = HRIR .* scalor;
sigLengthSec = (size(HRIR,1)/fs);
spikeRatePerBin = zeros(kv.numBin,2);
for chan = 1 : 2
singChanSig = HRIR(:,chan);
[electrodogram, vTime] = ...
kelvasa2015_ciprocessing(singChanSig,...
kv.FS_ACE,'argimport',flags,kv);
[APvec] = ...
kelvasa2015_anprocessing(electrodogram,...
vTime,'argimport',flags,kv);
[~,spikeRatePerBinT] = ...
kelvasa2015_anbinning(APvec,...
sigLengthSec,'argimport',flags,kv);
spikeRatePerBin(:,chan) = mean(spikeRatePerBinT,2);
end
SpkDiffPerBin(ang,:) = spikeRatePerBin(:,2)- spikeRatePerBin(:,1);
SpkSumPerBin(ang,:) = spikeRatePerBin(:,2) + spikeRatePerBin(:,1);
a = toc; timeLeft = round((a*(numLoops - n))/60);
H = waitbar(n/numLoops,H,...
['Computing Figure 6. Time left (min):',...
num2str(timeLeft)]); n = n+1;
end
dataOut(level).signalName = signalName;
dataOut(level).levelDB = levels(level);
dataOut(level).SpkDiffPerBin = SpkDiffPerBin;
dataOut(level).SpkSumPerBin = SpkSumPerBin;
end
delete(H)
amt_cache('set',savename,dataOut);
end
dataOut(1).azis=azis;
if flags.do_plot; plot_kelvasa2015(dataOut,'argimport',flags,kv); end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Figure 7
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if flags.do_fig7
%Needed parameters
azis = 0:5:90;
level = 65;
savename = ['Figure_7_data_',kv.identifier];
%Check for preprocessed calibration data
[dataOut] = amt_cache('get', savename, flags.cachemode);
if isempty(dataOut)
signalName = {'SSN.wav','PinkNoise.wav'};
%Main loop over all angles and signal
n = 1; numLoops = 2*numel(azis);
H = waitbar(0,'Computing data for Figure 7');
for sig = 1 : 2
[signal, fs] = amt_load('kelvasa2015',signalName{sig});
signal = signal(1:6*fs,:);
signal = resample(signal,kv.FS_ACE,fs);
SpkDiffPerBin = zeros(numel(azis),kv.numBin);
SpkSumPerBin = zeros(numel(azis),kv.numBin);
for ang = 1 : numel(azis)
tic
%HRTF filter signal and choose microphone channels
[HRIR] = HRTFfilter(signal,azis(ang),kv,HRTF);
if azis(ang) == 0
temp = HRIR(:,1)./rms(HRIR(:,1));
scalor = scaletodbspl(level);
scalor = rms(temp.*scalor)/rms(HRIR(:,1));
end
HRIR = HRIR .* scalor;
sigLengthSec = (size(HRIR,1)/fs);
spikeRatePerBin = zeros(kv.numBin,2);
for chan = 1 : 2
singChanSig = HRIR(:,chan);
[electrodogram, vTime] = ...
kelvasa2015_ciprocessing(singChanSig,...
kv.FS_ACE,'argimport',flags,kv);
[APvec] = ...
kelvasa2015_anprocessing(electrodogram,...
vTime,'argimport',flags,kv);
[~,spikeRatePerBinT] = ...
kelvasa2015_anbinning(APvec,...
sigLengthSec,'argimport',flags,kv);
spikeRatePerBin(:,chan) = mean(spikeRatePerBinT,2);
end
SpkDiffPerBin(ang,:) = spikeRatePerBin(:,2)- spikeRatePerBin(:,1);
SpkSumPerBin(ang,:) = spikeRatePerBin(:,2) + spikeRatePerBin(:,1);
a = toc; timeLeft = round((a*(numLoops - n))/60);
H = waitbar(n/numLoops,H,...
['Computing Figure 7. Time left (min):',...
num2str(timeLeft)]); n = n+1;
end
dataOut(sig).signalName = signalName{sig};
dataOut(sig).levelDB = (level);
dataOut(sig).SpkDiffPerBin = SpkDiffPerBin;
dataOut(sig).SpkSumPerBin = SpkSumPerBin;
end
%Save data
delete(H)
amt_cache('set',savename,dataOut);
end
if flags.do_plot; plot_kelvasa2015(dataOut,'argimport',flags,kv); end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Figure 8a
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if flags.do_fig8a
%Parameters
savename = ['Figure_8a_data_',kv.identifier];
%Check for preprocessed calibration data
[dataOut] = amt_cache('get', savename, flags.cachemode);
if isempty(dataOut); clear dataOut
levels = [45,55,65];
signalName = 'SSN.wav';
[signal fs] = amt_load('kelvasa2015',signalName);
signal = signal(1:6*fs,:);
signal = resample(signal,kv.FS_ACE,fs);
kv.AziCrvfitRange = 45;
kv.localizationModel = 'RateLevel';
%Run model
H = waitbar(0,'Computing data for fig8a');
for ind = 1 : numel(levels)
tic
dataOut(ind) = groupPredictions(signal,levels(ind),kv,flags,HRTF);
a = toc; timeLeft = round((a*(3 - ind))/60);
H = waitbar(ind/3,H,...
