THE AUDITORY MODELING TOOLBOX
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exp_osses2022
Comparison of eight monaural models
Program code:
function data = exp_osses2022(varargin)
%exp_osses2022 Comparison of eight monaural models
%
% Usage: data = exp_osses2022(flags)
% data = exp_osses2022(..., 'models', models)
%
%
% The following flags can be specified:
%
% 'fig3' Reproduce Fig. 3.
%
% 'fig4' Reproduce Fig. 4.
%
% 'fig5' Reproduce Fig. 5.
%
% 'fig6' Reproduce Fig. 6.
%
% 'fig7' Reproduce Fig. 7.
%
% 'fig8' Reproduce Fig. 8.
%
% 'fig9' Reproduce Fig. 9.
%
% 'fig10' Reproduce Fig. 10.
%
% 'fig11' Reproduce Fig. 11.
%
% 'fig12a' Reproduce Fig. 12a.
%
% 'fig12b' Reproduce Fig. 12b.
%
% 'fig13' Reproduce Fig. 13.
%
% 'fig14a' Reproduce Fig. 14a.
%
% 'fig14b' Reproduce Fig. 14b.
%
% 'fig15' Reproduce Fig. 15.
%
%
% EXP_OSSES2022(flags) reproduces the figures from Osses et al. (2022), where
% the following models are compared:
%
% - #1: DAU1997, model from Dau et al., (1997).
%
% - #2: ZILANY2014, model from Zilany et al., (2014).
%
% - #3: VERHULST2015, model from Verhulst et al., (2015).
%
% - #4: VERHULST2018, model from Verhulst et al., (2018).
%
% - #5: BRUCE2018, model from Bruce et al., (2018).
%
% - #6: KING2019, model from King et al., (2019).
%
% - #7: RELANOIBORRA2019, model from Relano-Iborra et al., (2019).
%
% - #8: OSSES2021, model from Osses and Kohlrausch (2021).
%
% EXP_OSSES2022(..., 'models', models) can be used to show data for
% specific models only. To this end, models is a vector selecting the models
% by the index as show in the list above. For example if models is [1 3 6]
% then the data for Dau et al., (1997), Verhulst et al., (2015), and King et al.,
% (2019) will be shown only.
%
%
% Additionally, the following flags can be specified:
%
% 'redo' Recomputes data for specified figure. Optional. Default:
% 'normal' which uses the cached data if available.
%
% 'plot' Plot the output of the experiment. This is the default.
%
% 'no_plot' Doesn't plot, only return some data (see code).
%
%
% Figures:
% --------
%
% To display Fig. 3 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig3');
%
% To display Fig. 4 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig4');
%
% To display Fig. 5 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig5');
%
% To display Fig. 6 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig6');
%
% To display Figs. 7 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig7'); % this flag will obtain both figures, 6 and 7
%
% To display Figs. 8 of Osses et al., (2022) use :
% out = exp_osses2022('fig8'); % this flag will obtain both figures, 6 and 7
%
% To display Fig. 9 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig9');
%
% To display Fig. 10 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig10');
%
% To display Fig. 11 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig11');
%
% To display Fig. 12a or 12b of Osses et al., (2022) use :
%
% out = exp_osses2022('fig12a');
% out = exp_osses2022('fig12b');
%
% To display Fig. 13 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig13');
%
% To display Fig. 14a or 14b of Osses et al., (2022) use :
%
% out = exp_osses2022('fig14a');
% out = exp_osses2022('fig14b');
%
% To display Fig. 15 of Osses et al., (2022) use :
%
% out = exp_osses2022('fig15');
% out = exp_osses2022('fig15','redo'); % example where the data are recomputed
%
% References:
% H. Relaño-Iborra, J. Zaar, and T. Dau. A speech-based computational
% auditory signal processing and perception model. The Journal of the
% Acoustical Society of America, 146(5), 2019.
%
% A. Osses and A. Kohlrausch. Perceptual similarity between piano notes:
% Simulations with a template-based perception model. The Journal of the
% Acoustical Society of America, 141(4), 2020.
%
% A. King, L. Varnet, and C. Lorenzi. Accounting for masking of frequency
% modulation by amplitude modulation with the modulation filter-bank
% concept. The Journal of the Acoustical Society of America, 145(2277),
% 2019.
%
% I. C. Bruce, Y. Erfani, and M. S. R. Zilany. A phenomenological model
% of the synapse between the inner hair cell and auditory nerve:
% Implications of limited neurotransmitter release sites. Hearing
% Research, 360:40--54, 2018.
%
% T. Dau, B. Kollmeier, and A. Kohlrausch. Modeling auditory processing
% of amplitude modulation. I. Detection and masking with narrow-band
% carriers. The Journal of the Acoustical Society of America,
% 102:2892--2905, 1997a.
%
% M. S. A. Zilany, I. C. Bruce, and L. H. Carney. Updated parameters and
% expanded simulation options for a model of the auditory periphery. The
% Journal of the Acoustical Society of America, 135(1):283--286, Jan.
% 2014.
%
% S. Verhulst, H. Bharadwaj, G. Mehraei, C. Shera, and
% B. Shinn-Cunningham. Functional modeling of the human auditory
% brainstem response to broadband stimulation. The Journal of the
% Acoustical Society of America, 138(3):1637--1659, 2015.
%
% S. Verhulst, A. Altoè, and V. Vasilkov. Functional modeling of the
% human auditory brainstem response to broadband stimulation. Hearing
% Research, 360:55--75, 2018.
%
% A. Osses Vecchi, L. Varnet, L. H. Carney, T. Dau, I. C. Bruce,
% S. Verhulst, and P. Majdak. A comparative study of eight human auditory
% models of monaural processing. Acta Acustica, 6:17, 2022. Publisher:
% EDP Sciences.
%
%
% See also: dau1997 verhulst2015 verhulst2018 zilany2014 bruce2018 osses2021 king2019 relanoiborra2019
%
% Url: http://amtoolbox.org/amt-1.6.0/doc/experiments/exp_osses2022.php
% #Author: Alejandro Osses (2020-2021): Integration in the AMT
% #Author: Piotr Majdak (2021): Adaptations for the AMT 1.0
% #Author: Alejandro Osses (2023): Updating display options, as of AMT 1.3.1
% #Author: Piotr Majdak (2024): documentation improvements, volatile display improvements for AMT 1.6.
% 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.
data = [];
definput.import={'amt_cache'};
definput.flags.type={'missingflag','fig3','fig4','fig5','fig6','fig7','fig8', ...
'fig9','fig10','fig11','fig12a','fig12b','fig13','fig14a','fig14b','fig15'};
definput.flags.plot={'plot','no_plot'};
definput.keyvals.models=[];
[flags,keyvals] = 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
dispflag = amt_flags;
%%%
FontSize = 13; % FontSize for all figure axes: TODO - apply to all the figures
MediumFontSize = 11;
SmallFontSize = 9;
fs = 100e3; % 100 kHz of sampling rate, this is arbitrary
dBFS = 94; % i.e., amplitude 1 = 1 Pa = 94 dB SPL re 2x10^{-5} Pa
%%% for Figs 3, 4, 5, 6 (confirm for the first figures)
models = {'dau1997','zilany2014','verhulst2015','verhulst2018','bruce2018','king2019','relanoiborra2019','osses2021'};
Colours = {'b',local_rgb('Green'),'r',local_rgb('LightSkyBlue'),local_rgb('Maroon'),'m','k',local_rgb('mediumorchid')};
Markers = {'o','s','<','>','v','^','p','d'};
MarkersSize = [10 10 10 10 10 10 10 10];
LineStyle = {'-','-','-.','-','--','-','-','-'};
LineWidth = [2 2 2 2 2 2 2 2];
if ~isempty(keyvals.models)
warning('Not all models selected');
idxs=keyvals.models;
Markers = Markers(idxs);
MarkersSize = MarkersSize(idxs);
Colours = Colours(idxs);
LineStyle = LineStyle(idxs);
LineWidth = LineWidth(idxs);
models = models(idxs);
end
N_models = length(models);
figure_handle = []; % multiple figures will be generated
figure_name = [];
%% ------ FIG 3 Osses et al. 2021 -----------------------------------------
if flags.do_fig3
% Params for FFT:
K = fs/2; % for FFT
% Memory allocation
h_dB = nan(K,N_models);
hmax_dB = nan(1,N_models);
h0_dB = nan(K,N_models);
k2remove = [];
label2use = [];
for k = 1:N_models
switch models{k}
case {'dau1997','king2019'}
% No middle ear filter
me_type = [];
case {'relanoiborra2019'}
me_type = 'lopezpoveda2001';
label2use{end+1} = 'relanoiborra2019, osses2021';
case 'osses2021'
% uses the same as relanoiborra (no need to calculate it again)
me_type = [];
case 'zilany2014'
me_type = 'zilany2009';
label2use{end+1} = 'zilany2014, bruce2018';
case 'bruce2018'
me_type = [];
case 'verhulst2015'
me_type = 'verhulst2015';
label2use{end+1} = 'verhulst2015';
case 'verhulst2018'
me_type = 'verhulst2018';
label2use{end+1} = 'verhulst2018';
end
if ~isempty(me_type)
[b,a] = middleearfilter(fs,me_type);
Nr_cascaded = size(b,1);
if Nr_cascaded == 1 % no cascaded
[h,w]=freqz(b,a,K);
else
h = ones(K,1);
for j = 1:Nr_cascaded
[htmp,w]=freqz(b(j,:),a(j,:),K);
h = h.*htmp;
end
end
f = w/pi*(fs/2); % freq. of the FFT bins in Hz
h_dB(:,k) = 20*log10(abs(h));
hmax_dB(k) = max(h_dB(:,k));
h0_dB(:,k) = h_dB(:,k) - hmax_dB(k);
else
k2remove = [k2remove k];
end
end
% Preparing the plots with the four middle ear filters:
models(k2remove) = [];
Colours(k2remove) = [];
Markers(k2remove) = [];
MarkersSize(k2remove) = [];
LineStyle(k2remove) = [];
LineWidth(k2remove) = [];
h_dB(:,k2remove) = [];
hmax_dB(k2remove) = [];
h0_dB(:,k2remove) = [];
%%%
b_oear = headphonefilter(fs); % Pralong filter
htmp=freqz(b_oear,1,K);
hOuter_ear = 20*log10(abs(htmp));
%%%
if flags.do_plot
pl = [];
figure;
end
for k = 1:length(models)
if flags.do_plot
opts_plot = {'Color',Colours{k},'LineWidth',LineWidth(k),'LineStyle',LineStyle{k}};
pl(end+1) = semilogx(f,h0_dB(:,k),opts_plot{:}); hold on, grid on;
if strcmp(models{k},'relanoiborra2019')
hCombined = h0_dB(:,k) + hOuter_ear;
semilogx(f,hCombined,'--','Color',[0.5 0.5 0.5],'LineWidth',4); hold on, grid on;
%%% Plotting again
semilogx(f,h0_dB(:,k),opts_plot{:}); hold on, grid on;
end
end
L1 = [min(f) max(f); -3 -3];
L2 = [f'; h0_dB(:,k)'];
P = local_InterX(L1,L2);
if ~isempty(P)
f_min03_low(k) = P(1,1);
f_min03_high(k) = P(1,end);
else
f_min03_low(k) = nan;
f_min03_high(k) = nan;
end
L1 = [min(f) max(f); -15 -15];
P = local_InterX(L1,L2);
if ~isempty(P)
f_min15_low(k) = P(1,1);
f_min15_high(k) = P(1,end);
else
f_min15_low(k) = nan;
f_min15_high(k) = nan;
end
end
if flags.do_plot
xlim([80 30000]);
hl = legend([pl(2:end) pl(1)],label2use);
set(hl ,'FontSize',SmallFontSize);
set(gca,'FontSize',FontSize);
f2tick = [63 125 250 500 1000 2000 4000 8000 16000];
set(gca,'XTick',f2tick);
set(gca,'YTick',-21:3:3);
ylim([-19.5 4.5])
xlabel('Frequency (Hz)');
ylabel('Amplitude (dB re. max)');
Pos = get(gcf,'Position');
Pos(3:4) = [640 330]; % fix width
set(gcf,'Position',Pos);
data.figure_flag = 'do_fig3';
data.figure_handle = gcf;
data.figure_name = 'fig03-me-resp'; % middle ear response
end
data.description = 'Frequency response of the middle ears used in the selected models';
data.hmax_dB = hmax_dB;
data.f_min03_low = f_min03_low;
data.f_min03_high = f_min03_high;
data.f_min15_low = f_min15_low;
data.f_min15_high = f_min15_high;
data.evaluated_models = models;
end
%% ------ FIG 4 Excitation patterns (Fig. 5) / FFT resp. (Fig. 6)----------
if flags.do_fig4
% 1. Top panels, based on data from Fig. 6: ---------------------------
data5 = exp_osses2022('fig5','no_plot','models',keyvals.models); % data from Fig. 5
vref = data5.v_ref;
idx_models = 1:length(models);
idx2remove = find(strcmp(models,'bruce2018'));
idx_models(idx2remove) = [];
lvls = data5.lvls;
lvls2look = [40 70 100];
for i = 1:length(lvls2look)
idxs2look(i) = find(lvls==lvls2look(i));
end
models = data5.models;
K = length(models);
if flags.do_plot
Markers = {'s','s','s','s','s','s','s','s'};
if length(idx_models) ~= length(Markers) % it will always be the case (bruce2018 is not processed)
% if true, then the user requested a subset of the models
Markers = Markers(idx_models);
Colours = Colours(idx_models);
end
num_rows = 4;
num_cols = 8;
YL = [-95 12];
ha1 = [];
ha2 = [];
str_extra = {'a)','b)','c)','d)','e)','f)','g)'};
for k = 1:K
models_str{k} = [str_extra{k} ' ' models{k}];
end
format_str = {'','-','-'};
Colour_low1 = [0.8 0.8 0.8];
Colour_high1 = [1 1 0]; % rgb('Yellow')
Colour_low2 = [0.6 0.6 0.6];
Colour_high2 = [1 0.6445 0]; % rgb('Orange');
Ylabel = 'Output level (dB)';
Xlabel = 'Frequency (Hz)';
end
flow1 = audtofreq(freqtoaud(500)-1);
fhigh1 = audtofreq(freqtoaud(500)+1);
flow2 = audtofreq(freqtoaud(4000)-1);
fhigh2 = audtofreq(freqtoaud(4000)+1);
fm1ERB = flow1; % 425;
fp1ERB = fhigh1; % 583;
fm1ERB = freqtoaud(fm1ERB); % converting from Hz to ERB_N
fp1ERB = freqtoaud(fp1ERB); % converting from Hz to ERB_N
i = 2; % i = 2 is the index of the tested 500 Hz tone
if flags.do_plot
figure;
end
for idx_lvl = idxs2look
for k = 1:K
fc_resp = data5.data_fc_resp{k,i};
fc_resp = freqtoaud(fc_resp); % converting from Hz to ERB_N
freq_resp = data5.data_out_freq_resp_all{k,i}(:,idx_lvl);
curve = freq_resp - vref(k);
if flags.do_plot
subplot(num_rows,num_cols,k)
plot(fc_resp,curve,format_str{i},'Color',Colours{k},'LineWidth',2); hold on; grid on;
end
L1 = [fc_resp(:) curve(:)]';
L2 = [fm1ERB*[1 1]; min(curve)-3 max(curve)+3];
P = local_InterX(L1,L2);
if dispflag.do_verbose
amt_disp(sprintf('%s: freq=%.0f, amp=%.1f dB re max',models{k},fm1ERB,P(2)));
end
if flags.do_plot
plot(P(1),P(2),'Color',Colours{k},'Marker',Markers{k},'MarkerFaceColor',Colour_low1,'LineWidth',1,'MarkerSize',6);
end
L2 = [fp1ERB*[1 1]; min(curve)-3 max(curve)+3];
P = local_InterX(L1,L2);
if dispflag.do_verbose
amt_disp(sprintf('%s: freq=%.0f, amp=%.1f dB re max',models{k},fp1ERB,P(2)));
end
if flags.do_plot
plot(P(1),P(2),'Color',Colours{k},'Marker',Markers{k},'MarkerFaceColor',Colour_high1,'LineWidth',1,'MarkerSize',6);
title(models{k});
ylabel(Ylabel);
if idx_lvl == 5
ha1(end+1) = gca;
% plot(flow1*[1 1],YL,'k-');
% plot(fhigh1*[1 1],YL,'k-');
end
end
end
end
if flags.do_plot
subplot(num_rows,num_cols,8)
semilogx(fc_resp,nan(size(freq_resp)));
ha1(end+1) = gca;
ylabel(Ylabel);
end
i = 3; % i = 3 is the index of the tested 4000 Hz tone
fm1ERB = flow2; % 3567;
fp1ERB = fhigh2; % 4482;
fm1ERB = freqtoaud(fm1ERB); % converting from Hz to ERB_N
fp1ERB = freqtoaud(fp1ERB); % converting from Hz to ERB_N
for idx_lvl = idxs2look
for k = 1:K
fc_resp = data5.data_fc_resp{k,i};
fc_resp = freqtoaud(fc_resp);
freq_resp = data5.data_out_freq_resp_all{k,i}(:,idx_lvl);
curve = freq_resp - vref(k);
if flags.do_plot
subplot(num_rows,num_cols,k + num_cols)
plot(fc_resp,curve,format_str{i},'Color',Colours{k},'LineWidth',2); hold on; grid on;
end
L1 = [fc_resp(:) curve(:)]';
L2 = [fm1ERB*[1 1]; min(curve)-3 max(curve)+3];
P = local_InterX(L1,L2);
if dispflag.do_verbose
amt_disp(sprintf('%s: freq=%.0f, amp=%.1f dB re max',models{k},fm1ERB,P(2)));
end
if flags.do_plot
plot(P(1),P(2),'Color',Colours{k},'Marker',Markers{k},'MarkerFaceColor',Colour_low2,'LineWidth',1,'MarkerSize',6);
end
L2 = [fp1ERB*[1 1]; min(curve)-3 max(curve)+3];
P = local_InterX(L1,L2);
if dispflag.do_verbose
amt_disp(sprintf('%s: freq=%.0f, amp=%.1f dB re max',models{k},fp1ERB,P(2)));
end
if flags.do_plot
plot(P(1),P(2),'Color',Colours{k},'Marker',Markers{k},'MarkerFaceColor',Colour_high2,'LineWidth',1,'MarkerSize',6);
if k == 4
xlabel(Xlabel);
end
ylabel(Ylabel)
if idx_lvl == 5
ha1(end+1) = gca;
% plot(flow2*[1 1],YL,'k-');
% plot(fhigh2*[1 1],YL,'k-');
end
end
end
end
if flags.do_plot
subplot(num_rows,num_cols,16)
semilogx(fc_resp,nan(size(freq_resp)));
ha1(end+1) = gca;
ylabel(Ylabel);
end
data6 = exp_osses2022('fig6','no_plot','models',keyvals.models); % data from Fig. 6
%%% Remove 'data_fc_resp'
% data_out_freq_resp_basef
lvls = [40 70 100]; % These were the tested levels (a priori knowledge)
fc2look = [500 4000];
idx_f0 = 1; % maybe remove
for ii = 1:length(fc2look)
for k = 1:K
P1 = [];
P2 = [];
val = [];
idx = [];
for idx_lvl = 1:3
fc_resp = data6.data_fig4.ff{k};
switch fc2look(ii)
case 500
freq_resp_MA = data6.data_fig4.freq_resp_MA0500{k}(:,idx_lvl);
freq_resp = data6.data_fig4.freq_resp0500{k}(:,idx_lvl);
case 4000
freq_resp_MA = data6.data_fig4.freq_resp_MA4000{k}(:,idx_lvl);
freq_resp = data6.data_fig4.freq_resp4000{k}(:,idx_lvl);
otherwise
error('Only tested using f=500 and 4000 Hz');
end
fc_resp = freqtoaud(fc_resp);
% freq_resp = data5.data_out_freq_resp_all{k,idx_lvl};
curve_broad = freq_resp - vref(k);
if flags.do_plot
subplot(num_rows,num_cols,k + num_cols*(ii+1)) % ii+1 = 3 for 500 Hz and ii+1 = 4 for 4000 Hz
% i.e., third and fourth row of plots
plot(fc_resp,curve_broad,'-','Color',.9*[1 1 1],'LineWidth',3.5); hold on; grid on;
end
fc_resp_basef = fc_resp;
freq_resp_basef = freq_resp_MA; % data5.data_out_freq_resp_basef{k,idx_lvl};
curve = freq_resp_basef(:,idx_f0) - vref(k);
if flags.do_plot
plot(fc_resp_basef,curve,'-','Color',Colours{k},'LineWidth',2); hold on;
end
L1 = [fc_resp_basef(:) curve(:)]';
L2 = [min(fc_resp) max(fc_resp); max(curve)-3 max(curve)-3];
P = local_InterX(L1,L2);
pp = audtofreq(P(1,:));
if dispflag.do_verbose
fc_here = (pp(2)+pp(1))/2;
bw_here = pp(2)-pp(1);
amt_disp(sprintf('%s: fc=%.0f Hz, BW=%.1f Hz',models{k},fc_here,bw_here));
end
if fc2look(ii) == 4000
switch models{k}
case {'verhulst2015','verhulst2018','relanoiborra2019'}
[val(idx_lvl),idx(idx_lvl)] = max(curve);
end
end
%%%
P1(end+1,:) = P(1,1:2);
P2(end+1,:) = P(2,1:2);
if flags.do_plot
plot(P(1,:),P(2,:),'k-','LineWidth',1.5); hold on;
if fc2look(ii) == 4000
if k == 4
xlabel(Xlabel);
end
end
ylabel(Ylabel);
if idx_lvl == 1
ha2(end+1) = gca;
end
end
end
if flags.do_plot
if fc2look(ii) == 4000
if ~isempty(val)
yoff = +5; % off
for i = 1:3
plot(fc_resp_basef(idx(i)),val(i)+yoff,'kv','MarkerFaceColor','g','MarkerSize',6); hold on
end
end
end
for i = 1:2
plot(P1(1,i)*[1 1],[P2(1,i) -120],'k--','LineWidth',1.5);
plot(P1(2,i)*[1 1],[P2(2,i) P2(1,i)],'k--','LineWidth',1.5);
plot(P1(3,i)*[1 1],[P2(3,i) P2(2,i)],'k--','LineWidth',1.5);
end
end
end
if flags.do_plot
subplot(num_rows,num_cols,8+num_cols*(ii+1)) % Extra empty panel
semilogx(fc_resp,nan(size(freq_resp)));
ha2(end+1) = gca;
ylabel(Ylabel);
end
end
%%%
if flags.do_plot
XL = [250 8000];
XL = freqtoaud(XL);
linkaxes([ha1 ha2],'x');
xlim(XL)
set([ha1 ha2],'YTick',-120:20:0)
linkaxes(ha1,'xy');
set(ha1,'YLim',YL);
linkaxes(ha2,'xy');
set(ha2,'YLim',YL-20);
XT_Hz = [audtofreq(freqtoaud(500)-1) 500 audtofreq(freqtoaud( 500)+1) ...
audtofreq(freqtoaud(4000)-1) 4000 audtofreq(freqtoaud(4000)+1)];
XT = freqtoaud(XT_Hz);
XTL = {'' '500' '' '' '4000' ''};
set([ha1 ha2],'XTick',XT)
set([ha1 ha2],'XTickLabel',XTL)
Pos = get(gcf,'Position');
Pos(3:4) = [1325 700];
set(gcf,'Position',Pos);
figure_handle = gcf;
figure_name = 'fig04-freq-responses';
data.figure_flag = 'do_fig5';
data.figure_handle = figure_handle;
data.figure_name = figure_name;
end
end
%% ------ FIG 5 Osses et al. 2021 -----------------------------------------
if flags.do_fig5
k2remove = [];
N_models_here = N_models; % Bruce2018, is removed later...
