THE AUDITORY MODELING TOOLBOX
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ZIEGELWANGER2014 - Time of arrival estimates
Program code:
function [Obj,results]=ziegelwanger2014(Obj,varargin)
%ZIEGELWANGER2014 Time of arrival estimates
% Usage: [Obj,results]=ziegelwanger2014(data,estimation,outlierDetection,model,p0_onaxis)
%
%
% Input parameters:
%
% Obj : SOFA object
%
% estimation : (optional) TOA estimation method:
%
% [TOAest] pre-estimated TOAs
%
% 1 Built-in maximum detection
%
% 2 Built-in centroid of squared IR,
%
% 3 Built-in mean group delay,
%
% 4 Built-in maximum of the minimum-phase cross-correlation (default)
%
% 5 Built-in maxium detection after 3k-low-pass filtering (similar to Andreopoulou and Katz, 2017)
%
% String (Experimental) Estimator from the 'itdestimator' function, requires further parameters
%
% outlierDetection : (optional) detect outliers in estimated TOAs
%
% 0 off
%
% 1 on (default values: [0.05;0.01])
%
% [alpha r] reject outliers using the extreme Studentized deviance test with the significance level of ALPHA and upper bound of outlier rate R
%
% model: (optional) correct estimated toa, using geometrical TOA-Model
%
% 0 no model, just estimate the TOAs
%
% 1 (default) off-axis TOA modeled
%
% 2 on-axis TOA modeled
%
% p0_onaxis: (optional) startvalues for lsqcurvefit
%
% lowpass: (optional) bandwidth setting when used with the estimator from 'itdestimator' ('lp' for lowpass, 'bb' for broadband)
%
% upper_cutfreq: (optional) lowpass cutoff (Hz) when used with the estimator from 'itdestimator'
%
% threshlvl: (optional) threshold level (dB) when used with the 'Threshold' estimator from 'itdestimator'
%
% Output parameters:
%
% Obj: SOFA Object
%
% results: struct with fields
%
% .toa: data matrix with time of arrival (TOA) for each impulse response (IR)
%
% .p_onaxis: estimated on-axis model-parameters
%
% .p_offaxis: estimated off-axis model-parameters
%
% Estimates the Time-of-Arrival for each measurement in Obj (SOFA) and
% corrects the results with a geometrical model of the head.
%
% Requirements:
% -------------
%
% 1) SOFA API from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)
%
% 2) Optimization Toolbox for Matlab
%
% Examples:
% ---------
%
% To calculate the model parameters for the on-axis time-of-arrival model
% (p_onaxis) and for the off-axis time-of-arrival model (p_offaxis) for a
% given HRTF set (SOFA object, 'Obj') with the minimum-phase
% cross-correlation estimation, use:
%
% [Obj,results]=ziegelwanger2014(Obj,4,1);
%
% See also: ziegelwanger2014_onaxis, ziegelwanger2014_offaxis,
% data_ziegelwanger2014, exp_ziegelwanger2014, itdestimator
%
% References:
% H. Ziegelwanger and P. Majdak. Modeling the direction-continuous
% time-of-arrival in head-related transfer functions. J. Acoust. Soc.
% Am., 135:1278--1293, 2014.
%
%
%
% Url: http://amtoolbox.org/amt-1.2.0/doc/models/ziegelwanger2014.php
% Copyright (C) 2009-2022 Piotr Majdak, Clara Hollomey, and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 1.2.0
%
% 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: Harald Ziegelwanger, Acoustics Research Institute, Vienna, Austria
% 2018/04/05, Robert Baumgartner: modified to incorporate ltfatarghelper
% 2018/04/16, Laurin Steidle: modified to incorporate itdestimator
%% ----------------------------convert to SOFA-----------------------------
if ~isfield(Obj,'GLOBAL_Version')
Obj=SOFAconvertARI2SOFA(Obj.hM,Obj.meta,Obj.stimPar);
end
%% ----------------------------check variables-----------------------------
definput.flags.lowpass = {'lp','bb'};
definput.flags.peak = {'hp','fp'};
definput.flags.disp = {'no_debug','debug'};
definput.keyvals.estimation = 4;
definput.keyvals.outlierDetection = [0.05;0.01];
definput.keyvals.model=1e-6;
definput.keyvals.p0_onaxis=[[0.0875; pi/2; 0; 0] [0.0875; -pi/2; 0; 0]];
definput.keyvals.threshlvl = -10;
definput.keyvals.upper_cutfreq = 3000;
definput.keyvals.butterpoly = 10;
[flags,kv]=ltfatarghelper({'estimation','outlierDetection','model','p0_onaxis'},definput,varargin);
estimation = kv.estimation;
outlierDetection = prod(kv.outlierDetection);
%% -------------------------initialize variables---------------------------
p0_onaxis=transpose(kv.p0_onaxis);
p_onaxis=zeros(size(p0_onaxis));
p0_offaxis=zeros(2,7);
p_offaxis=p0_offaxis;
toa=zeros(Obj.API.M,Obj.API.R);
toa_onaxis=toa;
toa_offaxis=toa;
indicator=zeros(Obj.API.M,Obj.API.R);
pos(:,1:2)=Obj.SourcePosition(:,1:2);
%% -----------------------estimate time-of-arrival-------------------------
if isscalar(estimation)
hM=Obj.Data.IR;
toaEst=zeros(Obj.API.M,Obj.API.R);
switch estimation
case 1 %---------------------------Threshold---------------------------
for ii=1:Obj.API.M
for jj=1:Obj.API.R
toaEst(ii,jj)=find(abs(hM(ii,jj,:))==max(abs(hM(ii,jj,:))),1);
end
end
case 2 %---------------------------Centroid----------------------------
for ii=1:Obj.API.M
for jj=1:Obj.API.R
toaEst(ii,jj)=find(cumsum(hM(ii,jj,:).^2)>(sum(hM(ii,jj,:).^2)/2),1);
end
end
case 3 %---------------------------Groupdelay--------------------------
for ii=1:Obj.API.M
for jj=1:Obj.API.R
[Gd,F]=grpdelay(transpose(double(squeeze(hM(ii,jj,:)))),...