['Computing Figure 8a. Time left (min):',...
num2str(timeLeft)]);
end
%Save data
delete(H)
amt_cache('set',savename,dataOut);
end
if flags.do_plot; plot_kelvasa2015(dataOut,'argimport',flags,kv); end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Figure 8b
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if flags.do_fig8b
%Parameters
savename = ['Figure_8b_data_',kv.identifier];
%Check for preprocessed calibration data
[dataOut] = amt_cache('get', savename, flags.cachemode);
if isempty(dataOut); clear dataOut
levels = [45,55,65];
signalName = 'SSN.wav';
[signal fs] = amt_load('kelvasa2015',signalName);
signal = signal(1:6*fs,:);
signal = resample(signal,kv.FS_ACE,fs);
kv.AziCrvfitRange = 45;
kv.localizationModel = 'ResponseDifferenceAN';
%Run model
H = waitbar(0,'Computing data for fig8b');
for ind = 1 : numel(levels)
tic
%Run model
[dataOut(ind)] = groupPredictions(signal,levels(ind),kv,flags,HRTF);
a = toc; timeLeft = round((a*(3 - ind))/60);
H = waitbar(ind/3,H,...
['Computing Figure 8b. Time left (min):',...
num2str(timeLeft)]);
end
delete(H)
%Save data
amt_cache('set',savename,dataOut);
end
if flags.do_plot; plot_kelvasa2015(dataOut,'argimport',flags,kv); end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Figure 9a
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if flags.do_fig9a
%Paramters
savename = ['Figure_9a_data_',kv.identifier];
%Check for preprocessed calibration data
[dataOut] = amt_cache('get', savename, flags.cachemode);
if isempty(dataOut); clear dataOut
levels = [45,55,65];
signalName = 'SSN.wav';
[signal fs] = amt_load('kelvasa2015',signalName);
signal = signal(1:6*fs,:);
signal = resample(signal,kv.FS_ACE,fs);
kv.AziCrvfitRange = 90;
kv.localizationModel = 'ResponseDifferenceAN';
%Run model
H = waitbar(0,'Computing data for fig9a');
for ind = 1 : numel(levels)
tic
%Run Model
[dataOut(ind)] = groupPredictions(signal,levels(ind),kv,flags,HRTF);
a = toc; timeLeft = round((a*(3 - ind))/60);
H = waitbar(ind/3,H,...
['Computing Figure 9a. Time left (min):',...
num2str(timeLeft)]);
end
%Save data
delete(H)
amt_cache('set',savename,dataOut);
end
if flags.do_plot; plot_kelvasa2015(dataOut,'argimport',flags,kv); end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Figure 10
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if flags.do_fig10
%Parameters
savename = ['Figure_10_data_',kv.identifier];
%Check for preprocessed calibration data
[dataOut] = amt_cache('get', savename, flags.cachemode);
if isempty(dataOut); clear dataOut
level = 55;
signalName = {'MixedSpeech.wav','FrozenSpeech.wav'};
localizationModel = {'MaxLikelihood','ResponseDifferenceAN'};
kv.AziCrvfitRange = 90;
%Run model
n = 1; H = waitbar(0,'Computing data for fig10');
for model = 1 : 2
tic
if model == 1;
kv.localizationModelCalibWav = 'MixedSpeech.wav';end
kv.localizationModel = localizationModel{model};
for sig = 1 : 2
[signal fs] = amt_load('kelvasa2015',signalName{sig});
signal = resample(signal,kv.FS_ACE,fs);
[dataOut(n)] = groupPredictions(signal,level,kv,flags,HRTF);
a = toc; timeLeft = round((a*(4 - n))/60);
H = waitbar(n/4,H,...
['Computing Figure 10. Time left (min):',...