%%% 1. Generating the input signals: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Signal parameters:
dur=100e-3; % 50 ms
lvls=0:10:100; % Level of the test signals
N_lvls = length(lvls);
f0s = [1000 500 4000];
dt = 1/fs; % \Delta t in s
t=0:dt:dur-dt;
cfs = local_m2hz(401); % default characteristic frequencies from Verhulst2012
Pos34 = [500 350];
for i = 1:length(f0s)
f0_target = f0s(i);
if f0_target == 1000
idx_lvls = find(lvls==100); % 0-dB reference
else
idx_lvls = 1:N_lvls;
end
idx = find(cfs>f0_target,1,'last');
f0 = cfs(idx);
f0_lo = audtofreq(freqtoaud(f0_target)-1); % min 1 ERB
idx_off(1) = find(cfs>f0_lo,1,'last');
f0_hi = audtofreq(freqtoaud(f0_target)+1); % plus 1 ERB
idx_off(2) = find(cfs>f0_hi,1,'last');
if dispflag.do_verbose
amt_disp(sprintf('actual f0=%.1f Hz using Verhulst''s mapping (bin=%.0f)',f0,idx));
amt_disp(sprintf('f0 off=%.1f Hz (bin=%.0f)',f0_lo,idx_off(1)));
amt_disp(sprintf('f0 off=%.1f Hz (bin=%.0f)',f0_hi,idx_off(2)));
end
% Basis input signal
insig = zeros(length(t),N_lvls); % Memory allocation
insig_orig = sin(2*pi*f0*t(:)); % the same sinusoid will be scaled at different levels
% Up/down cosine ramp (fixed)
dur_ramp_ms = 10;
dur_ramp = round((dur_ramp_ms*1e-3)*fs); % duration ramp in samples
rp = ones(size(insig_orig));
rp(1:dur_ramp) = rampup(dur_ramp);
rp(end-dur_ramp+1:end) = rampdown(dur_ramp);
%%%
% Calibration of the input signals:
for j = idx_lvls
insig(:,j) = scaletodbspl(insig_orig,lvls(j),dBFS); % setdbspl
insig(:,j) = rp.*insig(:,j);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
N = length(lvls(idx_lvls));
vrms = nan(N,N_models); % for the on-frequency values
vrms_off_lo = nan(N,N_models); % for the off-frequency values
vrms_off_hi = nan(N,N_models); % for the off-frequency values
k_test = 1;
insig_here = insig(:,idx_lvls);
for k = 1:N_models_here
%%% Loading flags and keyvals
[fg,kv] = local_get_flags(models{k});
fc_flags = fg.fc_flags;
afb_flags = fg.afb_flags;
afb_kv = kv.afb_keyvals;
% ihc_flags = fg.ihc_flags;
noihc_flags = fg.noihc_flags;
noan_flags = fg.noan_flags; % No auditory nerve module
% nomfb_flags = fg.nomfb_flags;
fname = ['fig05_' models{k} '-f0-' num2str(f0_target) '-Hz'];
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
bRun = 0;
out_all = c.out_all;
out_lo_all = c.out_lo_all;
out_hi_all = c.out_hi_all;
if isfield(c,'out_freq_resp_all')
fc_resp = c.fc_resp;
out_freq_resp_all = c.out_freq_resp_all;
end
if k == 1
fc_resp_ref = fc_resp;
end
data_fc_resp{k,i} = fc_resp;
data_out_freq_resp_all{k,i} = out_freq_resp_all;
else
bRun = 1;
out_all = [];
out_lo_all = [];
out_hi_all = [];
out_freq_resp_all = [];
switch models{k}
case {'verhulst2015','verhulst2018'}
% Nothing to do
case 'dau1997'
% Nothing to do
otherwise
fc_resp = fc_resp_ref;
end
end
if bRun
flow_here = 80;
fhigh_here = 8000;
bwmul_here = 0.125; % extra resolution for dau1997, osses2021
fc_kv_extra = {'bwmul',bwmul_here};
amt_disp(['Calculating ' models{k} '...']);
switch models{k}
case 'dau1997'
for j = 1:N
fc_kv = {'flow',f0,'fhigh',f0,'basef',f0,'dboffset',dBFS};
out = dau1997(insig_here(:,j),fs,fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_all(:,end+1) = out;
fc_kv = {'flow',f0_lo,'fhigh',f0_lo,'basef',f0_lo,'dboffset',dBFS};
out = dau1997(insig_here(:,j),fs,fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_lo_all(:,end+1) = out;
fc_kv = {'flow',f0_hi,'fhigh',f0_hi,'basef',f0_hi,'dboffset',dBFS};
out = dau1997(insig_here(:,j),fs,fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_hi_all(:,end+1) = out;
fc_kv = {'flow',flow_here,'fhigh',fhigh_here,'basef',f0,'dboffset',dBFS};
[out,fc_resp] = dau1997(insig_here(:,j),fs,fc_kv{:},fc_kv_extra{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_freq_resp_all(:,end+1) = local_rmsdb(out);
fc_resp_ref = fc_resp;
end
case 'zilany2014'
kv={'numH',0,'numM',0,'numL',0,'fiberType',4,'nrep',1};
for j = 1:N
[~,~,~,c1,c2] = zilany2014(insig_here(:,j),fs,f0,kv{:},afb_flags{:},'silent');
idxs = 1:length(insig_here); % to remove the zero padding
out = c1(:)+c2(:);
out = out(idxs);
out_all(:,end+1) = out;
[~,~,~,c1,c2] = zilany2014(insig_here(:,j),fs,f0_lo,kv{:},afb_flags{:},'silent');
out = c1(:)+c2(:);
out = out(idxs);
out_lo_all(:,end+1) = out;
[~,~,~,c1,c2] = zilany2014(insig_here(:,j),fs,f0_hi,kv{:},afb_flags{:},'silent');
out = c1(:)+c2(:);
out = out(idxs);
out_hi_all(:,end+1) = out;
out_tmp = [];
for kk = 1:length(fc_resp)
fc_here = fc_resp(kk);
if fc_here >= 125
[~,~,~,c1,c2] = zilany2014(insig_here(:,j),fs,fc_here, kv{:},afb_flags{:},'silent');
out = c1(:)+c2(:);
out = out(idxs);
out_tmp(:,end+1) = out;
else
last_idx_nan = kk;
end
end
out_tmp = local_rmsdb(out_tmp);
out_freq_resp_all(:,end+1) = out_tmp(:);
end
nan_array = nan(last_idx_nan, size(out_freq_resp_all,2));
out_freq_resp_all = [nan_array; out_freq_resp_all];
case 'verhulst2015'
% The default afb_flag related to velocity is 'no_v'.
% For this specific figure we actually need 'v'. For
% this reason, we replace 'no_v' by 'v':
idx_here = find(strcmp(afb_flags,'no_v'));
if ~isempty(idx_here)
afb_flags{idx_here} = 'v'; % overwritting 'no_v' by 'v':
end
out_model = verhulst2015(insig_here,fs,fc_flags{:},afb_flags{:},noihc_flags{:},noan_flags{:},'silent');
for j = 1:N
out = out_model(j).v(:,idx);
out_all(:,end+1) = out;
out = out_model(j).v(:,idx_off(1));
out_lo_all(:,end+1) = out;
out = out_model(j).v(:,idx_off(2));
out_hi_all(:,end+1) = out;
if j == 1
fc_resp = out_model(j).cf;
end
out_freq_resp_all(:,end+1) = local_rmsdb(out_model(j).v);
end
case 'verhulst2018'
% The default afb_flag related to velocity is 'no_v'.
% For this specific figure we actually need 'v'. For
% this reason, we replace 'no_v' by 'v':
idx_here = find(strcmp(afb_flags,'no_v'));
if ~isempty(idx_here)
afb_flags{idx_here} = 'v'; % overwritting 'no_v' by 'v':
end
out_model = verhulst2018(insig_here,fs,fc_flags{:},afb_flags{:},noihc_flags{:},noan_flags{:},'silent');
for j = 1:N
out = out_model(j).v(:,idx);
out_all(:,end+1) = out;
out = out_model(j).v(:,idx_off(1));
out_lo_all(:,end+1) = out;
out = out_model(j).v(:,idx_off(2));
out_hi_all(:,end+1) = out;
if j == 1
fc_resp = out_model(j).cf;
end
out_freq_resp_all(:,end+1) = local_rmsdb(out_model(j).v);
end
case 'bruce2018'
% bruce2018 has the same preprocessing as zilany2014
if i == 1
k2remove = [k2remove k];
end
case 'king2019'
for j = 1:N
fc_kv = {'flow',f0,'fhigh',f0,'basef',f0,'dboffset',dBFS};
out = king2019(insig_here(:,j),fs,afb_kv{:},fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_all(:,end+1) = out;
fc_kv = {'flow',f0_lo,'fhigh',f0_lo,'basef',f0_lo,'dboffset',dBFS};
out = king2019(insig_here(:,j),fs,afb_kv{:},fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_lo_all(:,end+1) = out;
fc_kv = {'flow',f0_hi,'fhigh',f0_hi,'basef',f0_hi,'dboffset',dBFS};
out = king2019(insig_here(:,j),fs,afb_kv{:},fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_hi_all(:,end+1) = out;
fc_kv = {'flow',flow_here,'fhigh',fhigh_here,'basef',f0,'dboffset',dBFS};
[out,fc_resp] = king2019(insig_here(:,j),fs,afb_kv{:},fc_kv{:},fc_kv_extra{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_freq_resp_all(:,end+1) = local_rmsdb(out);
end
case 'relanoiborra2019'
for j = 1:N
fc_kv = {'flow',f0,'fhigh',f0,'basef',f0,'no_ihc','no_an'};
[~,~,out] = relanoiborra2019_featureextraction(insig_here(:,j),fs,fc_kv{:},afb_flags{:});
out_all(:,end+1) = out;
fc_kv = {'flow',f0_lo,'fhigh',f0_lo,'basef',f0_lo,'no_ihc','no_an'};
[~,~,out] = relanoiborra2019_featureextraction(insig_here(:,j),fs,fc_kv{:},afb_flags{:});
out_lo_all(:,end+1) = out;
fc_kv = {'flow',f0_hi,'fhigh',f0_hi,'basef',f0_hi,'no_ihc','no_an'};
[~,~,out] = relanoiborra2019_featureextraction(insig_here(:,j),fs,fc_kv{:},afb_flags{:});
out_hi_all(:,end+1) = out;
fc_kv = {'flow',flow_here,'fhigh',fhigh_here,'basef',f0,'no_ihc','no_an'};
[~,~,out,fc_resp] = relanoiborra2019_featureextraction(insig_here(:,j),fs,fc_kv{:},fc_kv_extra{:},afb_flags{:});
out_freq_resp_all(:,end+1) = local_rmsdb(out);
end
case 'osses2021'
for j = 1:N
fc_kv = {'flow',f0,'fhigh',f0,'basef',f0,'dboffset',dBFS};
out = osses2021(insig_here(:,j),fs,fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_all(:,end+1) = out;
fc_kv = {'flow',f0_lo,'fhigh',f0_lo,'basef',f0_lo,'dboffset',dBFS};
out = osses2021(insig_here(:,j),fs,fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_lo_all(:,end+1) = out;
fc_kv = {'flow',f0_hi,'fhigh',f0_hi,'basef',f0_hi,'dboffset',dBFS};
out = osses2021(insig_here(:,j),fs,fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_hi_all(:,end+1) = out;
fc_kv = {'flow',flow_here,'fhigh',fhigh_here,'basef',f0,'dboffset',dBFS};
[out,fc_resp] = osses2021(insig_here(:,j),fs,fc_kv{:},fc_kv_extra{:},afb_flags{:},noihc_flags{:},noan_flags{:});
out_freq_resp_all(:,end+1) = local_rmsdb(out);
end
end
if ~isempty(out_all)
c = [];
c.out_all = out_all;
c.out_lo_all = out_lo_all;
c.out_hi_all = out_hi_all;
c.fc_resp = fc_resp;
c.out_freq_resp_all = out_freq_resp_all;
amt_cache('set',fname,c);
data_fc_resp{k,i} = fc_resp;
data_out_freq_resp_all{k,i} = out_freq_resp_all;
end
end
if ~isempty(out_all)
% Now calculation of RMS values:
for j = 1:N
vrms(j,k) = local_rmsdb(out_all(:,j));
vrms_off_lo(j,k) = local_rmsdb(out_lo_all(:,j));
vrms_off_hi(j,k) = local_rmsdb(out_hi_all(:,j));
end
end
end
if i == 1
% Preparing the plots with the four middle ear filters:
models(k2remove) = [];
Colours(k2remove) = [];
Markers(k2remove) = [];
MarkersSize(k2remove) = [];
LineStyle(k2remove) = [];
LineWidth(k2remove) = [];
N_models_here = length(models); % number of models after removing
vrms(:,k2remove) = [];
vrms_off_lo(:,k2remove) = [];
vrms_off_hi(:,k2remove) = [];
data_fc_resp(k2remove) = [];
data_out_freq_resp_all(k2remove) = [];
end
if f0_target == 1000
v_ref_0_dB = vrms;
% v_ref_0_dB = [ -7.3561 -44.0567 -77.9298 -101.8625 -44.0567]; % calculated at 1 kHz
data.v_ref = v_ref_0_dB;
else
if f0_target == 500
prefix4title = {'a) ','b) ','c) '};
elseif f0_target == 4000
prefix4title = {'d) ','e) ','f) '};
end
v_ref = v_ref_0_dB;
if flags.do_plot
Format = [];
for k = 1:N_models_here
Format{k} = {'Color',Colours{k},'LineStyle',LineStyle{k}, ...
'LineWidth',LineWidth(k),'Marker',Markers{k},'MarkerFaceColor','w', ...
'MarkerSize',MarkersSize(k)};
end
%%% End formatting options
figure;
for k = 1:N_models_here
pl(k)=plot(lvls,vrms(:,k)-v_ref(k),Format{k}{:});
grid on, hold on;
end
title(sprintf('\n%sCF_n at %.0f Hz, on-freq. (n=%.0f)',prefix4title{1},f0,idx));
xlabel('Input level (dB SPL)');
ylabel('Output level (dB)');
ylim([-103 13])
xlim([min(lvls)-5 max(lvls)+5])
set(gca,'XTick', 0:10:100)
set(gca,'YTick',-100:10:10)
figure_handle(end+1) = gcf;
figure_name{end+1} = sprintf('fig05-IO-at-%.0f-Hz',f0);
Pos = get(gcf,'Position');
Pos(3:4) = Pos34;
set(gcf,'Position',Pos);
%%%%
figure;
for k = 1:N_models_here
plot(lvls,vrms_off_lo(:,k)-v_ref(k),Format{k}{:});
grid on, hold on
end
title(sprintf('\n%sCF_n at %.0f Hz, off-freq. (n=%.0f)',prefix4title{2},f0_lo,idx_off(1)));
xlabel('Input level (dB SPL)');
ylabel('Output level (dB)');
ylim([-103 13])
xlim([min(lvls)-5 max(lvls)+5])
set(gca,'XTick', 0:10:100)
set(gca,'YTick',-100:10:10)
figure_handle(end+1) = gcf;
figure_name{end+1} = sprintf('fig05-IO-at-%.0f-Hz-off-lo',f0);
Pos = get(gcf,'Position');
Pos(3:4) = Pos34;
set(gcf,'Position',Pos);
figure;
for k = 1:N_models_here
plot(lvls,vrms_off_hi(:,k)-v_ref(k),Format{k}{:});
grid on, hold on
end
title(sprintf('\n%sCF_n at %.0f Hz, off-freq. (n=%.0f)',prefix4title{3},f0_hi,idx_off(2)));
xlabel('Input level (dB SPL)');
ylabel('Output level (dB)');
ylim([-103 13])
xlim([min(lvls)-5 max(lvls)+5])
set(gca,'XTick', 0:10:100)
set(gca,'YTick',-100:10:10)
Pos = get(gcf,'Position');
Pos(3:4) = Pos34;
set(gcf,'Position',Pos);
if i == length(f0s)
text4leg = models;
if strcmp(models{2},'zilany2014')
text4leg{2} = [text4leg{2} ',bruce2018'];
end
hl = legend(text4leg,'Location','SouthEast');
set(hl,'FontSize',8);
end
figure_handle(end+1) = gcf;
figure_name{end+1} = sprintf('fig05-IO-at-%.0f-Hz-off-hi',f0);
end
data(k_test).vrms = vrms;
data(k_test).vrms_off_lo = vrms_off_lo;
data(k_test).vrms_off_hi = vrms_off_hi;
data(k_test).f0 = f0;
k_test = k_test+1;
end
end
if flags.do_plot
data.figure_flag = 'do_fig5';
data.figure_handle = figure_handle;
data.figure_name = figure_name;
end
data.models = models;
data.v_ref_0_dB = v_ref_0_dB;
data.insig = insig;
data.insig_orig = insig_orig;
data.fs = fs;
data.ramp = rp;
data.dBFS = dBFS;
data.lvls = lvls;
data.data_fc_resp = data_fc_resp;
data.data_out_freq_resp_all = data_out_freq_resp_all;
end
%% ------ FIG 6 Frequency selectivity ------------------------------------
if flags.do_fig6
k2remove = [];
fsig = 4000; % Hz
basef = fsig;
dt = 1/fs;
lvls=[40 70 100];
N_lvls = length(lvls);
flow = 20;
fhigh = 10000;
BW = fhigh-flow;
fc = BW/2;
dur_stim_total = 3; % s, Total duration of the stimulus
bCreate_insig = 1;
fname = 'fig06_dau1997-QERB-40';
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
if isfield(c,'insig_not_scaled')
insig_not_scaled = c.insig_not_scaled;
bCreate_insig = 0;
end
end
if bCreate_insig == 1
insig_not_scaled = sig_bandpassnoise(fc,fs,dur_stim_total,50,BW);
end
% Bandpass noise (Stimulus bType = 2 in previous versions of this script)
dur_each_section = 0.5; % s
N_sections = dur_stim_total/dur_each_section;
% Up/down cosine ramp (fixed)
dur_ramp_ms = 10; % in ms
dur_ramp = round((dur_ramp_ms*1e-3)*fs); % duration ramp in samples
N_samples = round(dur_stim_total*fs);
rp = ones(N_samples,1);
rp(1:dur_ramp) = rampup(dur_ramp);
rp(end-dur_ramp+1:end) = rampdown(dur_ramp);
for i=1:N_lvls
lvl_tot = lvls(i); % + 10*log10(BW);
insigs(:,i) = rp.*scaletodbspl(insig_not_scaled,lvl_tot,dBFS);
end
% End: generating bandpass noise
cf = local_m2hz(401); % Get characteristic frequencies from Verhulst models
cf_max = 10000;
cf_min = 125;
%%%
N_sigs = 31; % Approximate number of bands
bin_cf_max = find(cf>cf_max,1,'last');
bin_cf_min = find(cf>cf_min,1,'last');
df_bin = floor(abs(bin_cf_max-bin_cf_min)/N_sigs);
idx_cf = [bin_cf_min:-df_bin:bin_cf_max 305 112]; % 305 - bin for 502 Hz, 112 bin for 4013 Hz
idx_cf = sort(idx_cf,'descend');
fc_ref = cf(idx_cf);
N_sigs = length(fc_ref); % Exact number of bands
for k = 1:N_models
%%% Loading flags and keyvals
[fg,kv] = local_get_flags(models{k});
fc_flags = fg.fc_flags;
afb_flags = fg.afb_flags;
afb_kv = kv.afb_keyvals;
noihc_flags = fg.noihc_flags;
noan_flags = fg.noan_flags; % No auditory nerve module
nomfb_flags = fg.nomfb_flags;
for j = 1:N_lvls
fname = ['fig06_' models{k} '-QERB-' num2str(lvls(j))];
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
bRun = 0;
outsig = c.outsig;
fc = c.fc;
if isfield(c,'insig_not_scaled')
% Only for dau1997:
insig_not_scaled = c.insig_not_scaled;
end
else
bRun = 1;
outsig = [];
end
if bRun
amt_disp(['Calculating ' models{k} '...']);
switch models{k}
case 'dau1997'
for ii = 1:N_sigs
fc_kv = {'flow',fc_ref(ii),'fhigh',fc_ref(ii),'basef',fc_ref(ii),'dboffset',dBFS};
[outsig(:,ii),fc(ii)] = dau1997(insigs(:,j),fs,fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
end
case 'zilany2014'
outsig = [];
kv={'nrep',1}; % kv={'numH',0,'numM',0,'numL',0,'fiberType',4,'nrep',1,'reptime',1.2};
for ii = 1:N_sigs
[~,~,~,c1,c2] = zilany2014(insigs(:,j),fs,fc_ref(ii),kv{:},afb_flags{:},'silent');
if ii == 1
L = size(insigs,1);
end
outsig(:,ii) = c1(1:L)+c2(1:L);
end
case 'verhulst2015'
%idx = find(strcmp(afb_flags,'no_v'));
%if ~isempty(idx)
% afb_flags{idx} = 'v'; % overwritting 'no_v' by 'v':
%end
outs = verhulst2015(insigs(:,j),fs,fc_flags{:},afb_flags{:},'v',noihc_flags{:},noan_flags{:},'silent');
if j == 1
CF_here = outs.cf;
end
outsig = [];
for ii = 1:N_sigs
outsig(:,ii) = outs.v(:,idx_cf(ii));
end
case 'verhulst2018'
%idx = find(strcmp(afb_flags,'no_v'));
%if ~isempty(idx)
% afb_flags{idx} = 'v'; % overwritting 'no_v' by 'v':
% For most of the simulations in this script 'v' provides redundant (big-sized) information
% for this reason the 'no_v' is only for this figure overwritten to 'v', because BM velocity
% is what we actually need to obtain this figure.
