function [Cohc,Cihc,OHC_Loss]=bruce2018_fitaudiogram(FREQUENCIES,dBLoss,species,Dsd_OHC_Loss)
%BRUCE2018_FITAUDIOGRAM Cohc and Cihc values that produce a desired threshold shift
%
% Usage:
% [Cohc,Cihc,OHC_Loss]=bruce2018_fitaudiogram(FREQUENCIES,dBLoss,species,Dsd_OHC_Loss)
%
% Input parameters:
% FREQUENCIES : vector containing audiogram frequencies
% dBLoss : loss [dB] per frequency in FREQUENCIES
% species : model species "1" for cat, "2" for human BM tuning from
% Shera et al. (PNAS 2002), or "3" for human BM tuning from Glasberg &
% Moore (Hear. Res. 1990)
% Dsd_OHC_Loss: optional array giving the desired threshold shift in
% dB that is caused by the OHC impairment alone (for each frequency in
% FREQUENCIES). If this array is not given, then the default desired
% threshold shift due to OHC impairment is 2/3 of the entire threshold
% shift at each frequency. This default is consistent with the
% effects of acoustic trauma in cats (see Bruce et al., JASA 2003, and
% Zilany and Bruce, JASA 2007) and estimated OHC impairment in humans
% (see Plack et al., JASA 2004).
%
% Output parameters:
% The output variables are arrays with values corresponding to each
% frequency in the input array FREQUENCIES.
%
% Cohc : is the outer hair cell (OHC) impairment factor; a value of 1
% corresponds to normal OHC function and a value of 0 corresponds to
% total impairment of the OHCs.
% Cihc : is the inner hair cell (IHC) impairment factor; a value of 1
% corresponds to normal IHC function and a value of 0 corresponds to
% total impairment of the IHC.
% OHC_Loss : is the threshold shift in dB that is attributed to OHC
% impairment (the remainder is produced by IHC impairment).
%
% BRUCE2018_FITAUDIOGRAM calculates the Cohc and Cihc values that produce a desired threshold shift
% for the cat & human auditory-periphery model of Zilany et
% al. (J. Acoust. Soc. Am. 2009, 2014) and Bruce, Erfani & Zilany (Hear.Res., 2018).
%
% Url: http://amtoolbox.org/amt-1.3.0/doc/modelstages/bruce2018_fitaudiogram.php
% #StatusDoc: Good
% #StatusCode: Perfect
% #Verification: Verified
% #Requirements: MATLAB MEX M-Signal
% #Author: M. S. A. Zilany (2013)
% #Author: Ian Bruce (2013)
% #Author: Alejandro Osses (2020): original implementation
% #Author: Clara Hollomey (2021): adapted to the AMT 1.0, removed saving of ANpopulation.mat
% #Author: Piotr Majdak (2021): adaptations to exp_osses2022; specificSRautoTiming added
% 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.
switch species
case 1
disp('Analyzing audiogram for cat AN model')
data = amt_load('bruce2018', 'THRESHOLD_ALL_CAT.mat');
case 2
disp('Analyzing audiogram for human AN model - BM tuning from Shera et al. (2002)')
data = amt_load('bruce2018', 'THRESHOLD_ALL_HM_Shera.mat');
case 3
disp('Analyzing audiogram for human AN model - BM tuning from Glasberg & Moore (1990)')
data = amt_load('bruce2018', 'THRESHOLD_ALL_HM_GM.mat');
otherwise
error(['Species # ' int2str(species) ' not known'])
end
CF = data.CF;
CIHC = data.CIHC;
COHC = data.COHC;
THR = data.THR;
% Variables are
% CF: 125 Hz to 10 kHz [1*37]
% CIHC: varies from 1.0 to 0.0001 [1*55]
% COHC: varies from 1.0 to 0 [1*56]
% THR : absolute thresholds [37*55*56]
for k = 1:length(THR(:,1,1))
dBShift(k,:,:)= THR(k,:,:) - THR(k,1,1);
end
if nargin<4, Dsd_OHC_Loss = 2/3*dBLoss;
end;
for m = 1:length(FREQUENCIES)
[W,N] = min(abs(CF-FREQUENCIES(m))); n = N(1);
if Dsd_OHC_Loss(m)>dBShift(n,1,end)
Cohc(m) = 0;
else
[a,idx]=sort(abs(squeeze(dBShift(n,1,:))-Dsd_OHC_Loss(m)));
Cohc(m)=COHC(idx(1));
end
OHC_Loss(m) = interp1(COHC,squeeze(dBShift(n,1,:)),Cohc(m),'nearest');
[mag,ind] = sort(abs(COHC-Cohc(m)));
Loss_IHC(m) = dBLoss(m)-OHC_Loss(m);
if dBLoss(m)>dBShift(n,end,ind(1))
Cihc(m) = 0;
else
[c,indx]=sort(abs(squeeze(dBShift(n,:,ind(1)))-dBLoss(m)));
Cihc(m)=CIHC(indx(1));
end
end