1,Obj.API.N*4,Obj.Data.SamplingRate*4);
toaEst(ii,jj)=mean(Gd(find(F>1000):find(F>5000)));
end
end
case 4 %---------------------------Minimal-Phase-----------------------
hMmin=ARI_MinimalPhase(Obj);
corrcoeff=zeros(Obj.API.M,Obj.API.R);
for ii=1:Obj.API.M
for jj=1:Obj.API.R
[c,lag]=xcorr(squeeze(hM(ii,jj,:)),squeeze(hMmin(ii,jj,:)),...
Obj.API.N*4-1,'none');
[corrcoeff(ii,jj),idx]=max(abs(c));
corrcoeff(ii,jj)=corrcoeff(ii,jj)/sum(hM(ii,jj,:).^2);
toaEst(ii,jj)=lag(idx);
end
end
case 5 % ------------ Threshold on low-passed IRs
amt_disp(' Applying Butterworth low pass',flags.disp)
amt_disp(strcat(' Polynomial order of Butterworth filter: ',num2str(kv.butterpoly)),flags.disp)
amt_disp(strcat(' Cut-off frequency is: ',num2str(kv.upper_cutfreq),' Hz'),flags.disp)
cut_off_freq_norm = kv.upper_cutfreq/(Obj.Data.SamplingRate/2);
[lp_a,lp_b] = butter(kv.butterpoly,cut_off_freq_norm);
% f_ir = zeros(pos,ear,Ns);
for ii=1:Obj.API.M
for jj=1:Obj.API.R
sir = squeeze( hM(ii,jj,:) );
f_sir = filter(lp_a,lp_b,sir);
% f_ir(ii,jj,:) = f_sir;
toaEst(ii,jj)=(find(abs(f_sir)==max(abs(f_sir)),1))-round(0.4e-3*Obj.Data.SamplingRate); % compensate for the delay of the filter, estimated for default parameters
end
end
end
elseif ischar(estimation)
[~,toaEst] = itdestimator(Obj,estimation,flags.lowpass,flags.peak,'guesstoa');
toaEst = toaEst*Obj.Data.SamplingRate;
else
toaEst=estimation;
end
%% --------------------Detect-Outliers-in-estimated-TOA--------------------
if outlierDetection>0
for ch=1:Obj.API.R
p0_onaxis(ch,4) = min(toaEst(indicator(:,ch)==0,ch))/Obj.Data.SamplingRate;
p0offset_onaxis = [0.06 pi pi/2 0.001];
x = pos(:,1:2)*pi/180;
y = toaEst(:,ch)/Obj.Data.SamplingRate;
tmp=lsqcurvefit(@ziegelwanger2014_onaxis,p0_onaxis(ch,:),...
x,y,p0_onaxis(ch,:)-p0offset_onaxis,...
p0_onaxis(ch,:)+p0offset_onaxis,...
optimset('Display','off','TolFun',1e-6));
outliers = toaEst(:,ch)-ziegelwanger2014_onaxis(tmp,pos(:,1:2)*pi/180)*Obj.Data.SamplingRate;
[~,idx]=deleteoutliers(outliers, outlierDetection*Obj.API.M);
indicator(idx,ch)=ones(length(idx),1);
end
end
%% ----------------------Fit-Models-to-estimated-TOA-----------------------
if kv.model>0
% Fit on-axis model to outlier adjusted set of estimated TOAs
for ch=1:Obj.API.R
p0_onaxis(ch,4) = min(toaEst(indicator(:,ch)==0,ch))/Obj.Data.SamplingRate;
p0offset_onaxis = [0.06 pi pi/2 0.001];
idx = find(indicator(:,ch)==0);
x = pos(idx,1:2)*pi/180;
y = toaEst(idx,ch)/Obj.Data.SamplingRate;
[p_onaxis(ch,:),performance.on_axis{ch}.resnormS,...