%end
outs = verhulst2018(insigs(:,j),fs,fc_flags{:},afb_flags{:},'v',noihc_flags{:},noan_flags{:},'silent');
if j == 1
CF_here = outs.cf;
end
outsig = [];
for ii = 1:N_sigs
outsig(:,ii) = outs.v(:,idx_cf(ii));
end
case 'bruce2018'
% already in zilany2014
if j == 1
k2remove = [k2remove k];
end
outsig = [];
fc = [];
case 'king2019'
for ii = 1:N_sigs
fc_kv = {'flow',fc_ref(ii),'fhigh',fc_ref(ii),'basef',fc_ref(ii),'dboffset',dBFS};
[outsig(:,ii),fc(ii)] = king2019(insigs(:,j),fs,fc_kv{:},afb_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
end
case 'relanoiborra2019'
for ii = 1:N_sigs
fc_kv = {'flow',fc_ref(ii),'fhigh',fc_ref(ii),'basef',fc_ref(ii),'erbspacebw','no_internalnoise','no_ihc','no_an'};
[~,~,outsig(:,ii),fc(ii)] = relanoiborra2019_featureextraction(insigs(:,j), fs,fc_kv{:});
end
case 'osses2021'
for ii = 1:N_sigs
fc_kv = {'flow',fc_ref(ii),'fhigh',fc_ref(ii),'basef',fc_ref(ii),'dboffset',dBFS};
[outsig(:,ii),fc(ii)] = osses2021(insigs(:,j),fs,fc_kv{:},afb_flags{:},noihc_flags{:},noan_flags{:});
end
end
if ~isempty(outsig)
c = [];
if strcmp(models{k},'dau1997')
c.insig_not_scaled = insig_not_scaled;
end
c.outsig = outsig;
c.fc = fc;
amt_cache('set',fname,c);
end
end
K = N_samples/2;
if ~isempty(outsig)
idx0500 = find(fc_ref > 500, 1, 'first'); % needed for Fig. 4
idx4000 = find(fc_ref > 4000, 1, 'first'); % needed for Fig. 4
switch j
case 1 % Low intensity
if dispflag.do_verbose
amt_disp(sprintf('%s: Calculating low-level tuning',models{k}));
end
for ii = 1:length(fc_ref)
N_here = length(outsig(:,ii));
insig_here = reshape(outsig(:,ii),N_here/2,2);
[BW_low_each(ii,:,k), QERB_low_each(ii,:,k), ...
Q03_low_each(ii,:,k), Q10_low_each(ii,:,k), extra] ...
= local_Get_ERB_estimation_multi(outsig(:,ii), fc_ref(ii), fs, N_sections);
if ii == idx0500
data_fig4.ff{k} = extra.freq_here;
data_fig4.freq_resp0500{k}(:,j) = extra.Amp_dB + extra.max_dB;
data_fig4.freq_resp_MA0500{k}(:,j) = extra.Amp_dB_MA + extra.max_dB + extra.max_MA;
end
if ii == idx4000
data_fig4.freq_resp4000{k}(:,j) = extra.Amp_dB + extra.max_dB;
data_fig4.freq_resp_MA4000{k}(:,j) = extra.Amp_dB_MA + extra.max_dB + extra.max_MA;
end
end
case 2 % Middle intensity
if dispflag.do_verbose
amt_disp(sprintf('%s: Calculating mid-level tuning',models{k}));
end
for ii = 1:length(fc_ref)
N_here = length(outsig(:,ii));
insig_here = reshape(outsig(:,ii),N_here/2,2);
[BW_mid_each(ii,1,k), QERB_mid_each(ii,1,k), ...
Q03_mid_each(ii,1,k), Q10_mid_each(ii,1,k), extra] ...
= local_Get_ERB_estimation_multi(outsig(:,ii), fc_ref(ii), fs, N_sections);
if ii == idx0500
data_fig4.ff{k} = extra.freq_here;
data_fig4.freq_resp0500{k}(:,j) = extra.Amp_dB + extra.max_dB;
data_fig4.freq_resp_MA0500{k}(:,j) = extra.Amp_dB_MA + extra.max_dB + extra.max_MA;
end
if ii == idx4000
data_fig4.freq_resp4000{k}(:,j) = extra.Amp_dB + extra.max_dB;
data_fig4.freq_resp_MA4000{k}(:,j) = extra.Amp_dB_MA + extra.max_dB + extra.max_MA;
end
end
case 3 % Higher intensity
if dispflag.do_verbose
amt_disp(sprintf('%s: Calculating high-level tuning',models{k}));
end
for ii = 1:length(fc_ref)
N_here = length(outsig(:,ii));
insig_here = reshape(outsig(:,ii),N_here/2,2);
[BW_high_each(ii,1,k), QERB_high_each(ii,1,k), ...
Q03_high_each(ii,1,k), Q10_high_each(ii,1,k), extra] ...
= local_Get_ERB_estimation_multi(outsig(:,ii), fc_ref(ii), fs, N_sections);
if ii == idx0500
data_fig4.ff{k} = extra.freq_here;
data_fig4.freq_resp0500{k}(:,j) = extra.Amp_dB + extra.max_dB;
data_fig4.freq_resp_MA0500{k}(:,j) = extra.Amp_dB_MA + extra.max_dB + extra.max_MA;
end
if ii == idx4000
data_fig4.freq_resp4000{k}(:,j) = extra.Amp_dB + extra.max_dB;
data_fig4.freq_resp_MA4000{k}(:,j) = extra.Amp_dB_MA + extra.max_dB + extra.max_MA;
end
end % end ii
end % end switch j
end % end ~isempty
end % end for j
end % end for models
Pos34 = [600 350];
%%%
% Preparing the plots
models(k2remove) = [];
Colours(k2remove) = [];
Markers(k2remove) = [];
MarkersSize(k2remove) = [];
LineStyle(k2remove) = [];
LineWidth(k2remove) = [];
N_models_here = length(models); % number of models after removing
BW_low_each(:,:,k2remove) = [];
QERB_low_each(:,:,k2remove) = [];
Q03_low_each(:,:,k2remove) = [];
Q10_low_each(:,:,k2remove) = [];
BW_mid_each(:,:,k2remove) = [];
QERB_mid_each(:,:,k2remove) = [];
Q03_mid_each(:,:,k2remove) = [];
Q10_mid_each(:,:,k2remove) = [];
BW_high_each(:,:,k2remove) = [];
QERB_high_each(:,:,k2remove) = [];
Q03_high_each(:,:,k2remove) = [];
Q10_high_each(:,:,k2remove) = [];
idx = find(fc_ref<= 8000);
fc_ref = fc_ref(idx);
QERB_low_each = QERB_low_each(idx,:,:);
Q03_low_each = Q03_low_each(idx,:,:);
Q10_low_each = Q10_low_each(idx,:,:);
QERB_mid_each = QERB_mid_each(idx,:,:);
Q03_mid_each = Q03_mid_each(idx,:,:);
Q10_mid_each = Q10_mid_each(idx,:,:);
QERB_high_each = QERB_high_each(idx,:,:);
Q03_high_each = Q03_high_each(idx,:,:);
Q10_high_each = Q10_high_each(idx,:,:);
data_fig4.ff(k2remove) = [];
data_fig4.freq_resp0500(k2remove) = [];
data_fig4.freq_resp_MA0500(k2remove) = [];
data_fig4.freq_resp4000(k2remove) = [];
data_fig4.freq_resp_MA4000(k2remove) = [];
%%% ---------------------------------------------------------------
if flags.do_plot
figure;
% Analytical expressions:
fc_ref_here = [125 fc_ref 10000];
alpha = 0.3; % Shera2002, Table I, col 'human'
beta = 12.7; % Shera2002, Table I, col 'human'
Qerb_Shera_extended = beta*((fc_ref_here/1000).^alpha);
Qerb_Shera = Qerb_Shera_extended(2:end-1);
% Q10 = Qerb*0.505+0.2085; % Ibrahim2010, Eq. 40.2
pl = [];
pl(end+1) = semilogx(fc_ref_here,Qerb_Shera_extended,'-','Color',local_rgb('LightGray'),'LineWidth',5); hold on
BW_Glasberg = 24.7*(4.37*(fc_ref_here/1000)+1);
Qerb_Glasberg_extended = fc_ref_here./BW_Glasberg;
Qerb_Glasberg = Qerb_Glasberg_extended(2:end-1);
% Q10 = Qerb*0.505+0.2085; % Ibrahim2010, Eq. 40.2
pl(end+1) = plot(fc_ref_here,Qerb_Glasberg_extended,'-','Color',local_rgb('Gray'),'LineWidth',5);
for k = 1:N_models_here % little adjustments in format for the coming figures
switch models{k}
case 'dau1997'
Marker = [Markers{k} '-'];
MSize = 7;
LW = 1;
case 'zilany2014'
Marker = [Markers{k} LineStyle{k}];
MSize = 7;
LW = LineWidth(k);
otherwise
Marker = LineStyle{k};
MSize = MarkersSize(k);
LW = LineWidth(k);
end
format_kv = {Marker,'Color',Colours{k},'MarkerSize',MSize,'LineWidth',LW};
if k == 1
disp('Using Q03')
end
Q_low_here(:,k) = mean(Q03_low_each(:,:,k),2); % average of the positive and negative click
pl(end+1) = semilogx(fc_ref,Q_low_here(:,k),format_kv{:},'MarkerFaceColor',Colours{k}); hold on
% plot(fc_ref,QERB_higher(:,k),'s--',format_kv{:},'MarkerFaceColor','w');
end
grid on;
XT = [20 125 250 500 1000 2000 4000 8000 16000];
set(gca,'XTick',XT);
YT = 2:2:26;
set(gca,'YTick',YT);
ylim([1 27])
xlabel('Frequency (Hz)');
ylabel('Q factor');
Pos = get(gcf,'Position');
Pos(3:4) = Pos34; % [600 350];
set(gcf,'Position',Pos);
figure_handle(end+1) = gcf;
figure_name{end+1} = sprintf('fig06-QERB-low-level');
% ---------------------------------------------------------------------
for k = 1:N_models_here
X_ref_empirical = mean(QERB_low_each(:,:,k),2);
Y_10 = Q10_low_each(:,1,k);
Y_03 = Q03_low_each(:,1,k);
% Fit a polynomial p of degree 1 to the (x,y) data:
p10(k,:) = polyfit(X_ref_empirical,Y_10,1);
p03(k,:) = polyfit(X_ref_empirical,Y_03,1);
switch models{k}
case {'dau1997','relanoiborra2019','king2019','osses2021'}
X_ref(:,k) = Qerb_Glasberg(:);
case {'zilany2014','verhulst2015','verhulst2018'}
X_ref(:,k) = Qerb_Shera(:);
end
p10_with_formula(k,:) = polyfit(X_ref(:,k),Y_10,1);
p03_with_formula(k,:) = polyfit(X_ref(:,k),Y_03,1); % figure; plot(X_ref,Y_10); hold on; plot(X_ref,Y_03,'r')
Q03_recons(:,k) = X_ref(:,k)*p03_with_formula(k,1) + p03_with_formula(k,2);
end
for k = 1:N_models_here
f_current = 125;
f_high_lim = 8000;
count(k) = 0;
while f_current < f_high_lim
count(k) = count(k)+1;
f_low(count(k),k) = f_current;
if count(k)>10000; warning('Break'); break; end % break the loop if got stuck
Q_here = interp1(fc_ref, mean(Q03_low_each(:,:,k),2), f_low(count(k),k),'linear','extrap');
BW_here = f_low(count(k),k)/Q_here;
f_high(count(k),k) = f_low(count(k),k) + BW_here;
f_current = f_high(count(k),k);
end
fc_max = max(f_high);
fc_min = f_low(1,:);
end
% Values reported in Table II:
counts = count;
erb_step = (freqtoaud(fc_max)-freqtoaud(fc_min))./(count-1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure; % Now middle levels only
% Analytical expressions:
pl = [];
pl(end+1) = semilogx(fc_ref_here,Qerb_Shera_extended ,'-','Color',local_rgb('LightGray'),'LineWidth',5); hold on
pl(end+1) = plot( fc_ref_here,Qerb_Glasberg_extended,'-','Color',local_rgb('Gray') ,'LineWidth',5);
for k = 1:N_models_here
switch models{k}
case 'dau1997'
Marker = [Markers{k} '-'];
MSize = 7;
LW = 1;
case 'zilany2014'
Marker = [Markers{k} LineStyle{k}];
MSize = 7;
LW = LineWidth(k);
otherwise
Marker = LineStyle{k};
MSize = MarkersSize(k);
LW = LineWidth(k);
end
format_kv = {Marker,'Color',Colours{k},'MarkerSize',MSize, ...
'LineWidth',LW};
if k == 1 % bUse_Q03
disp('Using Q03')
end
Q_mid_here(:,k) = mean(Q03_mid_each(:,:,k),2); % average of the positive and negative click
pl(end+1) = semilogx(fc_ref,Q_mid_here(:,k),format_kv{:},'MarkerFaceColor',Colours{k}); hold on
% plot(fc_ref,QERB_higher(:,k),'s--',format_kv{:},'MarkerFaceColor','w');
end
grid on;
set(gca,'XTick',XT);
YT = 2:2:26;
set(gca,'YTick',YT);
ylim([1 27])
leg = models;
k = 2;
if strcmp(leg{k},'zilany2014')
leg{k} = 'zilany2014,bruce2018';
end
leg{end+1} = 'Shera (2001)';
leg{end+1} = 'G & M (1990)';
legend([pl(3:end) pl(1:2)],leg,'Location','NorthWest');
xlabel('Frequency (Hz)');
ylabel('Q factor');
Pos = get(gcf,'Position');
Pos(3:4) = Pos34;
set(gcf,'Position',Pos);
figure_handle(end+1) = gcf;
figure_name{end+1} = sprintf('fig06-QERB-mid-level');
f_low = [];
f_high = [];
count = [];
for k = 1:N_models_here
f_current = 125;
f_high_lim = 8000;
count(k) = 0;
while f_current < f_high_lim
count(k) = count(k)+1;
f_low(count(k),k) = f_current;
Q_here = interp1(fc_ref, mean(Q03_mid_each(:,:,k),2), f_low(count(k),k),'linear','extrap');
if isnan(Q_here)
% Q_here = Q03_recons_hi(1,k);
warning('Warning what happens here')
end
BW_here = f_low(count(k),k)/Q_here;
f_high(count(k),k) = f_low(count(k),k) + BW_here;
f_current = f_high(count(k),k);
end
fc_max = max(f_high);
fc_min = f_low(1,:);
end
counts(2,:) = count;
erb_step(2,:) = (freqtoaud(fc_max)-freqtoaud(fc_min))./(count-1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure; % Now high levels only
% Analytical expressions:
pl = [];
pl(end+1) = semilogx(fc_ref_here,Qerb_Shera_extended ,'-','Color',local_rgb('LightGray'),'LineWidth',5); hold on
pl(end+1) = plot( fc_ref_here,Qerb_Glasberg_extended,'-','Color',local_rgb('Gray') ,'LineWidth',5);
for k = 1:N_models_here
switch models{k}
case 'dau1997'
Marker = [Markers{k} '-'];
MSize = 7;
LW = 1;
case 'zilany2014'
Marker = [Markers{k} LineStyle{k}];
MSize = 7;
LW = LineWidth(k);
otherwise
Marker = LineStyle{k};
MSize = MarkersSize(k);
LW = LineWidth(k);
end
format_kv = {Marker,'Color',Colours{k},'MarkerSize',MSize, ...
'LineWidth',LW};
if k == 1
disp('Using Q03')
end
Q_high_here(:,k) = mean(Q03_high_each(:,:,k),2); % average of the positive and negative click
pl(end+1) = semilogx(fc_ref,Q_high_here(:,k),format_kv{:},'MarkerFaceColor',Colours{k}); hold on
end
grid on;
set(gca,'XTick',XT);
YT = 2:2:26;
set(gca,'YTick',YT);
ylim([1 27])
xlabel('Frequency (Hz)');
ylabel('Q factor');
Pos = get(gcf,'Position');
Pos(3:4) = Pos34;
set(gcf,'Position',Pos);
figure_handle(end+1) = gcf;
figure_name{end+1} = sprintf('fig06-QERB-high-level');
% -----------------------------------------------------------------
figure; % Difference Low - high
% Analytical expressions:
pl = [];
for k = 1:N_models_here
switch models{k}
case 'dau1997'
Marker = [Markers{k} '-'];
MSize = 7;
LW = 1;
case 'zilany2014'
Marker = [Markers{k} LineStyle{k}];
MSize = 7;
LW = LineWidth(k);
otherwise
Marker = LineStyle{k};
MSize = MarkersSize(k);
LW = LineWidth(k);
end
format_kv = {Marker,'Color',Colours{k},'MarkerSize',MSize,'LineWidth',LW};
pl(end+1) = semilogx(fc_ref,Q_low_here(:,k)-Q_high_here(:,k),format_kv{:},'MarkerFaceColor',Colours{k}); hold on
end
grid on;
Pos = get(gcf,'Position');
Pos(3:4) = [600 250];
set(gcf,'Position',Pos);
set(gca,'XTick',XT);
ylim([-4.5 18.5])
YT = -4:2:18;
set(gca,'YTick',YT);
xlabel('Frequency (Hz)');
ylabel('Q factor difference');
figure_handle(end+1) = gcf;
figure_name{end+1} = sprintf('fig06-QERB-difference-high-low');
X_ref_empirical = [];
for k = 1:N_models_here
X_ref_empirical(:,k) = Q_high_here(:,k);
Y_10 = Q10_high_each(:,1,k);
Y_03 = Q03_high_each(:,1,k);
end
f_low = [];
f_high = [];
count = [];
for k = 1:N_models_here
f_current = 125;
f_high_lim = 8000;
count(k) = 0;
while f_current < f_high_lim
count(k) = count(k)+1;
f_low(count(k),k) = f_current;
if count(k)>10000; warning('break - this should not happen'); break; end % break the loop if got stuck
% Q_here = interp1(fc_ref,X_ref(:,k),f_low(count(k),k),'linear','extrap');
Q_here = interp1(fc_ref, mean(Q03_high_each(:,:,k),2), f_low(count(k),k),'linear','extrap');
if isnan(Q_here)
warning('Warning what happens here') % Q_here = Q03_recons_hi(1,k);
end
BW_here = f_low(count(k),k)/Q_here;
f_high(count(k),k) = f_low(count(k),k) + BW_here;
f_current = f_high(count(k),k);
end
fc_max = max(f_high);
fc_min = f_low(1,:);
end
counts(3,:) = count;
erb_step(3,:) = (freqtoaud(fc_max)-freqtoaud(fc_min))./(count-1);
data.figure_handle = figure_handle;
data.figure_name = figure_name;
data.counts = counts;
data.counts_description = 'Number of bands required to cover a frequency range from 125 to 8000 Hz';
data.erb_step = erb_step;
data.erb_step_description = 'ERB spacing that would ensure filters overlapped at their -3 dB points';
end
data.data_fig4 = data_fig4;
data.figure_flag = 'do_fig6';
data.models = models;
end
%% ------ FIG 7 or 8 ------------------------------------------------------
if flags.do_fig7 || flags.do_fig8
k2remove = [];
N_models_here = N_models; % Bruce2018, is removed later...
t_ms_all = [];
vihc_all = [];
ti_all = [];
%%% 1. Generating the input signals: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Signal parameters:
dur = 0.08; % 80 ms
lvl = 80; % 80 dB SPL
dur_i = 0.02; % starting time for AC assessment
dur_f = 0.07; % ending time for AC assessment
% Arbitrary zero padding before and after the signal:
dur_sil = 50e-3;
sil_bef = zeros(round(dur_sil*fs),1);
idxi = length(sil_bef)+1; % index corresponding to the first signal sample after the zero padding
% AC will be calculated between dur_i and dur_f, excluding the silence:
idxi_ac = idxi+round(dur_i*fs)-1;
idxf_ac = idxi+round(dur_f*fs)-1;
[cf,x] = local_m2hz(401); % Get characteristic frequencies from Verhulst models
cf_max = 4000;
cf_min = 125;
%%%
N_sigs = 12;
vac = nan(N_models, N_sigs);
vdc = nan(size(vac));
%%% Getting f0 ('f0_method'):
df_bin = 25;
idx_cf(N_sigs) = find(cf>cf_max,1,'last');
for i = N_sigs-1:-1:1
idx_cf(i) = idx_cf(i+1)+df_bin;
end
f0 = cf(idx_cf);
%%%
dt = 1/fs; % \Delta t in s
t=0:dt:dur-dt;
for i = 1:N_sigs
insig(:,i) = sin(2*pi*f0(i)*t); % the same sinusoid will be scaled at different levels
end
% Up/down cosine ramp (fixed)
dur_ramp_ms = 5; % in ms
dur_ramp = round((dur_ramp_ms*1e-3)*fs); % duration ramp in samples
rp = ones(size(insig(:,1)));
rp(1:dur_ramp) = rampup(dur_ramp);
rp(end-dur_ramp+1:end) = rampdown(dur_ramp);
% Adjusting the signal level before the ramp and silences are added:
insig = scaletodbspl(insig,lvl,dBFS);
% Adding the silence and ramps to the signals:
insig = [repmat(sil_bef,1,N_sigs); repmat(rp,1,N_sigs).*insig];
%%%
for k = 1:N_models_here
%%% Loading flags and keyvals
[fg,kv] = local_get_flags(models{k});
fc_flags = fg.fc_flags;
afb_flags = fg.afb_flags;
afb_kv = kv.afb_keyvals;
ihc_flags = fg.ihc_flags;
noan_flags = fg.noan_flags; % No auditory nerve module
nomfb_flags = fg.nomfb_flags;
%%%
fname = ['fig07_and_08_ihc-' models{k}];
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
bRun = 0;
vihc = c.vihc;
fs = c.fs;
fc = c.fc;
else
bRun = 1;
vihc = [];
end
amt_disp(['Calculating ' models{k} '...']);
switch models{k}
case 'dau1997'
zoom_higher(k) = 1;
if bRun
for j = 1:N_sigs
fc_here = f0(j);
fc_kv = {'flow',fc_here,'fhigh',fc_here,'basef',fc_here,'dboffset',dBFS};
[outsig,fc] = dau1997(insig(:,j),fs,fc_kv{:},afb_flags{:}, ...
