THE AUDITORY MODELING TOOLBOX
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ITDESTIMATOR - Estimate ITD from a binaural signal
Program code:
function [toa_diff,toa,IACC] = itdestimator(Obj,varargin)
%ITDESTIMATOR Estimate ITD from a binaural signal
% Usage: itd = itdestimator(data,mode,threshlvl,lowpass,butterpoly,upper_cutfreq)
%
% Input parameters:
%
% data: SOFA object or IR matrix with dimensions:
% emitter x receiver x time
%
% fs: sampling rate, used only if data provided as matrix
%
% mode: (optional) Select one estimation methods
% ('Threshold' (default),'Cen_e2','MaxIACCr', 'MaxIACCe',
% 'CenIACCr','CenIACCe', 'CenIACC2e', 'PhminXcor','IRGD')
%
% For details concerning estimation methods see:
% 'http://asa.scitation.org/doi/10.1121/1.4996457'
%
% lowpass: (optional) Bandwidth considered. 'lp' for lowpass (default), 'bb' for broadband
%
% peak: (optional) Method to find the max, used in Threshold mode only.
% 'hp' for max (default), 'fb' for findpeak
%
% threshlvl: (optional) Set threshold level for 'Threshold' mode in dB.
% Default is -10 dB.
%
% butterpoly: (optional) Select the order of the polynom
% applied in the butterworth filter. ( 2 =< i =< 10 )
% Default is 10.
%
% upper_cutfreq: (optional) Set frequency of lowpass cutoff in Hz.
% Default is 3000 Hz.
%
% lower_cutfreq: (optional) Set frequency of highpass cutoff in Hz,
% only used in IRGD mode. Default is 1000 Hz.
%
% 'debug' : output debug information about calculations.
%
%
% Output parameters:
%
% itd: interaural time difference in seconds
% toa: detected activation onsets for left and right channels
% IACC: interaural cross-correlation coefficient
% Available on when xcorr is used (modes: 'MaxIACCr', 'MaxIACCe',
% 'CenIACCr','CenIACCe', 'CenIACC2e')
%
%
% Purpose:
% Estimates the ITD based on biaural impulse responses.
% Several different estimaton methods can be chosen.
% MaxIAACe is recommended.
%
% Requirements:
% -------------
%
% 1) SOFA API from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)
%
%
% Examples:
% ---------
%
% Obj = SOFAload(fullfile(SOFAdbPath,'baumgartner2017','hrtf b_nh15.sofa'));
% toa_diff = itdestimator(Obj,'MaxIACCe','lp','upper_cutfreq',3000)
%
% With these settings the estimator uses the MaxIAAce method and applies
% a lowpass with a cut off frequency of 3 kHz.
%
% The output array is structured as the SOFA Data.IR
% If you would like to select for example only data on the horizontal
% plane you could:
%
% plane_idx = find( Obj.SourcePosition(:,2) == 0 );
% plane_angle = Obj.SourcePosition(plane_idx,1);
%
% Url: http://amtoolbox.org/amt-1.1.0/doc/common/itdestimator.php
% Copyright (C) 2009-2021 Piotr Majdak, Clara Hollomey, and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 1.1.0
%
% 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: Laurin Steidle
% data matrix option: Robert Baumgartner
% ---------------------- ltfatarghelper -------------------------------
definput.import = {'itdestimator'}; % load defaults from arg_itdestimator
[flags,kv]=ltfatarghelper({},definput,varargin);
% ---------------------- renaming input parameter ---------------------
if isstruct(Obj)
pos = Obj.API.M;
ear = Obj.API.R;
Ns = Obj.API.N;
IR = Obj.Data.IR;
fs = Obj.Data.SamplingRate;
else
pos = size(Obj,1);
ear = size(Obj,2);
Ns = size(Obj,3);
IR = Obj;
if isempty(kv.fs)
error('RB: No sampling rate (fs) provided.')