num2str(timeLeft)]); n = n+1;
end
end
delete(H)
amt_cache('set',savename,dataOut);
end
if flags.do_plot; plot_kelvasa2015(dataOut,'argimport',flags,kv); end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Figure 12
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if flags.do_fig12
%Parameters
savename = ['Figure_12_data_',kv.identifier];
%Check for preprocessed calibration data
[dataOut] = amt_cache('get', savename, flags.cachemode);
if isempty(dataOut); clear dataOut
level = 55;
signalName = 'SSN.wav';
[signal fs] = amt_load('kelvasa2015',signalName);
signal = signal(1:6*fs,:);
signal = resample(signal,kv.FS_ACE,fs);
Desired_SNR_dB = 5;
kv.localizationModel = 'ResponseDifferenceAN';
kv.AziCrvfitRange = 90;
%Create 360� noise interferer
azis = -180:5:175;
[noise fs] = amt_load('kelvasa2015','PinkNoise.wav');
noise = resample(noise,kv.FS_ACE,fs);
noise = noise(1:numel(signal));
for ang = 1:numel(azis)
%HRTF filter signal and choose microphone channels
[HRIR] = HRTFfilter(noise,azis(ang),kv,HRTF);
noiseL(:,ang) = HRIR(:,1);
noiseR(:,ang) = HRIR(:,2);
end
%Adjust signal to desired level. Referenced to 0� azi
rmsSig = rms(mean(HRTFfilter(signal,0,kv,HRTF),2));
rmsNoise = rms(mean([sum(noiseL,2),sum(noiseR,2)],2));
K = (rmsSig/rmsNoise)*10^(-Desired_SNR_dB/20); % Scale factor
noise = [sum(noiseL,2),sum(noiseR,2)].*K;
%Run Model on signals
H = waitbar(0,'Computing data for fig12');
for ind = 1 : 2
tic
if ind == 1
[dataOut(ind)] = groupPredictions(signal,level,kv,flags,HRTF);
else
[dataOut(ind)] = groupPredictions(signal,level,kv,flags,...
HRTF,noise);
end
a = toc; timeLeft = round((a*(2 - ind))/60);
H = waitbar(ind/2,H,...
['Computing Figure 12. Time left (min):',...
num2str(timeLeft)]);
end
delete(H)
%Save data
amt_cache('set',savename,dataOut);
end
if flags.do_plot; plot_kelvasa2015(dataOut,'argimport',flags,kv); end
end
ltfatsetdefaults('dbspl','dboffset',93.98);
end
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [dataOut] = groupPredictions(signal,level,kv,flags,HRTF,varargin)
azis = 0:5:90;
groupedBinPredictions{numel(kv.binPos)} = [];
groupedWeightedPrediction= [];
for ang = 1 : numel(azis)
%HRTF filter signal and choose microphone channels
[HRIR] = HRTFfilter(signal,azis(ang),kv,HRTF);
%Adjust signal to desired level. Referenced to 0� azi
if azis(ang) == 0
temp = HRIR(:,1)./rms(HRIR(:,1));
scalor = scaletodbspl(level);
scalor = rms(temp.*scalor)/rms(HRIR(:,1));
end
HRIR = HRIR .* scalor;
%If 360� interferer is to be added
if ~isempty(varargin)
noise = varargin{1}.*scalor;
HRIR = HRIR + noise;
end
%Run HRTF filtered two channel signal through models
[results] = kelvasa2015(HRIR,kv.FS_ACE,'argimport',flags,kv);
%Bin azimuthal predictions over all target angles
rangeAzis = [-10:5:90];
if ~isempty(results.ANbinPredictions)
for bin = 1 : numel(kv.binPos)
data = results.ANbinPredictions(bin,:);
[counts ind] = histc(data,rangeAzis);
groupedPredictionsT = unique(rangeAzis(ind));
countsT = counts(counts~=0);
newInd = [size(groupedBinPredictions{bin},2) + 1 : ...
size(groupedBinPredictions{bin},2) + ...
numel(groupedPredictionsT)];
groupedBinPredictions{bin}(1,newInd) = groupedPredictionsT;
groupedBinPredictions{bin}(2,newInd) = countsT;
groupedBinPredictions {bin}(3,newInd) = ...
repmat(azis(ang),1,numel(newInd));
end;end
data = results.weightedPredictions;
[counts ind] = histc(data,rangeAzis);
groupedPredictionsT = unique(rangeAzis(ind));
countsT = counts(counts~=0);
newInd = (size(groupedWeightedPrediction,2) + 1) : ...
(size(groupedWeightedPrediction,2) + ...
numel(groupedPredictionsT));
groupedWeightedPrediction(1,newInd) = groupedPredictionsT;
groupedWeightedPrediction(2,newInd) = countsT;
groupedWeightedPrediction(3,newInd) = ...
repmat(azis(ang),1,numel(newInd));
end
dataOut.groupedBinPredictions = groupedBinPredictions;
dataOut.groupedWeightedPrediction = groupedWeightedPrediction;
groupedBinPredictions = [];
end
%%
function [outsig] = HRTFfilter(insig,ang,kv,HRTF)
[~,ind_ang] = min(abs(HRTF.SourcePosition(:,1)-ang));
HRIR = resample(squeeze(HRTF.Data.IR(ind_ang,:,:))',...
kv.FS_ACE,HRTF.Data.SamplingRate);
%Filter signal with HRTF frequency domain
HRTFchan1 = ifft(fft(insig).*fft(HRIR(:,1),numel(insig)));
HRTFchan2 = ifft(fft(insig).*fft(HRIR(:,2),numel(insig)));
outsig = [HRTFchan1,HRTFchan2];
end
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