ihc_flags{:},noan_flags{:},nomfb_flags{:});
vihc(:,j) = outsig;
end
c = [];
c.vihc = vihc;
c.fs = fs;
c.fc = fc;
amt_cache('set',fname,c);
end
case 'zilany2014'
zoom_higher(k) = 3;
if bRun
kv={'nrep',1}; % kv={'numH',0,'numM',0,'numL',0,'fiberType',4,'nrep',1};
for j = 1:N_sigs
[~,~,vihc(:,j)] = zilany2014(insig(:,j),fs,f0(j),kv{:},afb_flags{:},'silent');
end
c = [];
c.vihc = vihc;
c.fs = fs;
c.fc = fc;
amt_cache('set',fname,c);
end
case 'verhulst2015'
zoom_higher(k) = 3;
if bRun
outsig = verhulst2015(insig,fs,fc_flags{:},afb_flags{:}, ...
ihc_flags{:},noan_flags{:},nomfb_flags{:},'silent');
for j = 1:N_sigs
vihc(:,j) = outsig(j).ihc(:,idx_cf(j));
end
c = [];
c.vihc = vihc;
c.fs = fs;
c.fc = fc;
amt_cache('set',fname,c);
end
case 'verhulst2018'
zoom_higher(k) = 3;
if bRun
outsig = verhulst2018(insig,fs,fc_flags{:},afb_flags{:}, ...
ihc_flags{:},noan_flags{:},nomfb_flags{:},'silent');
for j = 1:N_sigs
vihc(:,j) = outsig(j).ihc(:,idx_cf(j));
end
c = [];
c.vihc = vihc;
c.fs = fs;
c.fc = fc;
amt_cache('set',fname,c);
end
case 'bruce2018'
% already in zilany2014
k2remove = [k2remove k];
case 'king2019'
zoom_higher(k) = 1;
if bRun
for j = 1:N_sigs
fc_here = f0(j);
fc_kv = {'flow',fc_here,'fhigh',fc_here,'basef',fc_here,'dboffset',dBFS};
[outsig,fc] = king2019(insig(:,j),fs,fc_kv{:},afb_kv{:}, ...
afb_flags{:},ihc_flags{:},noan_flags{:},nomfb_flags{:});
vihc(:,j) = outsig;
end
c = [];
c.vihc = vihc;
c.fs = fs;
c.fc = fc;
amt_cache('set',fname,c);
end
case 'relanoiborra2019'
zoom_higher(k) = 3;
if bRun
for j = 1:N_sigs
fc_here = f0(j);
fc_kv = {'flow',fc_here,'fhigh',fc_here,'basef',fc_here};
[~,~,outsig,fc] = relanoiborra2019_featureextraction(insig(:,j), fs, ...
fc_kv{:},afb_flags{:},noan_flags{:});
vihc(:,j) = outsig;
end
c = [];
c.vihc = vihc;
c.fs = fs;
c.fc = fc;
amt_cache('set',fname,c);
end
case 'osses2021'
zoom_higher(k) = 3;
if bRun
for j = 1:N_sigs
fc_here = f0(j);
fc_kv = {'flow',fc_here,'fhigh',fc_here,'basef',fc_here,'dboffset',dBFS};
[outsig,fc] = osses2021(insig(:,j),fs,fc_kv{:},afb_flags{:}, ...
ihc_flags{:},noan_flags{:},nomfb_flags{:});
vihc(:,j) = outsig;
end
c = [];
c.vihc = vihc;
c.fs = fs;
c.fc = fc;
amt_cache('set',fname,c);
end
end
dc_reg = 1:length(sil_bef);
ac_reg = idxi_ac:idxf_ac;
if k~=5,
[ac_target,dc_target,vr] = local_get_ac_dc_osses2022(vihc,ac_reg,dc_reg);
vac(k,:) = ac_target;
vdc(k,:) = dc_target;
vrest(k,1) = vr(1);
% Plotting: -------------------------------------------------------
% % Normalisation to 1.5 the maximum value:
v_norm_factor(k) = 1.5*max(max(abs(vihc)));
t_ms = 1000*(1:size(vihc,1))/fs;
t2plot = [35 105];
idxi_plot = find(t_ms<=t2plot(1),1,'last');
idxf_plot = find(t_ms<=t2plot(2),1,'last');
idxs = idxi_plot:idxf_plot-1;
v_dec_norm = (vihc-vrest(k))/v_norm_factor(k); % normalisation to maximum value
idxs_N = 9:12;
v_dec_norm(:,idxs_N) = zoom_higher(k)*v_dec_norm(:,idxs_N);
idxs2store = idxs(1:5000);
t_ms_all = [t_ms_all t_ms(idxs2store)];
ti_all(end+1) = size(vihc_all,1)+1;
vihc_all = [vihc_all; v_dec_norm(idxs2store,:)];
vmin(k,:) = min(vihc(idxs2store,:));
end
end
models(k2remove) = [];
Colours(k2remove) = [];
Markers(k2remove) = [];
MarkersSize(k2remove) = [];
LineStyle(k2remove) = [];
LineWidth(k2remove) = [];
zoom_higher(k2remove) = [];
vac(k2remove,:) = []; vdc(k2remove,:) = []; vmin(k2remove,:) = [];
v_norm_factor(k2remove) = [];
N_models_here = length(models); % number of models after removing
if flags.do_plot
%%% All formatting options in one variable. The formatting options
% for the bruce2018 are overwritten for visibility reasons,
% given that it provides the same outputs of zilany2014
Format = [];
for k = 1:N_models_here
Format{k} = {'Color',Colours{k},'LineStyle',LineStyle{k}, ...
'LineWidth',LineWidth(k),'Marker',Markers{k},'MarkerFaceColor','w', ...
'MarkerSize',MarkersSize(k)};
end
%%% End formatting options
end
ti_all(end+1) = length(t_ms_all);
toffset = t_ms(idxs(1));
tf = 0;
idxi = 1;
if flags.do_plot
YL = [-.5 N_sigs];
plt = [];
idx_split = 8;
panel_labels = {'a) ','b) ','c) ','d) ','e) ','f) ','g) '};
for k = 1:N_models_here
if strcmp(models{k},'zilany2014')
leg{k} = [panel_labels{k} 'zilany2014,bruce2018'];
else
leg{k} = [panel_labels{k} models{k}];
end
end
end
for k = 1:N_models_here
if flags.do_plot
if k == 1
figure;
figure_handle(end+1) = gcf;
figure_name{end+1} = 'fig07-ihc-waveforms';
end
end
idxi = ti_all(k);
idxf = ti_all(k+1)-1;
t_here = t_ms_all(idxi:idxf);
vihc_here = vihc_all(idxi:idxf,:);
if flags.do_plot
for j = 1:N_sigs
offy = j-1; % (j-1) lower to higher freqs from bottom to top. Use (N_sigs-j) otherwise
if j == 1
plt(end+1) = plot(t_here-toffset+tf,vihc_here(:,j)+offy,'Color',Colours{k});
else
plot(t_here-toffset+tf,vihc_here(:,j)+offy,'Color',Colours{k});
end
hold on
if j == 1 && (k == 1 || k == idx_split)
ylim(YL);
end
plot(tf*[1 1],YL,'k-');
if (k == 1 || k == idx_split)
text(2,offy+.3,sprintf('%.0f Hz',f0(j)),'Color','k','FontSize',8);
grid on
dur_plot = 1000*length(idxs2store)/fs;
XL = [0 (idx_split-1)*dur_plot];
xlim(XL);
Pos = get(gcf,'Position');
Pos(3:4) = [1300 530];
set(gcf,'Position',Pos);
XT = 0:10:XL(end);
XTL = [0 repmat([10:10:dur_plot],1,(idx_split-1))];
XT(dur_plot/10+1:dur_plot/10:end) = [];
XTL(dur_plot/10+1:dur_plot/10:end) = [];
set(gca,'XTick',XT);
set(gca,'XTickLabel',XTL);
YT = 0:N_sigs;
set(gca,'YTick',YT);
set(gca,'YTickLabel','');
set(gca,'FontSize',MediumFontSize);
end
if j >= 9
% For the last three waveforms...
text((k*dur_plot-8),offy-.07,sprintf('x %.0f',zoom_higher(k)),'Color','k','FontSize',SmallFontSize);
end
end % end for j
x_coor = ((mod(k,idx_split))-1)*1/(idx_split-1)+.01;
y_coor = .98;
text(x_coor,y_coor,leg{k},'Units','Normalize','FontSize',SmallFontSize);
if k == idx_split-1
tf = 0;
else
tf = tf+(t_here(end)+dt*1000-toffset);
end
ylabel('Amplitude (a.u.)');
xlabel('Time (ms)');
plot(tf*[1 1],YL,'k-'); % last time for the second figure
end
end
ra = vac ./vdc;
if flags.do_plot
% Fig. 8 ----------------------------------------------------------
figure;
for k = 1:N_models_here
pl(k) = loglog(f0,ra(k,:),Format{k}{:}); grid on, hold on
xlabel('Frequency (Hz)');
ylabel('IHC AC/DC ratio');
end
set(gca,'XTick',[80 125 250 500 1000 2000 4000 8000]);
YT = get(gca,'YTick');
set(gca,'YTickLabel',YT);
xlim([100 6000]);
ylim([0.008 180]);
Pos = get(gcf,'Position');
Pos(3:4) = [600 300];
set(gcf,'Position',Pos);
leg = models;
if strcmp(leg{2},'zilany2014')
leg{2} = 'zilany2014,bruce2018';
end
legend(leg,'Location','SouthWest');
figure_handle(end+1) = gcf;
figure_name{end+1} = 'fig08-IHC-AC-DC';
data.figure_flag = 'do_fig7 or do_fig8';
data.figure_handle = figure_handle;
data.figure_name = figure_name;
end
data.insig = insig;
data.fs = fs;
data.ramp = rp;
data.models = models;
data.ACDCratio = ra;
data.AC = vac;
data.DC = vdc;
data.dBFS = dBFS;
data.lvl = lvl;
data.f0_approx = round(f0');
data.f0_exact = round(cf(idx_cf)');
data.vrest = vrest;
data.vmin = vmin;
data.v_norm_factor = v_norm_factor;
end
%% ------ FIG. 9 ----------------------------------------------------------
if flags.do_fig9
outsig_all = [];
t_an_all = [];
% 1. Stimulus creation:
dur = 300e-3; % 300 ms
lvl = 70;
t = (1:dur*fs)/fs; t = t(:); % creates 't' as a column array
fc = 4000;
dur_ramp_ms = 2.5;
dur_ramp = round((dur_ramp_ms*1e-3)*fs); % duration ramp in samples
insig = sin(2*pi*fc.*t);
insig = scaletodbspl(insig,lvl,dBFS); % calibration before applying the ramp
rp = ones(size(insig));
rp(1:dur_ramp) = rampup(dur_ramp);
rp(end-dur_ramp+1:end) = rampdown(dur_ramp);
insig = rp.*insig;
insig = [zeros(50e-3*fs,1); insig; zeros(200e-3*fs,1)]; % 50 and 200 ms
% of silence before and after the sine tone
ti_steady = 300*1e-3; % ms
tf_steady = 340*1e-3; % ms
for k = 1:N_models
%%% Loading flags and keyvals
[fg,kv] = local_get_flags(models{k});
afb_flags = fg.afb_flags;
afb_kv = kv.afb_keyvals;
ihc_flags = fg.ihc_flags;
an_flags = fg.an_flags;
an_kv = kv.an_keyvals;
nomfb_flags = fg.nomfb_flags;
fname = ['fig09_' models{k} '-an-wave'];
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
bRun = 0;
outsig = c.outsig;
fs_an = c.fs_an;
fc = c.fc;
if isfield(c,'out_psth')
out_psth = c.out_psth;
end
else
bRun = 1;
outsig = [];
end
YTL = []; % empty tick label
if bRun && ~isempty(an_kv)
% Loading common parameters
switch models{k}
case {'verhulst2015','verhulst2018','zilany2014','bruce2018'}
if strcmp(an_kv{1},'numH')
numH = an_kv{2};
else
error('an_kv should contain numH')
end
if strcmp(an_kv{3},'numM')
numM = an_kv{4};
else
error('an_kv should contain numM')
end
if strcmp(an_kv{5},'numL')
numL = an_kv{6};
else
error('an_kv should contain numM')
end
end
switch models{k}
case {'zilany2014','bruce2018'}
idx = find(strcmp(an_kv,'psth_binwidth'));
if ~isempty(idx)
% Only true for bruce2018:
psth_binwidth = an_kv{idx+1};
end
nrep = 100;
end
end
if bRun
amt_disp(['Calculating ' models{k} '...']);
switch models{k}
case 'dau1997'
fc_kv = {'flow',fc,'fhigh',fc,'basef',fc,'dboffset',dBFS};
outsig = dau1997(insig,fs,fc_kv{:},afb_flags{:}, ...
ihc_flags{:},an_flags{:},nomfb_flags{:});
fs_an = fs;
c = [];
c.outsig = outsig;
c.fs_an = fs_an;
c.fc = fc;
amt_cache('set',fname,c);
case 'zilany2014'
kv={'fiberType',4,'numH',numH,'numM',numM,'numL',numL,'nrep',nrep,'psth_binwidth',psth_binwidth};
[outsig,psth,~,~,~,~,out] = zilany2014(insig,fs,fc,kv{:},afb_flags{:},'silent');
fs_an = fs;
out_psth.psth = psth;
out_psth.psth_binwidth = psth_binwidth;
if isfield(out,'psth_LSR')
out_psth.psth_LSR = out.psth_LSR;
end
if isfield(out,'psth_MSR')
out_psth.psth_MSR = out.psth_MSR;
end
if isfield(out,'psth_HSR')
out_psth.psth_HSR = out.psth_HSR;
end
c = [];
c.outsig = outsig;
if isfield(out,'meanrate_LSR')
c.outsig_LSR = out.meanrate_LSR;
end
if isfield(out,'meanrate_MSR')
c.outsig_MSR = out.meanrate_MSR;
end
if isfield(out,'meanrate_HSR')
c.outsig_HSR = out.meanrate_HSR;
end
c.fs_an = fs_an;
c.fc = fc;
c.out_psth = out_psth;
amt_cache('set',fname,c);
case 'verhulst2015'
fc_flag = fc; % one frequency will be simulated only
out = verhulst2015(insig,fs,fc_flag,afb_flags{:},an_flags{:},'anfL','anfM','anfH',an_kv{:},nomfb_flags{:},'silent');
outsig = out(1).an_summed/(numL + numM + numH);
fs_an = out(1).fs_an;
c = [];
c.outsig = outsig;
c.fs_an = fs_an;
c.fc = fc;
amt_cache('set',fname,c);
case 'verhulst2018'
fc_flag = fc; % one frequency will be simulated only
out = verhulst2018(insig,fs,fc_flag,afb_flags{:},an_flags{:},an_kv{:},nomfb_flags{:},'silent');
outsig = out(1).an_summed/(numL + numM + numH);
fs_an = out(1).fs_an;
c = [];
c.outsig = outsig;
c.fs_an = fs_an;
c.fc = fc;
amt_cache('set',fname,c);
case 'bruce2018'
kv = {'numH',numH,'numM',numM,'numL',numL,'psthbinwidth_mr',psth_binwidth, ...
'nrep',nrep,'specificSRautoTiming'};
out = bruce2018(insig,fs,fc,kv{:},afb_flags{:},'silent');
outsig = out.meanrate;
out_psth.psth = out.psth;
out_psth.psth_binwidth = psth_binwidth;
if isfield(out,'psth_LSR')
out_psth.psth_LSR = out.psth_LSR;
end
if isfield(out,'psth_MSR')
out_psth.psth_MSR = out.psth_MSR;
end
if isfield(out,'psth_HSR')
out_psth.psth_HSR = out.psth_HSR;
end
fs_an = fs;
c = [];
c.outsig = outsig;
if isfield(out,'meanrate_LSR')
c.outsig_LSR = out.meanrate_LSR;
end
if isfield(out,'meanrate_MSR')
c.outsig_MSR = out.meanrate_MSR;
end
if isfield(out,'meanrate_HSR')
c.outsig_HSR = out.meanrate_HSR;
end
c.fs_an = fs_an;
c.fc = fc;
c.out_psth = out_psth;
amt_cache('set',fname,c);
case 'king2019'
fc_kv = {'flow',fc,'fhigh',fc,'basef',fc,'dboffset',dBFS};
outsig = king2019(insig,fs,fc_kv{:},afb_flags{:}, afb_kv{:}, ...
ihc_flags{:},an_flags{:},nomfb_flags{:});
fs_an = fs;
c = [];
c.outsig = outsig;
c.fs_an = fs_an;
c.fc = fc;
amt_cache('set',fname,c);
case 'relanoiborra2019'
fc_kv = {'flow',fc,'fhigh',fc,'basef',fc};
[~,~,outsig] = relanoiborra2019_featureextraction(insig, fs,fc_kv{:},afb_flags{:});
fs_an = fs;
c = [];
c.outsig = outsig;
c.fs_an = fs_an;
c.fc = fc;
amt_cache('set',fname,c);
case 'osses2021'
fc_kv = {'flow',fc,'fhigh',fc,'basef',fc,'dboffset',dBFS};
outsig = osses2021(insig,fs,fc_kv{:},afb_flags{:}, ...