end
fs = kv.fs;
end
% ---------------------- initialising variables -----------------------
toa = zeros(pos,ear);
toa_diff = zeros(pos,1);
IACC = zeros(pos,1);
amt_disp('itdestimator:',flags.disp);
% ---------------------- Applying low-pass ----------------------------
if flags.do_lp
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/(fs/2);
[lp_a,lp_b] = butter(kv.butterpoly,cut_off_freq_norm);
f_ir = zeros(pos,ear,Ns);
for ii=1:pos
for jj=1:ear
sir = squeeze( IR(ii,jj,:) );
f_sir = filter(lp_a,lp_b,sir);
f_ir(ii,jj,:) = f_sir;
end
end
else
amt_disp(' No low pass filter is applied',flags.disp)
f_ir = IR;
end
% ---------------------- estimating itd -------------------------------
% ---------------------------------------------------------------------
% ---------------------- Threshold ------------------------------------
switch(flags.mode)
case 'Threshold'
amt_disp(' Threshold mode',flags.disp)
amt_disp(strcat(' Threshold level is: ',num2str(kv.threshlvl),'dB'),flags.disp)
if flags.do_fp
for ii=1:pos
for jj=1:ear
indB = 0.5*mag2db(squeeze(f_ir(ii,jj,:)).^2);
[~,B] = findpeaks(indB);
th_value = indB(B(1)) + kv.threshlvl;
toa(ii,jj) = find(indB>th_value,1);
end
toa_diff(ii) = toa(ii,1) - toa(ii,2);
end
else
for ii=1:pos
for jj=1:ear
indB = 0.5*mag2db(squeeze(f_ir(ii,jj,:)).^2);
th_value = max(indB) + kv.threshlvl;
toa(ii,jj) = find(indB>th_value,1);
end
toa_diff(ii) = toa(ii,1) - toa(ii,2);
end
end
% ---------------------- Cross-Correlation ----------------------------
case 'Cen_e2'
amt_disp(' Cen-e2 mode',flags.disp)
for ii=1:pos
for jj = 1:ear
e_sir_sq = abs(hilbert(squeeze(f_ir(ii,jj,:))).^2);
toa(ii,jj) = centroid(transpose(1:Ns),e_sir_sq);
end
toa_diff(ii) = toa(ii,1) - toa(ii,2);
end
case 'MaxIACCr'
amt_disp(' MaxIACCr mode',flags.disp)
for ii=1:pos
cc = xcorr(squeeze(f_ir(ii,1,:)),squeeze(f_ir(ii,2,:)));
[IACC(ii),idx_lag] = max(abs(cc));
toa_diff(ii) = idx_lag - Ns;
end
if flags.do_guesstoa
toa = guesstoa(toa_diff,toa, kv.avgtoa);
end
case 'MaxIACCe'
amt_disp(' MaxIACCe mode',flags.disp)
for ii=1:pos
e_sir1 = abs(hilbert(squeeze(f_ir(ii,1,:))));
e_sir2 = abs(hilbert(squeeze(f_ir(ii,2,:))));
cc = xcorr(e_sir1,e_sir2);
[IACC(ii),idx_lag] = max(abs(cc));
toa_diff(ii) = idx_lag - Ns;
end
if flags.do_guesstoa
toa = guesstoa(toa_diff,toa, kv.avgtoa);
end
case 'CenIACCr'
amt_disp(' CenIACCr mode',flags.disp)
x = transpose(1:(Ns*2-1));
for ii=1:pos
cc = xcorr(squeeze(f_ir(ii,1,:)),squeeze(f_ir(ii,2,:)));
pos_cc = abs(cc);
IACC(ii) = max(pos_cc);
toa_diff(ii) = centroid(x,pos_cc)-Ns;
end
if flags.do_guesstoa
toa = guesstoa(toa_diff,toa, kv.avgtoa);
end
case 'CenIACCe'
amt_disp(' CenIACCe mode',flags.disp)
x = transpose(1:(Ns*2-1));
for ii=1:pos
e_sir1 = abs(hilbert(squeeze(f_ir(ii,1,:))));
e_sir2 = abs(hilbert(squeeze(f_ir(ii,2,:))));
cc = xcorr(e_sir1,e_sir2);
IACC(ii) = max(abs(cc));
toa_diff(ii) = centroid(x,abs(cc))-Ns;
end
if flags.do_guesstoa
toa = guesstoa(toa_diff,toa, kv.avgtoa);
end
case 'CenIACC2e'
amt_disp(' CenIACC2e mode',flags.disp)
x = transpose(1:(Ns*2-1));
for ii=1:pos
e_sir1 = abs(hilbert(squeeze(f_ir(ii,1,:))));
e_sir2 = abs(hilbert(squeeze(f_ir(ii,2,:))));
cc = xcorr(e_sir1,e_sir2).^2;
IACC(ii) = max(abs(cc));
toa_diff(ii) = centroid(x,abs(cc))-Ns;
end
if flags.do_guesstoa
toa = guesstoa(toa_diff,toa, kv.avgtoa);
end
case 'PhminXcor'
amt_disp(' PhminXcor mode',flags.disp)
ir_min=ARI_MinimalPhase(Obj);
for ii=1:pos
for jj=1:ear
cc = xcorr(squeeze(IR(ii,jj,:)),squeeze(ir_min(ii,jj,:)));
[~,toa(ii,jj)] = max(abs(cc));
end
toa_diff(ii) = toa(ii,1) - toa(ii,2);
end
% ---------------------- Groupdelay -----------------------------------
case 'IRGD'
amt_disp(' IRGD mode',flags.disp)
for ii = 1:pos
for jj = 1:ear
f_sir = squeeze( f_ir(ii,jj,:) );
[gd,w] = grpdelay(transpose(double(f_sir)),1,Ns,fs);
toa(ii,jj)=mean(gd(find(w>kv.lower_cutfreq): ...
find(w>kv.upper_cutfreq)));
end
toa_diff(ii) = toa(ii,1) - toa(ii,2);
end
end
toa_diff = toa_diff/fs;
toa = toa/fs;
% -------------------------------------------------------------------------
% ---------------------- Functions ----------------------------------------
% -------------------------------------------------------------------------
% ---------------------- Centroid -----------------------------------------
function idx_cent = centroid(x,y)
idx_cent = sum(x.*y)/sum(y);
% ---------------------- guess toa ----------------------------------------
function toa = guesstoa(toa_diff,toa, avgtoa)
toa(:,1) = toa(:,1) + avgtoa + toa_diff/2;
toa(:,2) = toa(:,2) + avgtoa - toa_diff/2;
% ---------------------- Create minimal phase -----------------------------
% as used in ziegelwanger2014
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,:));
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
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