performance.on_axis{ch}.residualS,...
performance.on_axis{ch}.exitflag,...
performance.on_axis{ch}.output] =...
lsqcurvefit(@ziegelwanger2014_onaxis,p0_onaxis(ch,:),x,y,...
p0_onaxis(ch,:)-p0offset_onaxis,...
p0_onaxis(ch,:)+p0offset_onaxis,...
optimset('Display','off','TolFun',1e-6));
toa(:,ch)=ziegelwanger2014_onaxis(p_onaxis(ch,:),pos(:,1:2)*pi/180)*Obj.Data.SamplingRate;
performance.on_axis{ch}.resnormS = ...
sqrt(performance.on_axis{ch}.resnormS/(Obj.API.M-sum(indicator(:,ch))));
performance.on_axis{ch}.resnormP = ...
norm((toaEst(:,ch)-toa(:,ch))/Obj.Data.SamplingRate)/sqrt(Obj.API.M);
end
toa_onaxis=toa;
% Fit off-axis model to outlier adjusted set of estimated TOAs
if kv.model~=2
for ch=1:Obj.API.R
idx = find(indicator(:,ch)==0);
x = pos(idx,1:2)*pi/180;
y = toaEst(idx,ch)/Obj.Data.SamplingRate;
p0_offaxis(ch,:) = [mean(p_onaxis(:,1)) 0.001 ...
-diff(p_onaxis(:,1))/2 0.001 ...
mean(p_onaxis(:,4)) ...
p_onaxis(ch,2) ...
p_onaxis(ch,3)];
p0offset_offaxis = [abs(diff(p_onaxis(:,1))/4) 0.1 0.1 0.1 0.001 pi/4 pi/4];
[p_offaxis(ch,:),performance.off_axis{ch}.resnormS,...
performance.off_axis{ch}.residualS,...
performance.off_axis{ch}.exitflag,...
performance.off_axis{ch}.output] = ...
lsqcurvefit(@ziegelwanger2014_offaxis,...
p0_offaxis(ch,:),x,y,...
p0_offaxis(ch,:)-p0offset_offaxis,...
p0_offaxis(ch,:)+p0offset_offaxis,...
optimset('Display','off','TolFun',kv.model));
toa(:,ch)=ziegelwanger2014_offaxis(p_offaxis(ch,:),pos(:,1:2)*pi/180)*Obj.Data.SamplingRate;
performance.off_axis{ch}.resnormS = ...
sqrt(performance.off_axis{ch}.resnormS/(Obj.API.M-sum(indicator(:,ch))));
performance.off_axis{ch}.resnormP = ...
norm((toaEst(:,ch)-toa(:,ch))/Obj.Data.SamplingRate)/sqrt(Obj.API.M);
end
toa_offaxis=toa;
end
else
toa=toaEst;
p_offaxis=p0_offaxis;
end
%Save to output variables
performance.outliers=indicator;
for ii=1:size(indicator,2)
performance.outlierRate(ii)=sum(indicator(:,ii))/Obj.API.M*100;
end
results.toa=toa;
results.toaEst=toaEst;
results.toa_onaxis=toa_onaxis;
results.toa_offaxis=toa_offaxis;
results.p_onaxis=transpose(p_onaxis);
results.p_offaxis=transpose(p_offaxis);
results.performance=performance;
if exist('corrcoeff','var')
results.performance.corrcoeff=corrcoeff;
end
end %of function
function hMmin=ARI_MinimalPhase(Obj)
hM=Obj.Data.IR;
hMmin=hM;
for jj=1:Obj.API.R
for ii=1:Obj.API.M
h=[squeeze(hM(ii,jj,:)); zeros(4096*4-size(hM,3),1)];
amp1=abs(fft(h));
amp2=amp1;
an2u=-imag(hilbert(log(amp1)));
an2u=an2u(1:floor(length(h)/2)+1);
an3u=[an2u; -flipud(an2u(2:end+mod(length(h),2)-1))];
an3=an3u-round(an3u/2/pi)*2*pi;
amp2=amp2(1:floor(length(h)/2)+1);
amp3=[amp2; flipud(amp2(2:end+mod(length(h),2)-1))];
h2=real(ifft(amp3.*exp(1i*an3)));
hMmin(ii,jj,:)=h2(1:Obj.API.N);
end
end
end
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