ihc_flags{:},an_flags{:},nomfb_flags{:});
fs_an = fs;
c = [];
c.outsig = outsig;
c.fs_an = fs_an;
c.fc = fc;
amt_cache('set',fname,c);
end
end
switch models{k}
case {'zilany2014','bruce2018'}
pst_hist = out_psth.psth;
dt = out_psth.psth_binwidth;
t_psth = (1:length(pst_hist))*dt;
end
t_an = (1:length(outsig))/fs_an;
t_an = t_an(:); % creates 't_an' as a column array
outsig_all{k} = outsig;
t_an_all{k} = t_an;
id_steady = find(t_an>=ti_steady & t_an<=tf_steady);
onset = max(outsig);
steady = mean(outsig(id_steady));
ra(k) = onset/steady;
data_sim(k).onset = onset;
data_sim(k).onset = steady;
fs_an_all(k) = fs_an;
end
if flags.do_plot
for k = 1:N_models
psth_offx = 20;
switch models{k}
case {'dau1997','relanoiborra2019','osses2021'}
figure(1);
if strcmp(models{k},'osses2021') % osses2021 will be shifted
offx = psth_offx;
else
offx = 0;
end
handle_data(k) = plot(t_an_all{k}*1000+offx,outsig_all{k},'Color',Colours{k},'LineWidth',2); hold on, grid on;
if k == 1
figure_handle(end+1) = gcf; % multiple figures will be generated
figure_name{end+1} = ['fig09-tone-4-kHz-' models{k}];
end
units_amplitude = '(MU)';
YL = [-300 1500]; % a priori knowledge
stepY = 100;
YT = YL(1)+stepY:stepY:YL(2)-stepY;
if k == 1
YTL = [];
for ii = 1:length(YT)
if mod(YT(ii),200)==0
YTL{ii} = num2str(YT(ii));
else
YTL{ii} = '';
end
end
end
case {'zilany2014','bruce2018'}
figure;
handle_data(k) = stairs(t_psth*1000 + psth_offx,pst_hist,'Color',0.5*Colours{k},'LineWidth',2); hold on, grid on;
plot(t_an_all{k}*1000,outsig_all{k},'Color',Colours{k},'LineWidth',2); hold on, grid on;
% Trick to add labels later:
plot(t_psth(1)*1000 + psth_offx,pst_hist(1),'Color',0.5*Colours{k},'LineWidth',2)
% End: Trick
figure_handle(end+1) = gcf; % multiple figures will be generated
figure_name{end+1} = ['fig09-tone-4-kHz-' models{k}];
units_amplitude = '(spikes/s)';
YL = [0 1000]; % a priori knowledge
stepY = 100;
YT = YL(1)+stepY:stepY:YL(2)-stepY;
case {'verhulst2015','verhulst2018'}
figure(3)
if strcmp(models{k},'verhulst2015')
offx = psth_offx;
else
offx = 0;
end
handle_data(k) = plot(t_an_all{k}*1000 + offx,outsig_all{k},'Color',Colours{k},'LineWidth',2); hold on, grid on;
if k == 3
figure_handle(end+1) = gcf; % multiple figures will be generated
figure_name{end+1} = ['fig09-tone-4-kHz-' models{k}];
end
units_amplitude = '(spikes/s)';
YL = [0 1000]; % a priori knowledge
stepY = 100;
YT = YL(1)+stepY:stepY:YL(2)-stepY;
case 'king2019'
factor = 1e3;
figure;
handle_data(k) = plot(t_an_all{k}*1000,factor*outsig_all{k},'Color',Colours{k},'LineWidth',2); hold on, grid on;
if k == 6
figure_handle(end+1) = gcf; % multiple figures will be generated
figure_name{end+1} = ['fig09-tone-4-kHz-' models{k}];
end
units_amplitude = '(arbitrary units x 10^{-3})';
YL = factor*1.8e-3*[-1 1]; % a priori knowledge
stepY = factor*.3e-3;
YT = YL(1)+stepY:stepY:YL(2)-stepY;
end
xlabel('Time (ms)');
ylabel(['Amplitude ' units_amplitude])
set(gca,'XTick',50:50:450);
xlim([25 475]);
ylim(YL);
set(gca,'YTick',YT);
switch k
case {1,7,8}
set(gca,'YTickLabel',YTL);
end
offy = 5;
Xhere = 1000*[t_an(min(id_steady)) t_an(max(id_steady))];
Yhere = (YL(1)+offy)*[1 1];
plot(Xhere,Yhere,'--','LineWidth',2,'Color',[0.5 0.5 0.5]);
Yhere = (YL(2)-offy)*[1 1];
plot(Xhere,Yhere,'--','LineWidth',2,'Color',[0.5 0.5 0.5]);
Yhere = YL;
Xhere = 1000*t_an(min(id_steady))*[1 1];
plot(Xhere,Yhere,'--','LineWidth',2,'Color',[0.5 0.5 0.5]);
Xhere = 1000*t_an(max(id_steady))*[1 1];
plot(Xhere,Yhere,'--','LineWidth',2,'Color',[0.5 0.5 0.5]);
Pos = get(gcf,'Position');
Pos(3:4) = [600 250];
set(gcf,'Position',Pos);
end
end
if flags.do_plot
data.figure_flag = 'do_fig9';
end
data.ra = ra;
data.insig = insig;
data.fs = fs;
data.data_sim = data_sim;
data.models = models;
if flags.do_plot
data.figure_handle = figure_handle;
data.handle_data = handle_data;
data.figure_name = figure_name;
end
data.outsig_all = outsig_all;
data.fs_an_all = fs_an_all;
end
%% ------ FIG 10 or FIG11 -------------------------------------------------
if flags.do_fig10 || flags.do_fig11
% Code adapted from testPopRateLevel_BEZ2018_mine.m
%%% 1. Generating the input signals: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Signal parameters:
CF = 4000; % CF in Hz;
lvls = 0:10:100;
N_lvls = length(lvls);
dur = 300e-3; % stimulus duration in seconds
dur_sil = 50e-3; % duration of silence at the beginning
dur_ramp_ms = 2.5; % rise/fall time in seconds
dur_ramp = round((dur_ramp_ms*1e-3)*fs); % duration ramp in samples
t = 0:1/fs:dur-1/fs; t = t(:); % time vector
insig_orig = sin(2*pi*CF*t);
insig = zeros(length(t),N_lvls); % memory allocation
rp = ones(size(insig_orig));
rp(1:dur_ramp) = rampup(dur_ramp);
rp(end-dur_ramp+1:end) = rampdown(dur_ramp);
for kk = 1:N_lvls
lvl = lvls(kk);
insig(:,kk) = scaletodbspl(insig_orig,lvl,dBFS); % calibration before applying the ramp
end
insig = repmat(rp,1,N_lvls).*insig;
%%% End generating the signal (the level adjustment is done later)
insig = [zeros(dur_sil*fs,N_lvls); insig]; % 50 ms of silence before and after the sine tone
ti_steady_ms = (dur+dur_sil)*1e3 - 50; % 300 ms (if dur=300 ms)
tf_steady_ms = (dur+dur_sil)*1e3 - 10; % 340 ms (if dur=340 ms)
numH = 12; % Automate this
numM = 4;
numL = 4;
numTot = numH+numM+numL;
numCum = cumsum([numL numM numH]);
rates_steady = nan(numTot,N_lvls,N_models);
rates = nan(numTot,N_lvls,N_models);
for k = 1:N_models
meanAN_H = [];
meanAN_M = [];
meanAN_L = [];
%%% Loading flags and keyvals
[fg,kv] = local_get_flags(models{k});
afb_flags = fg.afb_flags;
afb_kv = kv.afb_keyvals;
ihc_flags = fg.ihc_flags;
an_flags = fg.an_flags;
nomfb_flags = fg.nomfb_flags;
an_kv = kv.an_keyvals;
%%%
fname = ['fig10_rate-level-' models{k}];
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
bRun = 0;
meanAN_H = c.meanAN_H;
meanAN_M = c.meanAN_M;
meanAN_L = c.meanAN_L;
rates = c.rates;
fs_an = c.fs_an;
idxi = c.idxi;
idxf = c.idxf;
outsig = c.outsig;
if isfield(c,'psth_L')
psth_L = c.psth_L;
end
if isfield(c,'psth_M')
psth_M = c.psth_M;
end
if isfield(c,'psth_H')
psth_H = c.psth_H;
end
if isfield(c,'rates_psth')
rates_psth = c.rates_psth;
end
else
bRun = 1;
end
if ~isempty(an_kv)
switch models{k}
case {'zilany2014','bruce2018'}
idx = find(strcmp(an_kv,'psth_binwidth'));
if ~isempty(idx)
% Only true for bruce2018:
psth_binwidth = an_kv{idx+1};
end
nrep = 100;
end
end
if bRun
amt_disp(['Calculating ' models{k} '...']);
% Initialisation:
meanAN_H = [];
meanAN_M = [];
meanAN_L = [];
rates = [];
switch models{k}
case {'zilany2014','bruce2018'}
psth_L = []; psth_M = []; psth_H = []; rates_psth = [];
end
switch models{k}
case 'dau1997'
fc_kv = {'flow',CF,'fhigh',CF,'basef',CF,'dboffset',dBFS};
for j = 1:N_lvls
meanAN_H(:,j) = dau1997(insig(:,j),fs,fc_kv{:}, ...
afb_flags{:},ihc_flags{:},an_flags{:},nomfb_flags{:});
end
outsig = meanAN_H;
fs_an = fs;
units_amplitude = '(Model Units)';
idxi = round(ti_steady_ms*1e-3*fs_an)+1;
idxf = round(tf_steady_ms*1e-3*fs_an);
case 'zilany2014'
L = size(insig,1);
for j = 1:N_lvls
% out_psTH.psth_binwidth = psth_binwidth;
kv={'fiberType',4,'numH',numH,'numM',numM,'numL',numL,'nrep',nrep,'psth_binwidth',psth_binwidth};
[outsig,psth,~,~,~,~,out] = zilany2014(insig(:,j),fs,CF,kv{:},afb_flags{:}),'silent';
meanAN_L(:,j) = out.meanrate_LSR(1:L);
meanAN_M(:,j) = out.meanrate_MSR(1:L);
meanAN_H(:,j) = out.meanrate_HSR(1:L);
rates(:,j) = outsig(1:L);
psth_L(:,j) = out.psth_LSR;
psth_M(:,j) = out.psth_MSR;
psth_H(:,j) = out.psth_HSR;
rates_psth(:,j) = psth;
outsig = rates;
end
units_amplitude = '(spikes/s)';
fs_an = fs;
idxi = round(ti_steady_ms*1e-3*fs_an)+1; % round(15e-3*fs_an)+1; % start after 15 ms (strong onset);
idxf = round(tf_steady_ms*1e-3*fs_an); % end
case 'verhulst2015'
fc_flag = CF;
out = verhulst2015(insig,fs,fc_flag,afb_flags{:},an_flags{:},'anfL','anfM','anfH',an_kv{:},nomfb_flags{:},'silent');
for j = 1:N_lvls
% psTH =out(kk).an_summed/numTot;
meanAN_L(:,j) = out(j).anfL;
meanAN_M(:,j) = out(j).anfM;
meanAN_H(:,j) = out(j).anfH;
end
units_amplitude = '(spikes/s)';
outsig = (numL*meanAN_L + numM*meanAN_M + numH*meanAN_H)/numTot;
fs_an = out(1).fs_an;
idxi = round(ti_steady_ms*1e-3*fs_an)+1;
idxf = round(tf_steady_ms*1e-3*fs_an);
case 'verhulst2018'
fc_flag = CF;
out = verhulst2018(insig,fs,fc_flag,afb_flags{:},an_flags{:},'anfL','anfM','anfH',an_kv{:},nomfb_flags{:},'silent');
for j = 1:N_lvls
% psTH =out(kk).an_summed/numTot;
meanAN_L(:,j) = out(j).anfL;
meanAN_M(:,j) = out(j).anfM;
meanAN_H(:,j) = out(j).anfH;
end
units_amplitude = '(spikes/s)';
outsig = (numL*meanAN_L + numM*meanAN_M + numH*meanAN_H)/numTot;
fs_an = out(1).fs_an;
idxi = round(ti_steady_ms*1e-3*fs_an)+1;
idxf = round(tf_steady_ms*1e-3*fs_an);
case 'bruce2018'
psth_L = []; psth_M = []; psth_H = [];
L = size(insig,1);
kv={'numH',numH,'numM',numM,'numL',numL,'nrep',nrep,'psthbinwidth_mr',psth_binwidth, ...
'specificSRautoTiming'};
for j = 1:N_lvls
out = bruce2018(insig(:,j),fs,CF,kv{:},afb_flags{:},'silent');
meanAN_L(:,j) = out.meanrate_LSR(1:L);
meanAN_M(:,j) = out.meanrate_MSR(1:L);
meanAN_H(:,j) = out.meanrate_HSR(1:L);
rates(:,j) = out.meanrate(1:L);
psth_L(:,j) = out.psth_LSR;
psth_M(:,j) = out.psth_MSR;
psth_H(:,j) = out.psth_HSR;
rates_psth(:,j) = out.psth;
end
% bruce2018 returns the 'weighted LSR, MSR, and HSR', unweighting (for visual scaling),
% to obtain an average PSTH per neurone type...
disp('Applying scaling, assuming an_summed = numL*Raw_L + numM*Raw_M + numH*Raw_H')
psth_L = psth_L/(numL/numTot);
psth_M = psth_M/(numM/numTot);
psth_H = psth_H/(numH/numTot);
outsig = rates;
units_amplitude = '(spikes/s)';
fs_an = fs;
idxi = round(ti_steady_ms*1e-3*fs_an)+1;
idxf = round(tf_steady_ms*1e-3*fs_an);
case {'king2019'}
fc_kv = {'flow',CF,'fhigh',CF,'basef',CF,'dboffset',dBFS};
% clear meanAN_H
for j = 1:N_lvls
meanAN_H(:,j) = king2019(insig(:,j),fs,fc_kv{:}, ...
afb_flags{:},afb_kv{:},ihc_flags{:},an_flags{:},nomfb_flags{:});
end
outsig = meanAN_H;
fs_an = fs;
factor = 1e3;
if factor == 1e3
units_amplitude = '(a.u. x 10^{-3})';
else
units_amplitude = '(a.u.)';
end
idxi = round(ti_steady_ms*1e-3*fs_an)+1;
idxf = round(tf_steady_ms*1e-3*fs_an);
case {'relanoiborra2019'}
fc_kv = {'flow',CF,'fhigh',CF,'basef',CF};
for j = 1:N_lvls
[~,~,meanAN_H(:,j)] = relanoiborra2019_featureextraction(insig(:,j), fs,fc_kv{:},afb_flags{:});
end
outsig = meanAN_H;
fs_an = fs;
units_amplitude = '(Model Units)';
idxi = round(ti_steady_ms*1e-3*fs_an)+1;
idxf = round(tf_steady_ms*1e-3*fs_an);
case 'osses2021'
fc_kv = {'flow',CF,'fhigh',CF,'basef',CF,'dboffset',dBFS};
for j = 1:N_lvls
meanAN_H(:,j) = osses2021(insig(:,j),fs,fc_kv{:}, ...
afb_flags{:},ihc_flags{:},an_flags{:},nomfb_flags{:});
end
outsig = meanAN_H;
fs_an = fs;
units_amplitude = '(Model Units)';
idxi = round(ti_steady_ms*1e-3*fs_an)+1;
idxf = round(tf_steady_ms*1e-3*fs_an);
end
c = [];
c.meanAN_H = meanAN_H;
c.meanAN_M = meanAN_M;
c.meanAN_L = meanAN_L;
c.rates = rates;
c.outsig = outsig;
switch models{k}
case {'zilany2014','bruce2018'}
c.psth_binwidth = psth_binwidth;
c.psth_L = psth_L;
c.psth_M = psth_M;
c.psth_H = psth_H;
c.rates_psth = rates_psth;
end
c.fs_an = fs_an;
c.idxi = idxi;
c.idxf = idxf;
c.units_amplitude = units_amplitude;
amt_cache('set',fname,c);
end
rates_max_tot(:,k) = max(outsig)';
rates_mean_all(:,k) = mean(outsig(idxi:idxf,:))';
for j = 1:N_lvls
if ~isempty(meanAN_L)
rates_max( 1:numCum(1),j,k)= max(meanAN_L(:,j));
rates_steady( 1:numCum(1),j,k)= mean(meanAN_L(idxi:idxf,j));
else
rates_max( 1:numCum(1),j,k)= nan;
rates_steady( 1:numCum(1),j,k)= nan;
end
if ~isempty(meanAN_M)
rates_max( numCum(1)+1:numCum(2),j,k)= max(meanAN_M(:,j));
rates_steady(numCum(1)+1:numCum(2),j,k)= mean(meanAN_M(idxi:idxf,j));
else
rates_max(numCum(1)+1:numCum(2),j,k) = nan;
rates_steady(numCum(1)+1:numCum(2),j,k) = nan;
end
rates_max(numCum(2)+1:numCum(3),j,k)= max(meanAN_H(:,j));
rates_steady(numCum(2)+1:numCum(3),j,k)= mean(meanAN_H(idxi:idxf,j));
switch models{k}
case {'zilany2014','bruce2018'}
dt = psth_binwidth;
% t_psth = (1:size(rates_psth,1))*dt;
idxi_psth = round(ti_steady_ms*1e-3/dt)+1; % start after 15 ms (exludes the first 15 ms of strong onset)
idxf_psth = round(tf_steady_ms*1e-3/dt);
rates_steady_PSTH(1,j,k)= mean(psth_L(idxi_psth:idxf_psth,j));
rates_steady_PSTH(2,j,k)= mean(psth_M(idxi_psth:idxf_psth,j));
rates_steady_PSTH(3,j,k)= mean(psth_H(idxi_psth:idxf_psth,j));
rates_max_PSTH(1,j,k)= max(psth_L(:,j));
rates_max_PSTH(2,j,k)= max(psth_M(:,j));
rates_max_PSTH(3,j,k)= max(psth_H(:,j));
rates_max_PSTH(4,j,k)= max(rates_psth(:,j));
end
end
Raw_L = squeeze(rates_steady(1:numCum(1),:,k));
Raw_M = squeeze(rates_steady(numCum(1)+1:numCum(2),:,k));
Raw_H = squeeze(rates_steady(numCum(2)+1:numCum(3),:,k));
rates_avg_all{k} = squeeze(rates_steady(numCum(2)+1:numCum(3),:,k));
if k == 1
rates_avg_description = 'rate levels for ''HSR''';
end
if k == 1
rates_max_description = 'onset rate levels for ''HSR''';
end
if flags.do_plot
do_PSTH = 0;
Pos34 = [320 250]; % width and height of the resulting plot
factor = 1;
if flags.do_fig10
switch models{k}
case {'dau1997','relanoiborra2019','osses2021'}
figure(1);
if k == 1
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig10-AN-firing-rates-model-' models{k}];
else
hold on; % grid on
end
YL = 'Amplitude (MU)';
YLim = [-10 130];
YT = 0:20:130;
case {'zilany2014','bruce2018'}
do_PSTH = 1; % to do after...
figure;
YL = 'Firing rate (spikes/s)';
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig10-AN-firing-rates-model-' models{k}];
if strcmp(models{k},'zilany2014')
YLim = [-20 380];
YT = 0:30:360;
else
YLim = [-20 230];
YT = 0:30:300;
end
case {'verhulst2015','verhulst2018'}
figure
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig10-AN-firing-rates-model-' models{k}];
YL = 'Firing rate (spikes/s)';
YLim = [-20 300];
YT = 0:30:280;
case 'king2019'
factor = 1e5;
figure;
if factor == 1e5
YL = 'Amplitude (a.u. x 10^{-5})';
else
YL = 'Amplitude (a.u.)';
end
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig10-AN-firing-rates-model-' models{k}];
YLim = [-0.2 3.2];
YT = (0:.3:3);
end
switch models{k}
case {'zilany2014','bruce2018','verhulst2015','verhulst2018'}
% No change in Colour
Colour_here = local_rgb('Gray'); % Colours{k}
hl1 = plot(lvls,factor*mean(Raw_L),'o-','Color',Colour_here,'MarkerFaceColor','w','LineWidth',3,'MarkerSize',10,'MarkerFaceColor','w'); hold on, grid on
hl2 = plot(lvls,factor*mean(Raw_M),'^--','Color',Colour_here,'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',[0.8 0.8 0.8]);
hl3 = plot(lvls,factor*mean(Raw_H),'s-','Color',Colour_here,'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',Colour_here);
otherwise
hl1 = 0;
hl2 = 0;
hl3 = 0;
end
hl4 = plot(lvls,factor*rates_mean_all(:,k),'d-','Color',Colours{k},'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',Colours{k}); grid on
handle_data(k,:) = [hl1(1) hl2(1) hl3(1) hl4(1)];
xlim([min(lvls)-2 max(lvls)+2])
ylim(YLim)
xlabel('Stimulus Level (dB SPL)')
ylabel(YL)
set(gca,'XTick',0:10:100);
set(gca,'YTick',YT);
Pos = get(gcf,'Position');
Pos(3:4) = Pos34;
set(gcf,'Position',Pos);
factors(k) = factor;
%%%
if do_PSTH
factor = 1;
figure;
YL = 'Firing rate (spikes/s)';
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig10-AN-firing-rates-model-' models{k} '-PSTH'];
Raw_L = squeeze(rates_steady_PSTH(1,:,k));
Raw_M = squeeze(rates_steady_PSTH(2,:,k));
Raw_H = squeeze(rates_steady_PSTH(3,:,k));
hl1 = plot(lvls,factor*Raw_L,'o-','Color' ,local_rgb('Gray'),'MarkerFaceColor','w','LineWidth',3,'MarkerSize',10,'MarkerFaceColor','w'); hold on, grid on
hl2 = plot(lvls,factor*Raw_M,'^--','Color',local_rgb('Gray'),'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',[0.8 0.8 0.8]);
hl3 = plot(lvls,factor*Raw_H,'s-','Color' ,local_rgb('Gray'),'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',local_rgb('Gray'));
psth_tot(k,:) = (numL*Raw_L+numM*Raw_M+numH*Raw_H)/(numL+numM+numH);
hl4 = plot(lvls,factor*psth_tot(k,:),'d-','Color',0.5*Colours{k},'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',0.5*Colours{k});
handle_data_psth(k,:) = [hl1(1) hl2(1) hl3(1) hl4(1)];
xlim([min(lvls)-2 max(lvls)+2])
ylim(YLim)
xlabel('Stimulus Level (dB SPL)')
ylabel(YL)
set(gca,'XTick',0:10:100);
set(gca,'YTick',YT);
Pos = get(gcf,'Position');
Pos(3:4) = Pos34;
set(gcf,'Position',Pos);
end
end
%%%%
if flags.do_fig11
switch models{k}
case {'dau1997','relanoiborra2019','osses2021'}
figure(1);
if k == 1
figure_handle(end+1) = gcf;
figure_name{end+1} = sprintf('fig11-tone-4-kHz-IO-onset-%s',models{k});
else
hold on; % grid on
end
YL = 'Amplitude (MU)';
YLim = [-50 1650];
stepY = 100;
YT = 0:stepY:1600;
if k == 1
YTL = [];
for ii = 1:length(YT)
if mod(YT(ii),200)==0
YTL{ii} = num2str(YT(ii));
else
YTL{ii} = '';
end
end
end
case {'zilany2014','bruce2018'}
do_PSTH = 1; % to do after...
figure;
YL = 'Firing rate (Spikes/s)';
figure_handle(end+1) = gcf; % multiple figures will be generated
figure_name{end+1} = sprintf('fig10-tone-4-kHz-IO-onset-%s',models{k});
if strcmp(models{k},'zilany2014')
YLim = [-50 1650];
else
YLim = [-50 1050];
end
stepY = 100;
YT = 0:stepY:1600;
case {'verhulst2015','verhulst2018'}
figure
figure_handle(end+1) = gcf; % multiple figures will be generated
figure_name{end+1} = sprintf('fig10-tone-4-kHz-IO-onset-%s',models{k});
YL = 'Firing rate (Spikes/s)';
YLim = [-50 1650];
stepY = 100;
YT = 0:stepY:1600;
case 'king2019'
factor = 1e3;
figure;
if factor == 1e3
YL = '(a.u. x 10^{-3})';
else
YL = '(a.u.)';
end
figure_handle(end+1) = gcf; % multiple figures will be generated
figure_name{end+1} = sprintf('fig10-tone-4-kHz-IO-onset-%s',models{k});
YLim = [-0.2 5.2];
YT = (0:.5:5);
end
Raw_L = squeeze(rates_max(1:numCum(1),:,k));
Raw_M = squeeze(rates_max(numCum(1)+1:numCum(2),:,k));
Raw_H = squeeze(rates_max(numCum(2)+1:numCum(3),:,k));
switch models{k}
case {'zilany2014','bruce2018','verhulst2015','verhulst2018'}
% No change in Colour
Colour_here = local_rgb('Gray'); % Colours{k}
hl1 = plot(lvls,factor*mean(Raw_L),'o-','Color',Colour_here,'MarkerFaceColor','w','LineWidth',3,'MarkerSize',10,'MarkerFaceColor','w'); hold on, grid on
hl2 = plot(lvls,factor*mean(Raw_M),'^--','Color',Colour_here,'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',[0.8 0.8 0.8]);
hl3 = plot(lvls,factor*mean(Raw_H),'s-','Color',Colour_here,'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',Colour_here);
otherwise
hl1 = 0;
hl2 = 0;
hl3 = 0;
end
hl4 = plot(lvls,factor*rates_max_tot(:,k),'d-','Color',Colours{k},'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',Colours{k}); grid on
handle_data(k,:) = [hl1(1) hl2(1) hl3(1) hl4(1)];
xlim([min(lvls)-2 max(lvls)+2])
ylim(YLim)
xlabel('Stimulus Level (dB SPL)')
ylabel(YL);
set(gca,'YTick',YT);
switch models{k}
case {'king2019'}
otherwise
set(gca,'YTickLabel',YTL);
end
set(gca,'XTick',0:10:100);
Pos = get(gcf,'Position');
Pos(3:4) = Pos34;
set(gcf,'Position',Pos);
factors(k) = factor;
%%%
if do_PSTH
factor = 1;
% case {'zilany2014','bruce2018'}
figure;
YL = 'Firing rate (Spikes/s)';
figure_handle(end+1) = gcf;
figure_name{end+1} = sprintf('fig10-tone-4-kHz-IO-onset-%s-PSTH',models{k});
Raw_L = squeeze(rates_max_PSTH(1,:,k));
Raw_M = squeeze(rates_max_PSTH(2,:,k));
Raw_H = squeeze(rates_max_PSTH(3,:,k));
psth_tot(k,:) = squeeze(rates_max_PSTH(4,:,k))';
% psth_totTest = (numL*Raw_L+numM*Raw_M+numH*Raw_H)/(numL+numM+numH);
hl1 = plot(lvls,factor*Raw_L,'o-','Color' ,local_rgb('Gray'),'MarkerFaceColor','w','LineWidth',3,'MarkerSize',10,'MarkerFaceColor','w'); hold on, grid on
hl2 = plot(lvls,factor*Raw_M,'^--','Color',local_rgb('Gray'),'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',[0.8 0.8 0.8]);
hl3 = plot(lvls,factor*Raw_H,'s-','Color' ,local_rgb('Gray'),'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',local_rgb('Gray'));
hl4 = plot(lvls,factor*psth_tot(k,:),'d-','Color',0.5*Colours{k},'LineWidth',3,'MarkerSize',10,'MarkerFaceColor',0.5*Colours{k});
handle_data_psth(k,:) = [hl1(1) hl2(1) hl3(1) hl4(1)];
YLim = [-50 1650];
xlim([min(lvls)-2 max(lvls)+2])
ylim(YLim)
xlabel('Stimulus Level (dB SPL)')
ylabel(YL)
set(gca,'XTick',0:10:100);
set(gca,'YTick',YT);
set(gca,'YTickLabel',YTL);
Pos = get(gcf,'Position');
Pos(3:4) = Pos34;
set(gcf,'Position',Pos);
end
end
%%%
end
end
data.rate_avg_all = rates_avg_all;
data.rate_avg_description = rates_avg_description;
if flags.do_fig10
data.figure_flag = 'do_fig10';
% data.rate_avg_all = rates_avg_all;
% data.rate_avg_description = rates_avg_description;
end
data.rate_max_tot = rates_max_tot;
data.rate_max_description = rates_max_description;
if flags.do_fig11
if flags.do_plot
data.figure_flag = 'do_fig11';
end
end
if flags.do_plot
data.factors = factors;
data.figure_handle = figure_handle;
data.figure_name = figure_name;
data.handle_data = handle_data;
if exist('handle_data_psth','var')
data.handle_data_psth = handle_data_psth;
end
end
data.models = models;
data.lvls = lvls;
end
%% ------ FIG 12 Osses et al. 2021 ----------------------------------------
if flags.do_fig12a || flags.do_fig12b
figure_handle_ax = [];
% This is similar to Verhulst et al. 2018, Fig. 3C:
%%% 1. Generating the input signals: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Signal parameters:
fc_target = 4000; % Hz, frequency of the carrier
cf = local_m2hz(401); % Get characteristic frequencies from Verhulst models
idx_cf = find(cf>fc_target,1,'last');
fc = cf(idx_cf);
fmod = 100; % Hz, frequency of the modulator
mdepth = 1; % value between 0 and 1
dur = 500e-3; % stimulus duration in seconds
dur_ramp = 2.5e-3; % s, duration of the ramp
lvl = 60; % level, dB
N_samples = round(dur*fs);
dur_ramp_samples = round((dur_ramp)*fs);
% Creating a cosine ramp:
ramp = ones(N_samples,1);
ramp(1:dur_ramp_samples) = rampup(dur_ramp_samples);
ramp(end-dur_ramp_samples+1:end) = rampdown(dur_ramp_samples);
% AM stimulus and calibration:
t = (0:N_samples-1)/fs; t=t(:);
carrier = sin(2*pi*fc*t); % starts at phase = 0
env = (1 + mdepth * sin(2*pi*fmod*t-pi/2) ); % modulator starts at minimum (phase=-pi/2)
insig = env .* carrier; % Amplitude-modulated signal
insig = scaletodbspl(insig,lvl,dBFS);
insig = ramp.*insig;
%%%
psth_bin4plot = 0.5e-3; % 0.5 ms
percent4plot = 90; % percentage overlap
%%%
outs_anf = [];
for k = 1:N_models
%%% Loading flags and keyvals:
[fg,kv] = local_get_flags(models{k});
fc_flags = fg.fc_flags;
afb_flags = fg.afb_flags;
afb_kv = kv.afb_keyvals;
ihc_flags = fg.ihc_flags;
an_flags = fg.an_flags; % No auditory nerve module
nomfb_flags= fg.nomfb_flags;
an_kv = kv.an_keyvals;
fname = ['fig12_' models{k} '-an'];
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
bRun = 0;
an_summed = c.an_summed;
fs_an = c.fs_an;
anfH = c.anfH;
anfM = c.anfM;
anfL = c.anfL;
if isfield(c,'out_psth')
out_psth = c.out_psth;
end
else
bRun = 1;
an_summed = [];
amt_disp(['Calculating ' models{k} '...']);
end
if bRun && ~isempty(an_kv)
% Loading common parameters
switch models{k}
case {'verhulst2015','verhulst2018','zilany2014','bruce2018'}
if strcmp(an_kv{1},'numH')
numH = an_kv{2};
else
error('an_kv should contain numH')
end
if strcmp(an_kv{3},'numM')
numM = an_kv{4};
else
error('an_kv should contain numM')
end
if strcmp(an_kv{5},'numL')
numL = an_kv{6};
else
error('an_kv should contain numM')
end
end
switch models{k}
case {'zilany2014','bruce2018'}
idx = find(strcmp(an_kv,'psth_binwidth'));
if ~isempty(idx)
% Only true for bruce2018:
psth_binwidth = an_kv{idx+1};
end
nrep = 100;
end
end
%%% Figure settings (overloaded later for dau1997):
text4ylabel='(spikes/s)';
YL = [-48 330];
factor = 1;
%%%
switch models{k}
case 'dau1997'
if bRun
fc_kv = {'flow',fc,'fhigh',fc,'basef',fc,'dboffset',dBFS};
out = dau1997(insig,fs,fc_kv{:},afb_flags{:}, ...
ihc_flags{:},an_flags{:},nomfb_flags{:});
fs_an = fs;
an_summed = out;
anfH = out;
anfM = []; % 0*out;
anfL = []; % 0*out;
c = [];
c.an_summed = an_summed;
c.fs_an = fs_an;
c.anfH = anfH;
c.anfM = anfM;
c.anfL = anfL;
amt_cache('set',fname,c);
end
text4ylabel='(MU)';
YL = [-230 1450];
case {'zilany2014'}
if bRun
kv={'fiberType',4,'numH',numH,'numM',numM,'numL',numL,'nrep',nrep,'psth_binwidth',psth_binwidth};
[an_summed,out_psth.psth,~,~,~,~,out] = zilany2014(insig,fs,fc,kv{:},afb_flags{:},'silent');
fs_an = fs;
anfH = out.meanrate_HSR;
anfM = out.meanrate_MSR;
anfL = out.meanrate_LSR;
out_psth.psth_binwidth = psth_binwidth;
c = [];
c.an_summed = an_summed;
c.fs_an = fs_an;
c.anfH = anfH;
c.anfM = anfM;
c.anfL = anfL;
c.out_psth = out_psth;
amt_cache('set',fname,c);
end
YL = [-20 780];
case {'verhulst2015'}
if bRun
hear_profile = 'Flat00'; % NH audiogram, default
fc_kv = {'hearing_profile',hear_profile};
out = verhulst2015(insig,fs,fc_flags{:},fc_kv{:},afb_flags{:},an_kv{:},an_flags{:},'anfL','anfM','anfH',nomfb_flags{:});
fs_an = out.fs_abr;
num_tot = numH+numM+numL;
an_summed = out.an_summed(:,idx_cf)/num_tot;
anfH = out.anfH;
anfM = out.anfM;
anfL = out.anfL;
c = [];
c.an_summed = an_summed;
c.fs_an = fs_an;
c.anfH = anfH;
c.anfM = anfM;
c.anfL = anfL;
amt_cache('set',fname,c);
end
YL = [-20 780];
case {'verhulst2018'}
if bRun
%%% Model parameters (only one hearing profile, Flat00 and 13-3-3):
hear_profile = 'Flat00';
fc_kv = {'hearing_profile',hear_profile};
out = verhulst2018(insig,fs,fc_flags{:},fc_kv{:},afb_flags{:},'anfL','anfM','anfH',an_kv{:},an_flags{:},nomfb_flags{:},'silent');
fs_an = out.fs_abr;
num_tot = numH+numM+numL;
an_summed = out.an_summed(:,idx_cf)/num_tot;
anfH = out.anfH;
anfM = out.anfM;
anfL = out.anfL;
c = [];
c.an_summed = an_summed;
c.fs_an = fs_an;
c.anfH = anfH;
c.anfM = anfM;
c.anfL = anfL;
amt_cache('set',fname,c);
end
YL = [-20 780];
case {'bruce2018'}
if bRun
kv = {'numH',numH,'numM',numM,'numL',numL,'psthbinwidth_mr',psth_binwidth, ...
'nrep',nrep,'specificSRautoTiming'};
out = bruce2018(insig,fs,fc,kv{:},afb_flags{:},'silent');
an_summed = out.meanrate;
anfH=out.meanrate_HSR; anfM = out.meanrate_MSR; anfL = out.meanrate_LSR;
out_psth.psth = out.psth;
out_psth.psth_HSR = out.psth_HSR;
out_psth.psth_MSR = out.psth_MSR;
out_psth.psth_LSR = out.psth_LSR;
out_psth.psth_binwidth = psth_binwidth;
fs_an = fs;
c = [];
c.an_summed = an_summed;
c.fs_an = fs_an;
c.anfH = anfH;
c.anfM = anfM;
c.anfL = anfL;
c.out_psth = out_psth;
amt_cache('set',fname,c);
end
YL = [-20 780];
case 'king2019'
if bRun
fc_kv = {'flow',fc,'fhigh',fc,'basef',fc,'dboffset',dBFS};
out = king2019(insig,fs,fc_kv{:},afb_flags{:},afb_kv{:}, ...
ihc_flags{:},an_flags{:},nomfb_flags{:});
fs_an = fs;
an_summed = out;
anfH = out;
anfM = []; % 0*out;
anfL = []; % 0*out;
c = [];
c.fs_an = fs_an;
c.an_summed = an_summed;
c.anfH = anfH;
c.anfM = anfM;
c.anfL = anfL;
amt_cache('set',fname,c);
end
factor = 1000;
if factor == 1000
text4ylabel='(a.u. x 10^-3)';
else
text4ylabel='(a.u.)';
end
YL = 1e-3*[-1.1 1.1];
case 'relanoiborra2019'
if bRun
fc_kv = {'flow',fc,'fhigh',fc,'basef',fc};
[~,~,out] = relanoiborra2019_featureextraction(insig, fs,fc_kv{:},afb_flags{:});
fs_an = fs;
an_summed = out;
anfH = out;
anfM = []; % 0*out;
anfL = []; % 0*out;
c = [];
c.an_summed = an_summed;
c.fs_an = fs_an;
c.anfH = anfH;
c.anfM = anfM;
c.anfL = anfL;
c.out_psth = out;
amt_cache('set',fname,c);
end
text4ylabel='(MU)';
YL = [-230 1450];
case 'osses2021'
if bRun
fc_kv = {'flow',fc,'fhigh',fc,'basef',fc,'dboffset',dBFS};
out = osses2021(insig,fs,fc_kv{:},afb_flags{:}, ...
ihc_flags{:},an_flags{:},nomfb_flags{:});
fs_an = fs;
an_summed = out;
anfH = out;
anfM = []; % 0*out;
anfL = []; % 0*out;
c = [];
c.an_summed = an_summed;
c.fs_an = fs_an;
c.anfH = anfH;
c.anfM = anfM;
c.anfL = anfL;
amt_cache('set',fname,c);
end
text4ylabel='(MU)';
YL = [-230 1450];
end
t_anf = (1:length(an_summed))'/fs_an;
L_bin = round(psth_bin4plot*fs_an); % samples
L_overlap = round((percent4plot/100)*L_bin);
t_anf = buffer(t_anf, L_bin, L_overlap,'nodelay');
t_anf = t_anf(1,:);
anf = buffer(an_summed, L_bin, L_overlap,'nodelay');
anf = mean(anf); % one value per bin
if flags.do_plot
offx = 2.5;
if flags.do_fig12a
XL = [0-offx 50+offx];
elseif flags.do_fig12b
XL = [350-offx 400+offx];
end
switch models{k}
case {'dau1997','relanoiborra2019','osses2021'}
if k == 1
fig_functional = figure;
figure_handle_ax(end+1) = gca;
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig12-Adaptation-' models{k}];
hold on, grid on;
else
figure(fig_functional);
end
if strcmp(models{k},'osses2021')
Style = '--';
else
Style = '-';
end
case {'zilany2014','bruce2018'}
figure;
figure_handle_ax(end+1) = gca;
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig12-Adaptation-' models{k}];
hold on, grid on;
Style = '-';
case {'verhulst2015','verhulst2018'}
if ~exist('fig_verhulst','var')
fig_verhulst = figure;
figure_handle_ax(end+1) = gca;
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig12-Adaptation-' models{k}];
hold on, grid on;
else
figure(fig_verhulst);
end
Style = '-';
case 'king2019'
figure;
figure_handle_ax(end+1) = gca;
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig12-Adaptation-' models{k}];
hold on, grid on;
Style = '-';
end
plot(t_anf*1000,factor*anf,Style,'Color',Colours{k},'LineWidth',2); grid on, hold on
end
switch models{k}
case {'zilany2014','bruce2018'}
psth_count = out_psth.psth;
t_psth = (1:length(psth_count))*out_psth.psth_binwidth;
if flags.do_plot
stairs(t_psth*1000,psth_count,Style,'Color',0.5*Colours{k},'LineWidth',2); grid on, hold on
end
outs_anf(k).psth_count = psth_count;
outs_anf(k).t_psth = t_psth;
if flags.do_plot
% Trick to add labels later:
plot(t_psth(1)*1000,psth_count(1),'Color',0.5*Colours{k},'LineWidth',2);
end
end
if flags.do_plot
xlim(XL);
ylim(factor*YL);
xlabel('Time (ms)');
ylabel(text4ylabel);
Pos = get(gcf,'Position');
Pos(3:4) = [360 400];
set(gcf,'Position',Pos);
end
outs_anf(k).t_anf = t_anf;
outs_anf(k).fs_an = fs_an;
outs_anf(k).anf = anf;
outs_anf(k).anfH = anfH;
outs_anf(k).anfM = anfM;
outs_anf(k).anfL = anfL;
end
% hl = legend(text4leg,'Location','SouthEast');
% set(hl,'FontSize',8);
data.models = models;
data.insig = insig;
data.fs = fs;
data.outs_anf = outs_anf;
if flags.do_plot
if flags.do_fig12a
data.figure_flag = 'do_fig12a';
elseif flags.do_fig12b
data.figure_flag = 'do_fig12b';
end
data.figure_handle = figure_handle;
data.figure_name = figure_name;
data.figure_handle_ax = figure_handle_ax;
end
end
%% ------ FIG 13 Osses et al. 2021 ----------------------------------------
if flags.do_fig13
dur = 300e-3;
rampdur = 10e-3;
lvl = 50;
basef = 1000;
baseaud = freqtoaud(basef);
N_freqs = 7;
erb_step = 3;
freqs = audtofreq(baseaud-N_freqs+1:1:baseaud);
freqs4tones = freqs(1:erb_step:end);
starting_phases = [0.7145 4.2320 2.2943]; % Three fixed (frozen) phases, without using fixed seeds
[insig,f2test,starting_phases] = local_Profile_Analysis(dur, rampdur, lvl, fs,freqs4tones,starting_phases);
insig = insig(:);
ti = 220; % ms
tf = 260;
ti_e = ti+10;
%%%
for k = 1:length(models)
out = [];
suff_str = [];
%%% Loading flags and keyvals
[fg,kv] = local_get_flags(models{k});
fc_flags = fg.fc_flags;
afb_flags = fg.afb_flags;
afb_keyvals = kv.afb_keyvals;
ihc_flags = fg.ihc_flags;
an_flags = fg.an_flags;
nomfb_flags = fg.nomfb_flags;
an_kv = kv.an_keyvals;
%%%
fname = ['fig13_profile_' models{k}];
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
bRun = 0;
out = c.out;
fc = c.fc;
fs_an = c.fs_an;
else
bRun = 1;
out = [];
amt_disp(['Calculating ' models{k} '...']);
end
fc_green = local_m2hz(401);
for j = 1:N_freqs
bin_nrs(j) = find(fc_green>freqs(j),1,'last');
end
% bin_nrs = bin_nrs(1:erb_step:end);
CFs = fc_green(bin_nrs);
if ~isempty(afb_keyvals)
idx = find(strcmp(afb_keyvals,'compression_n'));
if ~isempty(idx)
compression_n = afb_keyvals{idx+1};
else
error('mfb_keyvals should contain BMF')
end
end
if bRun && ~isempty(an_kv)
% Loading common parameters
switch models{k}
case {'verhulst2015','verhulst2018','zilany2014','bruce2018'}
if strcmp(an_kv{1},'numH')
numH = an_kv{2};
else
error('an_kv should contain numH')
end
if strcmp(an_kv{3},'numM')
numM = an_kv{4};
else
error('an_kv should contain numM')
end
if strcmp(an_kv{5},'numL')
numL = an_kv{6};
else
error('an_kv should contain numM')
end
end
switch models{k}
case 'bruce2018'
idx = find(strcmp(an_kv,'psth_binwidth'));
if ~isempty(idx)
% Only true for bruce2018:
psth_binwidth = an_kv{idx+1};
end
nrep = 100;
end
end
extra_after = '';
switch models{k}
case 'dau1997'
if bRun
for j = 1:length(CFs)
fc_kv = {'basef',CFs(j),'flow',CFs(j),'fhigh',CFs(j),'dboffset',dBFS};
out_tmp = dau1997(insig,fs,afb_flags{:},ihc_flags{:}, ...
an_flags{:},nomfb_flags{:},fc_kv{:});
out(:,j) = out_tmp;
fc(j) = freqs(j);
end
fs_an = fs;
c = [];
c.out = out;
c.fc = fc;
c.fs_an = fs_an;
amt_cache('set',fname,c);
end
fc_ref = fc;
ZL = [-240 1000];
ymax = 1600;
yscale = ymax/2;
ymax_str = sprintf('%.0f MU',yscale);
extra_str = 'a) ';
case 'zilany2014'
if bRun
% Only one repetition because we will use the mean-rate output.
kv={'fiberType',4,'numH',numH,'numM',numM,'numL',numL,'nrep',1};
out = zilany2014(insig,fs,fc_ref,kv{:},afb_flags{:},'silent');
out = out(1:length(insig),:);
fs_an = fs;
c = [];
c.out = out;
c.out_description = 'AN mean-rate output';
c.fc = fc_ref;
c.fs_an = fs_an;
amt_cache('set',fname,c);
end
ymax = 1000;
yscale = ymax/2;
ymax_str = sprintf('%.0f spikes/s',yscale);
extra_str = 'c) ';
extra_after = ', mean rate';
case 'verhulst2015'
if bRun
out_tmp = verhulst2015(insig,fs,fc_flags{:},afb_flags{:},an_flags{:},an_kv{:},nomfb_flags{:},'silent');
fc = fc_green(bin_nrs);
fs_an = out_tmp.fs_an;
numTot = numL+numM+numH;
out = out_tmp.an_summed(:,bin_nrs)/numTot;
c = [];
c.out = out;
c.fc = fc;
c.fs_an = fs_an;
amt_cache('set',fname,c);
end
extra_str = 'e) ';
ymax = 1000;
yscale = ymax/2;
ymax_str = sprintf('%.0f spikes/s',yscale);
case 'verhulst2018'
if bRun
out_tmp = verhulst2018(insig,fs,fc_flags{:},afb_flags{:},an_flags{:},an_kv{:},nomfb_flags{:},'silent');
fc = fc_green(bin_nrs);
fs_an = out_tmp.fs_an;
numTot = numL+numM+numH;
out = out_tmp.an_summed(:,bin_nrs)/numTot;
c = [];
c.out = out;
c.fc = fc;
c.fs_an = fs_an;
amt_cache('set',fname,c);
end
extra_str = 'f) ';
ymax = 1000;
yscale = ymax/2;
ymax_str = sprintf('%.0f spikes/s',yscale);
case 'bruce2018'
bUse_PSTH = 1;
if bRun
fc = fc_ref;
kv = {'numH',numH,'numM',numM,'numL',numL, ...
'psthbinwidth_mr',psth_binwidth,'nrep',nrep,'specificSRautoTiming'};
for j = 1:length(fc_ref)
output = bruce2018(insig,fs,fc_ref(j),kv{:},afb_flags{:},'silent');
out(:,j) = output.psth;
end
dt = psth_binwidth;
fs_an = 1/dt;
Nf = round(dur*fs_an);
out = out(1:Nf,:);
c = [];
c.out = out;
c.out_description = 'PSTH output';
c.fc = fc;
c.fs_an = fs_an;
amt_cache('set',fname,c);
end
if bUse_PSTH == 0
ymax = 300;
else
ymax = 1000;
end
yscale = ymax/2;
ymax_str = sprintf('%.0f spikes/s',yscale);
extra_str = 'd) ';
extra_after = ', PSTH';
case 'king2019'
switch compression_n
case 0.3
ymax = 2.5e-3; % all channels compressed
end
if bRun
fc = [];
for j = 1:length(fc_ref)
fc_kv = {'basef',fc_ref(j),'flow',fc_ref(j),'fhigh',fc_ref(j),'dboffset',dBFS};
[out_tmp,fc(j)] = king2019(insig,fs,afb_flags{:},ihc_flags{:}, ...
an_flags{:},nomfb_flags{:},fc_kv{:},'compression_n',compression_n);
out(:,j) = out_tmp;
end
fs_an = fs;
c = [];
c.out = out;
c.fc = fc;
c.fs_an = fs_an;
amt_cache('set',fname,c);
end
extra_str = 'b) ';
yscale = ymax/2;
ymax_str = sprintf('%.4f a.u',yscale);
case 'relanoiborra2019'
if bRun
fc = [];
for j = 1:length(fc_ref)
fc_kv = {'basef',fc_ref(j),'flow',fc_ref(j),'fhigh',fc_ref(j),'dboffset',dBFS};
[~,~,out_tmp,fc(j)] = relanoiborra2019_featureextraction(insig, fs, ...
fc_kv{:}, afb_flags{:});
out(:,j) = out_tmp;
end
% out = out(:,1:2*erb_step:end);
% fc = fc(1:2*erb_step:end);
fs_an = fs;
c = [];
c.out = out;
c.fc = fc;
c.fs_an = fs_an;
amt_cache('set',fname,c);
end
ZL = [-240 1000];
ymax = 1600;
yscale = ymax/2;
ymax_str = sprintf('%.0f MU',yscale);
extra_str = 'g) ';
case 'osses2021'
if bRun
for j = 1:length(fc_ref)
fc_kv = {'basef',fc_ref(j),'flow',fc_ref(j),'fhigh',fc_ref(j),'dboffset',dBFS};
out_tmp = osses2021(insig,fs,afb_flags{:},ihc_flags{:}, ...
an_flags{:},nomfb_flags{:},fc_kv{:});
out(:,j) = out_tmp;
fc(j) = fc_ref(j);
end
fs_an = fs;
c = [];
c.out = out;
c.fc = fc;
c.fs_an = fs_an;
amt_cache('set',fname,c);
end
extra_str = 'h) ';
ZL = [-240 1000];
ymax = 1600;
yscale = ymax/2;
ymax_str = sprintf('%.0f MU',yscale);
end
idxi_=round(ti*1e-3*fs_an);
idxf_=round(tf*1e-3*fs_an);
offy = [];
t_ms = 1000*(1:size(insig,1))/fs;
tan_ms = 1000*(1:size(out,1))/fs_an;
bw_erb = audfiltbw(fc);
if flags.do_plot
figure;
end
for i = 1:length(fc)
me = mean(out(idxi_:idxf_,i)); % looks for peaks above the avg in the steady section
[env_here,idx_here] = local_Get_envelope2(out(:,i),me);
tan_ms_here = tan_ms(idx_here);
idxs = find(tan_ms >= ti-10 & tan_ms <= tf-7.5); % 10 ms before ti
if flags.do_plot
offy(i) = (i-1);
plot(tan_ms(idxs),out(idxs,i)/ymax + offy(i),'Color',Colours{k}); hold on, grid on
end
idxs = find(tan_ms_here >= ti-10 & tan_ms_here <= tf-7.5);
if flags.do_plot
plot(tan_ms_here(idxs),env_here(idxs)/ymax + offy(i),'Color',[0.6 0.6 0.6],'LineWidth',2);
end
env_mean(k,i) = mean(env_here(idxs));
env_std(k,i) = std(env_here(idxs));
env_scale(k) = yscale;
end
factor_std = 30; % scaling (arbitrary) applied to the std of each model/channel
if flags.do_plot
%%% YAxis: scale
tf_here=tf-7.5-.5;
plot(tf_here*[1 1],[0 0.5],'k-','LineWidth',2);
plot([tf_here-2 tf_here],0.5*[1 1],'k-','LineWidth',2);
plot([tf_here-2 tf_here], [0 0],'k-','LineWidth',2);
text(tf_here-6,-0.35,ymax_str,'FontWeight','Bold');
%%%
ylabel('Simulated CF_n (Hz)')
xlim([ti-2.5 tf]);
ylim([-.5 7.5])
YL = get(gca,'YLim');
tf_i_here = tf+2.5-10; % '0' for envelope metric (vertical black line)
env_std_here = env_std(k,:)/(env_scale(k)/factor_std);
ampl_here = env_mean(k,:)/ymax + offy;
plot(tf_i_here+env_std_here, ampl_here,'o--','Color',local_rgb('Maroon'),'LineWidth',2,'MarkerFaceColor','w');
plot(tf_i_here*[1 1],YL,'k-');
tf_f_here = tf_i_here + 2*3;
y_here = 6.8;
plot([tf_i_here tf_f_here],y_here*[1 1],'-','Color',local_rgb('Maroon'),'LineWidth',2);
amp_val = 0;
for ii=1:4
tf_f_here = tf_i_here + amp_val;
plot(tf_f_here*[1 1],[y_here-.2 y_here],'-','Color',local_rgb('Maroon'),'LineWidth',2);
% text(tf_f_here,y_here+.1,num2str(amp_val),'FontSize',8);
amp_val = amp_val+2;
end
if k == 1
x_fc = (1:length(fc));
YTL = [];
for j = 1:length(CFs)
YTL{j} = [num2str(round(CFs(j))) ' Hz'];
end
end
set(gca,'YTick',x_fc-1);
set(gca,'YTickLabel',YTL);
set(gca,'XTick',ti:10:tf-10);
% set(gca,'XTickLabel',[])
ht = text(0.15,0.97,[extra_str models{k} extra_after],'Units','Normalized','FontSize',MediumFontSize);
Pos = get(gcf,'Position');
Pos(3:4) = [360 300]; % Previous size: [500 300];
set(gcf,'Position',Pos);
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig13-profile-' models{k} suff_str];
end
end
data.env_mean = env_mean;
data.env_std = env_std;
data.env_scale = env_scale;
data.env_scale_std = env_scale/factor_std;
data.insig = insig;
data.starting_phases = starting_phases;
data.fs = fs;
data.models = models;
if flags.do_plot
data.figure_flag = 'do_fig13';
data.figure_handle = figure_handle;
data.figure_name = figure_name;
end
end
%% ------ FIG 14 ----------------------------------------------------------
if flags.do_fig14a || flags.do_fig14b
% Adapted from g20190618_investigating_IC_CN.m
%%% Stimulus generation:
if flags.do_fig14a
L=30; % dB SPL
elseif flags.do_fig14b
L=70; % dB SPL
end
dur= 300e-3; % ms
t = 0:1/fs:dur-1/fs;
t = t(:); % column vector
fc = 1000; % Hz
dur_samples = length(t);
fmod_step = 5;
fmods = 10:fmod_step:130; % 250;
N_signals = length(fmods);
insig = zeros(dur_samples,N_signals); % Memory allocation
% Up/down cosine ramp (fixed)
dur_ramp_ms = 5;
dur_ramp = round((dur_ramp_ms*1e-3)*fs); % duration ramp in samples
rp = ones(dur_samples,1);
rp(1:dur_ramp) = rampup(dur_ramp);
rp(end-dur_ramp+1:end) = rampdown(dur_ramp);
%Stimulus
for i = 1:length(fmods)
fmod = fmods(i);
mod_index=1;
carrier=sin(2*pi*fc.*t);
modulator=mod_index*cos(2*pi*fmod.*t+pi);
insig_tmp=(1+modulator).*carrier;
insig_tmp = scaletodbspl(insig_tmp,L,dBFS);
insig(:,i) = rp.*insig_tmp;
end
ti = 190e-3;
tf = 290e-3;
for k = 1:N_models
%%% Loading flags and keyvals
[fg,kv] = local_get_flags(models{k});
fc_flags = fg.fc_flags;
afb_flags = fg.afb_flags;
afb_keyvals = kv.afb_keyvals;
ihc_flags = fg.ihc_flags;
an_flags = fg.an_flags;
an_keyvals = kv.an_keyvals;
mfb_flags = fg.mfb_flags;
mfb_keyvals = kv.mfb_keyvals;
%%%
switch L
case 70
suff_lvl='';
case 30
suff_lvl = '-30-dB';
otherwise
error('This function has been tested with levels of L=30 or 70 dB SPL only')
% suff_lvl = sprintf('-%.0f-dB',L);
end
fname = ['fig14_MTF-' models{k} suff_lvl];
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
bRun = 0;
outsig = c.outsig;
subfs = c.subfs;
mfc = c.mfc;
else
bRun = 1;
outsig = [];
end
mfc_target = 100;
if bRun && ~isempty(afb_keyvals)
idx = find(strcmp(afb_keyvals,'compression_n'));
if ~isempty(idx)
compression_n = afb_keyvals{idx+1};
else
error('mfb_keyvals should contain BMF')
end
end
if bRun && ~isempty(an_keyvals)
% Loading common parameters
switch models{k}
case {'verhulst2015','verhulst2018','zilany2014','bruce2018'}
if strcmp(an_keyvals{1},'numH')
numH = an_keyvals{2};
else
error('an_keyvals should contain numH')
end
if strcmp(an_keyvals{3},'numM')
numM = an_keyvals{4};
else
error('an_keyvals should contain numM')
end
if strcmp(an_keyvals{5},'numL')
numL = an_keyvals{6};
else
error('an_keyvals should contain numM')
end
end
switch models{k}
case {'zilany2014','bruce2018'}
if ~isempty(mfb_keyvals)
idx = find(strcmp(mfb_keyvals,'BMF'));
if ~isempty(idx)
BMF = mfb_keyvals{idx+1};
mfb_keyvals(idx:idx+1) = []; % removed from the keyvals
else
error('mfb_keyvals should contain BMF')
end
end
switch models{k}
case 'bruce2018'
idx = find(strcmp(an_keyvals,'psth_binwidth'));
if ~isempty(idx)
% Only true for bruce2018:
psth_binwidth = an_keyvals{idx+1};
end
nrep = 100;
end
end
end
switch models{k}
case 'dau1997'
if bRun
afb_kv = {'basef',fc,'flow',fc,'fhigh',fc,'dboffset',dBFS};
for j = 1:N_signals
[out_tmp,fc,mfc_here] = dau1997(insig(:,j),fs,afb_kv{:},afb_flags{:}, ...
ihc_flags{:},an_flags{:},mfb_flags{:},mfb_keyvals{:});
mfc_idx = find(mfc_here<mfc_target,1,'last');
outsig(:,j) = out_tmp{1}(:,mfc_idx);
if j == 1
mfc = mfc_here(mfc_idx);
if size(out_tmp{1},1) == size(insig,1)
% Makers sure that no other default subfs is used
subfs = fs;
end
end
end
c = [];
c.outsig = outsig;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
case 'zilany2014'
if bRun
insigL = size(insig,1);
kv={'fiberType',4,'numH',numH,'numM',numM,'numL',numL,'nrep',1}; % 1 repetition is enough (no PSTH here)
for j = 1:N_signals
mean_rate = zilany2014(insig(:,j),fs,fc,kv{:},afb_flags{:},'silent');
mean_rate = mean_rate(1:insigL);
[out_ic,~,~,par] = carney2015(mean_rate,BMF,fs,mfb_keyvals{:});
outsig(:,j) = out_ic(1:insigL); % column array
if j == 1
subfs = fs;
mfc = BMF;
amt_disp(sprintf('Zilany2014: CN: tau_exc=%f, tan_inh=%f, D=%f, S=%f',par.tau_ex_cn,par.tau_inh_cn,par.cn_delay,par.Sinh_cn));
amt_disp(sprintf('Zilany2014: IC: tau_exc=%f, tan_inh=%f, D=%f, S=%f',par.tau_ex_ic,par.tau_inh_ic,par.ic_delay_inh,par.Sinh_ic));
end
end
c = [];
c.outsig = outsig;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
case 'verhulst2015'
if bRun
fc_green = local_m2hz(401);
idx = find(fc_green>fc,1,'last');
out = verhulst2015(insig,fs,fc_flags{:},afb_flags{:},ihc_flags{:},an_flags{:}, ...
kv.an_keyvals{:},mfb_flags{:},'silent'); % no_ihc just reduce the data load here
subfs = out(1).fs_abr;
%%%
numTot = numH + numM + numL;
%%%
for j = 1:N_signals
outsig(:,j) = out(j).ic(:,idx)/numTot;
end
mfc = [];
c = [];
c.outsig = outsig;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
case 'verhulst2018'
if bRun
fc_green = local_m2hz(401);
idx = find(fc_green>fc,1,'last');
out = verhulst2018(insig,fs,fc_flags{:},afb_flags{:},ihc_flags{:},an_flags{:}, ...
kv.an_keyvals{:},mfb_flags{:},'no_ihc','no_an','no_cn','silent');
subfs = out(1).fs_abr;
%%%
numTot = numH + numM + numL;
%%%
for j = 1:N_signals
outsig(:,j) = out(j).ic(:,idx)/numTot;
end
mfc = [];
c = [];
c.outsig = outsig;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
case 'bruce2018'
if bRun
fc_kv = {};
kv = {'numH',numH,'numM',numM,'numL',numL,'nrep',nrep, ...
'specificSRautoTiming','psthbinwidth_mr',psth_binwidth};
for j = 1:N_signals
out_tmp = bruce2018(insig(:,j),fs,fc,fc_kv{:},kv{:},afb_flags{:},'silent');
if j == 1
dt = psth_binwidth;
fs_an = 1/dt;
insigL = round(dur*fs_an);
end
out_psth = out_tmp.psth(1:insigL);
outsig(:,j) = carney2015(out_psth,BMF,fs_an,mfb_keyvals{:});
if j == 1
subfs = fs_an;
mfc = BMF;
end
end
c = [];
c.outsig = outsig;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
case 'relanoiborra2019'
if bRun
afb_kv = {'basef',fc,'flow',fc,'fhigh',fc};
for j = 1:N_signals
[out_tmp,mfc_here,~,~] = relanoiborra2019_featureextraction(insig(:,j), fs, afb_kv{:}, afb_flags{:});
mfc_idx = find(mfc_here<mfc_target,1,'last');
outsig(:,j) = squeeze(out_tmp(:,1,mfc_idx));
subfs = fs;
if j == 1
mfc = mfc_here(mfc_idx);
end
end
c = [];
c.outsig = outsig;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
case 'king2019'
if bRun
for j = 1:N_signals
afb_kv = {'basef',fc,'flow',fc,'fhigh',fc,'compression_n',compression_n,'dboffset',dBFS,'subfs',fs};
[out_tmp,fc,mfc_here,extras] = king2019(insig(:,j),fs,afb_kv{:});
mfc_idx = find(mfc_here<mfc_target,1,'last');
out_tmp = squeeze(out_tmp);
mfc = mfc_here(mfc_idx);
outsig(:,j) = out_tmp(:,mfc_idx);
if j == 1
subfs = extras.subfs;
end
end
c = [];
c.outsig = outsig;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
case 'osses2021'
if bRun
afb_kv = {'basef',fc,'flow',fc,'fhigh',fc,'dboffset',dBFS};
for j = 1:N_signals
[out_tmp,fc,mfc_here] = osses2021(insig(:,j),fs,afb_kv{:},afb_flags{:},ihc_flags{:},an_flags{:},mfb_flags{:});
mfc_idx = find(mfc_here<mfc_target,1,'last');
outsig(:,j) = out_tmp{1}(:,mfc_idx);
subfs = fs;
if j == 1
mfc = mfc_here(mfc_idx);
end
end
c = [];
c.outsig = outsig;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
end
if ~isempty(outsig)
switch models{k}
case {'dau1997','relanoiborra2019','osses2021'}
YL = [-100 1000];
case {'zilany2014','verhulst2015','verhulst2018','bruce2018'}
YL = [-200 500];
case 'king2019'
YL = [-.1 1]*1e-4;
end
idxi = round(ti*subfs)+1;
idxf = round(tf*subfs);
% Maximum values:
[vals_ma,idx_ma] = max(outsig(idxi:idxf,:));
MFic(k,1:N_signals) = vals_ma; % figure; plot(fmods,vals); hold on
MFic_norm(k,1:N_signals) = vals_ma / max(abs(vals_ma));
switch L
case 30
MF_label = 'a) AM tones at 30 dB';
case 70
MF_label = 'b) AM tones at 70 dB';
end
end
end
if flags.do_plot
%%% Custom figure format here:
for k = 1:N_models % little adjustments in format for the coming figures
switch models{k}
case 'dau1997'
Marker = [Markers{k} '-'];
LW = LineWidth(k);
case 'zilany2014'
Marker = [Markers{k} LineStyle{k}];
LW = LineWidth(k);
case 'bruce2018'
Marker = LineStyle{k};
LW = 4;
case 'king2019'
Marker = LineStyle{k};
LW = 4;
otherwise
Marker = LineStyle{k};
LW = LineWidth(k);
end
if k == 1
figure;
end
opts = {Marker,'Color',Colours{k},'LineWidth',LW,'MarkerFaceColor',Colours{k}}; % ,'MarkerFaceColor','w','Markers',Markers{k},'MarkerSize',MarkersSize(k)}
plot(fmods,MFic_norm(k,:),opts{:}); grid on, hold on
end
xlabel('Modulation frequency (Hz)')
ylabel('On-CF response (Normalised)')
xlim([min(fmods)-5 max(fmods)+5]);
ylim([-0.05 1.15])
Pos = get(gcf,'Position');
Pos(3:4) = [400 360]; % [550 360];
set(gcf,'Position',Pos);
XTL = [];
for i = 1:length(fmods)
if mod(i,2) == 1
XTL{i} = num2str(fmods(i));
else
XTL{i} = '';
end
end
set(gca,'XTick' ,fmods);
set(gca,'XTickLabel',XTL);
set(gca,'YTick',-1:.1:1);
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig14-modulation-strength' suff_lvl];
title(MF_label);
end
%%% End: figure
data.figure_flag = 'do_fig14';
data.fmods = fmods;
data.MFic = MFic;
data.MFic_norm = MFic_norm;
data.models = models;
if flags.do_plot
data.figure_handle = figure_handle;
data.figure_name = figure_name;
end
end
%% ------ FIG 15 ----------------------------------------------------------
if flags.do_fig15
% Local script: g20191107_rerun_Verhulst2018a.m
% Generating click
lvl = 70; % lvl_dBnHL + 30;
p0 = 2e-5;
dur_click = 1; % s
N_samples = dur_click*fs;
t=(0:1/fs:dur_click);
click_duration = (100e-6*fs); % 100 us click (Osses and Verhulst 2019 used 80 us)
N_length = fs/10;
sil_skip = N_length-click_duration; % 90.1 ms
samples_click = [];
samples_click_even = [];
Nr_clicks = floor(fs*dur_click/(sil_skip+click_duration));
idx_offset = round(10e-3*fs); % click starts 10 ms after time 0
for i = 1:Nr_clicks
start_sample = (i-1)*N_length+idx_offset;
if mod(i,2) == 1
samples_click = [samples_click start_sample+click_duration:start_sample+click_duration+click_duration];
else
samples_click_even = [samples_click_even start_sample+click_duration:start_sample+click_duration+click_duration];
end
end
A = 2*sqrt(2)*p0*10^(lvl/20);
insig = zeros(N_samples,1);
insig(samples_click) = A;
insig(samples_click_even) = -A;
for k = 1:N_models
%%% Loading flags and keyvals
[fg,kv] = local_get_flags(models{k});
fc_flags = fg.fc_flags;
afb_flags = fg.afb_flags;
ihc_flags = fg.ihc_flags;
an_flags = fg.an_flags;
afb_keyvals = kv.afb_keyvals;
an_keyvals = kv.an_keyvals;
mfb_flags = fg.mfb_flags;
% nomfb_flags = fg.nomfb_flags;
mfb_keyvals = kv.mfb_keyvals;
%%%
fname = ['fig15_click-' models{k}];
c = amt_cache('get',fname,flags.cachemode);
if ~isempty(c)
bRun = 0;
out = c.out;
subfs = c.subfs;
mfc = c.mfc;
else
bRun = 1;
out = [];
end
mfc_target = 100;
if bRun && ~isempty(afb_keyvals)
idx = find(strcmp(afb_keyvals,'compression_n'));
if ~isempty(idx)
compression_n = afb_keyvals{idx+1};
else
error('mfb_keyvals should contain BMF')
end
end
if bRun && ~isempty(an_keyvals)
% Loading common parameters
switch models{k}
case {'verhulst2015','verhulst2018','zilany2014','bruce2018'}
if strcmp(an_keyvals{1},'numH')
numH = an_keyvals{2};
else
error('an_keyvals should contain numH')
end
if strcmp(an_keyvals{3},'numM')
numM = an_keyvals{4};
else
error('an_keyvals should contain numM')
end
if strcmp(an_keyvals{5},'numL')
numL = an_keyvals{6};
else
error('an_keyvals should contain numM')
end
end
switch models{k}
case {'zilany2014','bruce2018'}
if ~isempty(mfb_keyvals)
idx = find(strcmp(mfb_keyvals,'BMF'));
if ~isempty(idx)
BMF = mfb_keyvals{idx+1};
mfb_keyvals(idx:idx+1) = []; % removed from the keyvals
else
error('mfb_keyvals should contain BMF')
end
end
switch models{k}
case 'bruce2018'
idx = find(strcmp(an_keyvals,'psth_binwidth'));
if ~isempty(idx)
% Only true for bruce2018:
psth_binwidth = an_keyvals{idx+1};
end
nrep = 100;
end
idx_step = 8; % used in zilany2014 and bruce2018
end
end
switch models{k}
case 'dau1997'
if bRun
amt_disp('Calculating dau1997...','volatile');
subfs = 20000; % Hz, fs to be used in the modulation filter bank
afb_kv = {'subfs',subfs,'dboffset',dBFS,'basef',[]};
t_start = tic;
[out_tmp,fc_here,mfc_here] = dau1997(insig,fs,afb_kv{:},afb_flags{:},ihc_flags{:},an_flags{:},mfb_flags{:});
out.t_elapsed = toc(t_start);
mfc_idx = find(mfc_here<mfc_target,1,'last');
Nr_fc = length(fc_here);
outsig = [];
fcs = [];
for j = 1:Nr_fc
if size(out_tmp{j},2) >= mfc_idx
fcs(end+1) = fc_here(j); % only adding the fcs of filters containing mfc_idx
outsig(:,end+1) = out_tmp{j}(:,mfc_idx);
end
end
out.fcs = fcs;
out.oustig = outsig;
out.outsig_description = 'Modulation filter bank output';
mfc = mfc_here(mfc_idx);
c = [];
c.out = out;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
factor = 1;
unit = 'MU';
YL = [-20 560];
YT = 0:50:500;
case 'zilany2014'
if bRun
fcs = local_m2hz(401);
idxs = length(fcs):-idx_step:1;
fcs = fcs(idxs);
fcs = fcs(fcs>125);
kv={'fiberType',4,'numH',numH,'numM',numM,'numL',numL,'nrep',1};
amt_disp(''); % start of volatile display
for j = 1:length(fcs)
amt_disp(sprintf('Calculating zilany2014: band %.0f of %.0f\n',j,length(fcs)),'volatile');
t_start = tic;
mean_rate = zilany2014(insig,fs,fcs(j),afb_flags{:},kv{:},'silent');
if j == 1
L = size(insig,1);
end
mean_rate = mean_rate(1:L);
[ic_out,~,~,par] = carney2015(mean_rate, BMF,fs, mfb_keyvals{:});
t_elapsed = toc(t_start);
out.ic(:,j) = ic_out(1:L);
out.t_elapsed(j) = t_elapsed;
if j == 1
subfs = fs;
amt_disp(sprintf('zilany2014 (CN): tau_exc=%f, tan_inh=%f, D=%f, S=%f',par.tau_ex_cn,par.tau_inh_cn,par.cn_delay,par.Sinh_cn));
amt_disp(sprintf('zilany2014 (IC): tau_exc=%f, tan_inh=%f, D=%f, S=%f',par.tau_ex_ic,par.tau_inh_ic,par.ic_delay_inh,par.Sinh_ic));
end
end
amt_disp(); %end of volatile display
out.fcs = fcs;
out.fcs_description = 'CFs that were tested';
mfc = [];
out.ic_all = sum(out.ic,2);
out = rmfield(out,'ic'); % to reduce size
c = [];
c.out = out;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
factor = 1;
unit = 'spikes/s';
YL = [-15 145];
YT = 0:15:130;
case 'verhulst2015'
if bRun
t_start = tic;
amt_disp('Calculating verhulst2015...','volatile');
out = verhulst2015(insig,fs,fc_flags{:},afb_flags{:},ihc_flags{:},an_flags{:}, ...
an_keyvals{:},mfb_flags{:},'silent');
t_elapsed = toc(t_start);
subfs = out(1).fs_abr;
out.t_elapsed = t_elapsed;
out = rmfield(out,{'fs_bm','ihc','an_summed','ic'});
mfc = [];
c = [];
c.out = out;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
factor = 1e6; unit = '\muV';
YL = [-.14 .18];
YT = -.3:.03:.3;
case 'verhulst2018'
if bRun
t_start = tic;
amt_disp('Calculating verhulst2018...','volatile');
out = verhulst2018(insig,fs,fc_flags{:},afb_flags{:},ihc_flags{:},an_flags{:}, ...
kv.an_keyvals{:},mfb_flags{:},'no_ihc','no_an','no_cn','silent');
t_elapsed = toc(t_start);
subfs = out(1).fs_abr;
out.t_elapsed = t_elapsed;
out = rmfield(out,{'fs_bm','ic'});
mfc = [];
c = [];
c.out = out;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
factor = 1e6; unit = '\muV';
YL = [-.22 .32];
YT = -.3:.05:.3;
case 'bruce2018'
if bRun
fcs = local_m2hz(401);
idxs = length(fcs):-idx_step:1;
fcs = fcs(idxs);
fcs = fcs(fcs>125);
kv = {'numH',numH,'numM',numM,'numL',numL,'nrep',nrep, ...
'specificSRautoTiming','psthbinwidth_mr',psth_binwidth};
kv_one_rep = {'numH',numH,'numM',numM,'numL',numL,'nrep',1,'specificSRautoTiming'};
dt = psth_binwidth;
subfs = 1/dt;
L = round(dur_click*subfs);
for j = 1:length(fcs)
amt_disp(sprintf('Calculating bruce2018: band %.0f of %.0f\n',j,length(fcs)),'volatile');
t_start = tic;
%%% To measure computing time only:
out_tmp = bruce2018(insig,fs,fcs(j),afb_flags{:},kv_one_rep{:},'silent');
ic_out = carney2015(out_tmp.psth(1:L),BMF,subfs, mfb_keyvals{:});
t_elapsed = toc(t_start);
%%% To assess a reliable PSTH:
out_tmp = bruce2018(insig,fs,fcs(j),afb_flags{:},kv{:},'silent');
out_psth = out_tmp.psth(1:L);
ic_out = carney2015(out_psth,BMF,subfs,mfb_keyvals{:});
out.ic(1:L,j) = ic_out(1:L);
out.t_elapsed(j) = t_elapsed;
end
out.fcs = fcs;
out.fcs_description = 'CFs that were tested';
mfc = [];
out.ic_all = sum(out.ic,2);
out = rmfield(out,'ic'); % to reduce size
c = [];
c.out = out;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
factor = 1;
unit = 'spikes/s';
YL = [-15 145];
YT = 0:15:130;
case 'king2019'
if bRun
subfs = 20000;
afb_kv = {'basef',[],'flow',80,'fhigh',8000, ... % arbitrary
'compression_n',compression_n,'dboffset',dBFS,'subfs',subfs, ...
'modbank_Nmod',10}; % 10 modulation filters instead of 5
t_start = tic;
amt_disp('Calculating king2019...','volatile');
[outsig,fcs,mfc_here] = king2019(insig,fs,afb_kv{:},afb_flags{:},ihc_flags{:},an_flags{:},mfb_flags{:});
out.t_elapsed = toc(t_start);
mfc_idx = find(mfc_here<mfc_target,1,'last');
outsig = squeeze(outsig(:,:,mfc_idx));
out.fcs = fcs;
out.oustig = outsig;
out.outsig_description = 'Modulation filter bank output';
mfc = mfc_here(mfc_idx);
c = [];
c.out = out;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
factor = 1e4;
unit = 'a.u. x 10^{-4}';
YL = [-0.1 2.1];
YT = [0:.2:2];
case 'relanoiborra2019'
if bRun
% afb_kv = {'erbspacebw','no_internalnoise'};
t_start = tic;
amt_disp('Calculating relanoiborra2019...','volatile');
[out_tmp,mfc_here,~,fc_here] = relanoiborra2019_featureextraction(insig, fs, afb_flags{:});
out.t_elapsed = toc(t_start);
mfc_idx = find(mfc_here<mfc_target,1,'last');
mfc = mfc_here(mfc_idx);
Nr_fc = length(fc_here);
outsig = [];
fcs = [];
for j = 1:Nr_fc
if mfc < fc_here(j)/4 % limiting the bands (Verhey1999)
if size(out_tmp,3) >= mfc_idx
fcs(end+1) = fc_here(j); % only adding the fcs of filters containing mfc_idx
outsig(:,end+1) = out_tmp(:,j,mfc_idx);
% outsig_adapt(:,end+1) = out_tmp_an(:,j);
end
end
end
out.Nr_fc = Nr_fc;
out.fcs = fcs;
out.oustig = outsig;
out.outsig_description = 'Modulation filter bank output';
subfs = fs;
c = [];
c.out = out;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
factor = 1;
unit = 'MU';
YL = [-20 500];
YT = 0:50:450;
case 'osses2021'
if bRun
subfs = 20000;
afb_kv = {'subfs',subfs,'dboffset',dBFS};
t_start = tic;
amt_disp('Calculating osses2021...','volatile');
[out_tmp,fc_here,mfc_here] = osses2021(insig,fs,afb_kv{:},afb_flags{:},ihc_flags{:},an_flags{:},mfb_flags{:});
out.t_elapsed = toc(t_start);
mfc_idx = find(mfc_here<mfc_target,1,'last');
Nr_fc = length(fc_here);
outsig = [];
fcs = [];
for j = 1:Nr_fc
if size(out_tmp{j},2) >= mfc_idx
fcs(end+1) = fc_here(j); % only adding the fcs of filters containing mfc_idx
outsig(:,end+1) = out_tmp{j}(:,mfc_idx);
end
end
out.fcs = fcs;
out.oustig = outsig;
out.outsig_description = 'Modulation filter bank output';
mfc = mfc_here(mfc_idx);
c = [];
c.out = out;
c.subfs = subfs;
c.mfc = mfc;
amt_cache('set',fname,c);
end
factor = 1;
unit = 'MU';
YL = [-20 230];
YT = 0:20:220;
end
if ~isempty(out)
w5 = [];
switch models{k}
case {'dau1997','king2019'}
if ~isfield(out,'Nr_fc')
Nr_fc = 31;
else
Nr_fc = out.Nr_fc;
end
num_cfs = length(out.fcs);
w5 = sum(out.oustig,2)/num_cfs;
case {'relanoiborra2019','osses2021'}
num_cfs = length(out.fcs);
if num_cfs == 26
if ~isfield(out,'Nr_fc')
Nr_fc = 31;
else
Nr_fc = out.Nr_fc;
end
elseif num_cfs == 51
if ~isfield(out,'Nr_fc')
Nr_fc = 60;
else
Nr_fc = out.Nr_fc;
end
end
w5 = sum(out.oustig,2)/num_cfs;
case {'zilany2014','bruce2018'}
num_cfs = length(out.fcs);
w5 = out.ic_all/num_cfs;
if ~isfield(out,'Nr_fc')
Nr_fc = num_cfs;
else
Nr_fc = out.Nr_fc;
end
case {'verhulst2015','verhulst2018'}
if ~isfield(out,'Nr_fc')
Nr_fc = 401;
else
Nr_fc = out.Nr_fc;
end
num_cfs = Nr_fc;
w5 = out.w5;
end
% -------------------------------------------------------------
% Time elapsed:
if length(out.t_elapsed) ~= 1
t_elapsed_here = sum(out.t_elapsed);
else
t_elapsed_here = out.t_elapsed;
end
t_elapsed(1,k) = t_elapsed_here;
t_elapsed(2,k) = Nr_fc;
t_elapsed(3,k) = t_elapsed(1,k)/Nr_fc;
% if k == 1
% t_elapsed_description = 'Row 1: Total time elapsed (s); Row 2: Total number of simulated channels; Row 3 = time per channel (Row 1/Row 2)';
% end
amt_disp(sprintf('%s: # of channels: %.0f; # of bands with a BMF of 100 Hz: %.0f',pad(models{k},16),Nr_fc,num_cfs),'documentation');
amt_disp(sprintf(' Time elapsed=%.3f s (%.4f s per channel, %.0f channels)',t_elapsed(1,k),t_elapsed(3,k),t_elapsed(2,k)),'documentation');
% END Time elapsed --------------------------------------------
t_ms = 1000*(1:length(w5))/subfs;
% disp('Using Wave-V estimate only...')
efr_here = w5;
if flags.do_plot
figure; % For each model there is one new figure
figure_handle(end+1) = gcf;
figure_name{end+1} = ['fig15-click-' models{k}];
opts = {'LineStyle','-' ,'Color',Colours{k},'LineWidth',LineWidth(k)};
opts_neg = {'LineStyle','--','Color',local_rgb('Gray'),'LineWidth',3};
offx = 10;
end
t_ms_orig = t_ms;
%%% Getting the clicks to be plotted only
idxs = [1 2 9 10]; % Click numbers to be plotted
N = length(efr_here)/Nr_clicks;
M = Nr_clicks;
t_ms_orig = t_ms;
t_ms = reshape(t_ms ,N,M);
efr_here = reshape(efr_here,N,M);
efr_here = efr_here(:,idxs);
Nr_clicks_here = length(idxs);
t_ms = t_ms(:,idxs);
efr_here = efr_here(:); % truncated clicks back to a one-column array
if Nr_clicks_here == 4
efr_here = reshape(efr_here,N*2,Nr_clicks_here/2);
end
switch models{k}
case {'zilany2014','bruce2018'}
t_ms_here = t_ms_orig(1:size(efr_here,1));
idx_pos = find(efr_here(:,2)>=0); % positive-valued amplitudes
idx_neg = find(efr_here(:,2)<0); % negative-valued amplitudes
efr2plot = nan(size(efr_here(:,2)));
efr2plot(idx_pos) = efr_here(idx_pos,2);
if flags.do_plot
pl(k) = plot(t_ms_here,factor*efr2plot,opts{:}); hold on, grid on
end
efr2plot = nan(size(efr_here(:,2)));
efr2plot(idx_neg) = zeros(size(efr_here(idx_neg,2)));
if flags.do_plot
plot(t_ms_here,factor*efr2plot,opts_neg{:}); hold on, grid on
end
otherwise
if flags.do_plot
pl(k) = plot(t_ms_orig(1:size(efr_here,1)),factor*efr_here(:,2),opts{:}); hold on, grid on
end
end
efr_here = efr_here(:);
if flags.do_plot
xlabel('Time relative to Click A onset (ms)');
title(models{k})
step_time = 25; % 30
XT = 10:step_time:210;
XT_for_tick = XT;
% XL = [-5 205];
XTL = [];
for ii = 1:length(XT)
if mod(ii,2) == 1
XTL{ii} = num2str(XT_for_tick(ii)-offx);
else
XTL{ii} = '';
end
end
set(gca,'XTick' ,XT);
set(gca,'XTickLabel',XTL);
ylim(YL);
YTL = [];
for ii = 1:length(YT)
if mod(ii,2) == 1
YTL{ii} = num2str(YT(ii));
else
YTL{ii} = '';
end
end
set(gca,'YTick',YT);
set(gca,'YTickLabel',YTL);
ylabel(['(' unit ')']);
Pos = get(gcf,'Position');
Pos(3:4) = [380 300];
set(gcf,'Position',Pos);
end % flags.do_plot
end % end ~ifempty(out)
Nr_samples = length(efr_here);
output_buf = reshape(efr_here,round(Nr_samples/Nr_clicks_here),Nr_clicks_here);
Amp_max(k,:) = max(output_buf);
Amp_min(k,:) = min(output_buf);
end % end for k
data.Amp_max = Amp_max;
data.Amp_min = Amp_min;
data.Amp_pp = Amp_max - Amp_min;
data.models = models;
if flags.do_plot
data.figure_flag = 'do_fig15';
data.figure_handle = figure_handle;
data.figure_name = figure_name;
end
end
%%% End of file exp_osses2022.m
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Start of subfunctions:
function [flags,keyvals] = local_get_flags(model_str)
switch model_str
case {'verhulst2015','verhulst2018'}
kSR_H = 68.5; % spikes/s, spontaneous rate: only Verhulst 2018
kSR_M = 10; % spikes/s, spontaneous rate: Verhulst 2015 and 2018
kSR_L = 1; % spikes/s, spontaneous rate: Verhulst 2015 and 2018
end
numH = 12;
numM = 4;
numL = 4;
bAMT = 1; % Is AMT v1.0
switch model_str
case 'dau1997'
fc_flags = [];
afb_flags = {};
afb_keyvals = [];
ihc_flags = {'ihc','ihc_dau1996'};
noihc_flags = {'no_ihc'};
an_flags = {'adt','adt_dau1997'};
noan_flags = {'no_adt','no_mfb'};
an_keyvals = [];
mfb_flags = {'mfb','mfb_dau1997'};
nomfb_flags = {'no_mfb'};
mfb_keyvals = {'subfs',[]}; % subfs will be the same as fs
case 'osses2021'
fc_flags = [];
afb_flags = {'afb_osses2021'};
afb_keyvals = [];
ihc_flags = {'ihc','ihc_breebaart2001'};
noihc_flags = {'no_ihc'};
an_flags = {'adt', 'adt_osses2021'};
noan_flags = {'no_adt','no_mfb'};
an_keyvals = [];
mfb_flags = {'mfb','mfb_jepsen2008'};
nomfb_flags = {'no_mfb'};
mfb_keyvals = [];
case 'relanoiborra2019'
fc_flags = [];
afb_flags = {'no_internalnoise','erbspacebw'};
afb_keyvals = [];
ihc_flags = {'ihc','ihc_relanoiborra2019'};
noihc_flags = {'no_ihc'};
an_flags = {'adt', 'adt_relanoiborra2019'};
noan_flags = {'no_an'};% ,'no_mfb'};
an_keyvals = [];
mfb_flags = {'mfb','mfb_jepsen2008'};
nomfb_flags = {'no_mfb'};
mfb_keyvals = [];
case 'king2019'
fc_flags = [];
afb_flags = {'afb','compression_brokenstick'};
afb_keyvals = {'compression_n',0.3};
ihc_flags = {'ihc'};
noihc_flags = {'no_ihc'};
an_flags = {'adt'};
noan_flags = {'no_adt','no_mfb'};
an_keyvals = [];
mfb_flags = {'mfb'};
nomfb_flags = {'no_mfb'};
mfb_keyvals = [];
case 'verhulst2015'
fc_flags = {'abr'}; % abr = 401 frequency channels
afb_flags = {'no_outerear','middleear','no_v','no_oae','no_y'}; % these are the defaults
afb_keyvals = [];
ihc_flags = {'ihc'}; % this is the default
noihc_flags = {'no_ihc'};
an_flags = {'an'};
noan_flags = {'no_an','no_cn','no_ic'}; % if no_an, then no_cn and no_ic
an_keyvals = {'numH',numH,'numM',numM,'numL',numL,'kSR_H',kSR_H,'kSR_M',kSR_M,'kSR_L',kSR_L};
mfb_flags = {'no_cn','ic'};
nomfb_flags = {'no_cn','no_ic'};
mfb_keyvals = [];
case 'verhulst2018'
fc_flags = {'abr'}; % abr = 401 frequency channels
afb_flags = {'no_outerear','middleear','no_v','no_oae','no_y'}; % these are the defaults
afb_keyvals = [];
ihc_flags = {'ihc'}; % this is the default
noihc_flags = {'no_ihc'};
an_flags = {'an'};
noan_flags = {'no_an','no_cn','no_ic'};
an_keyvals = {'numH',numH,'numM',numM,'numL',numL,'kSR_H',kSR_H,'kSR_M',kSR_M,'kSR_L',kSR_L};
mfb_flags = {'no_cn','ic'};
nomfb_flags = {'no_cn','no_ic'};
mfb_keyvals = [];
case 'zilany2014'
species = 'human';
noiseType = 'fixedFGn';
% noiseType = 'varFGn';
fc_flags = [];
if bAMT
afb_flags = {species};
else
afb_flags = {'middleear',species}; % this is the default, making sure that 'species' is always loaded
end
afb_keyvals = [];
ihc_flags = {'ihc'}; % this is the default
noihc_flags = {'no_ihc'};
an_flags = {'an',species,noiseType};
if numH ~= 0
an_flags(end+1) = {'anfH'};
end
if numM ~= 0
an_flags(end+1) = {'anfM'};
end
if numL ~= 0
an_flags(end+1) = {'anfL'};
end
noan_flags = {'no_an',species,noiseType};
an_keyvals = {'numH',numH,'numM',numM,'numL',numL,'psth_binwidth',0.5e-3};
mfb_flags = {'cn','ic'};
nomfb_flags = {'no_cn','no_ic'};
mfb_keyvals = {'BMF',90,'ic_delay_inh',0.0011};
case 'bruce2018'
species = 'human';
noiseType = 'fixedFGn';
% noiseType = 'varFGn';
fc_flags = [];
if bAMT
afb_flags = {species};
else
afb_flags = {'middleear',species}; % this is the default, making sure that 'species' is always loaded
end
afb_keyvals = [];
ihc_flags = {'ihc'}; % this is the default
noihc_flags = {'no_ihc'};
an_flags = {'an',species,noiseType};
if numH ~= 0
an_flags(end+1) = {'anfH'};
end
if numM ~= 0
an_flags(end+1) = {'anfM'};
end
if numL ~= 0
an_flags(end+1) = {'anfL'};
end
noan_flags = {'no_an',species,noiseType};
an_keyvals = {'numH',numH,'numM',numM,'numL',numL,'psth_binwidth',0.5e-3};
mfb_flags = {'cn','ic'};
nomfb_flags = {'no_cn','no_ic'};
mfb_keyvals = {'BMF',90,'ic_delay_inh',0.0011};
end
flags = [];
flags.fc_flags = fc_flags;
flags.afb_flags = afb_flags;
flags.ihc_flags = ihc_flags;
flags.noihc_flags = noihc_flags;
flags.an_flags = an_flags;
flags.noan_flags = noan_flags;
flags.mfb_flags = mfb_flags;
flags.nomfb_flags= nomfb_flags;
keyvals = [];
keyvals.afb_keyvals = afb_keyvals;
keyvals.an_keyvals = an_keyvals;
keyvals.mfb_keyvals = mfb_keyvals;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function rms_val = local_rmsdb(insig)
rms_val = 20*log10(rms(insig));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ac_target,dc_target,vrest] = local_get_ac_dc_osses2022(ihc_target,ac_reg,dc_reg)
% function [ac_target,dc_target,vrest] = local_get_ac_dc_osses2022(ihc_target,ac_reg,dc_reg)
%
% DC: is computed from the signal (mean value using the ac_reg samples), and
% then this value is referenced to the resting potential
% AC: I compute it here as the peak-to-peak voltage, in line with Russel and
% Palmer 1986
vrest = median(ihc_target(dc_reg,:)); % median of the silent initial segment
dc_target = mean(ihc_target(ac_reg,:))-vrest;
v_min = min(ihc_target(ac_reg,:));
v_max = max(ihc_target(ac_reg,:));
dist_ac_pp = v_max-v_min; % peak to peak
ac_target = dist_ac_pp; % approximation to the RMS of the AC
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [f,x] = local_m2hz(Nr_sections,k)
% function [f,x] = local_m2hz(Nr_sections,k)
%
% 1. Description:
% See Greenwood 1990, Equation 1 with A = 165.4, k = 0.85.
% bApex_to_base is referenced to Helicotrema.
%
% Programmed by Alejandro Osses, WAVES, UGent, Belgium, 2018-2020
if nargin < 2
k = 0.85; % As used in the models by Verhulst et al
end
if nargin == 0
Nr_sections = 500;
end
cochleaLength = 0.035; % m
helicotremaLength = 0.001; % m
A = 165.4118; % For human specie
a = 61.765; %
bm_length = cochleaLength-helicotremaLength;
switch Nr_sections
case {201,401}
% Nr_sections = 500;
f_range = [12000 112];
step_bm = bm_length/500;
otherwise
f_range = [];
step_bm = bm_length/Nr_sections;
end
A0 = 20682; % cochlear_model2018.py, L114
switch Nr_sections
case 201
x = step_bm :2*step_bm:bm_length;
case 401
x = step_bm :step_bm:bm_length;
case 500
x = step_bm/2:step_bm:bm_length;
case 1000
x = step_bm :step_bm:bm_length;
otherwise
error('Check this function: %s',mfilename);
end
f = A0*(10.^(-a*x)) - A*k;
if ~isempty(f_range)
idxi = find(f>= f_range(1),1,'last');
idxf = find(f>= f_range(2),1,'last');
f = f(idxi:idxf);
x = x(idxi:idxf);
end
dispflag = amt_flags;
if nargout == 0
if dispflag.do_verbose
amt_disp(sprintf('The output CF derived from %.0f cochlear sections with frequencies between %.1f and %.1f [Hz]',length(f),f(1),f(end)));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function col_rgb = local_rgb(colour)
switch colour
case 'Gray'
col_rgb = [0.5 0.5 0.5];
case 'LightGray'
col_rgb = [0.8242 0.8242 0.8242];
case 'Green'
col_rgb = [0 0.5 0];
case 'LightSkyBlue'
col_rgb = [0.5273 0.8047 0.9792];
case 'Maroon'
col_rgb = [0.5 0 0];
case 'mediumorchid'
col_rgb = [0.7266 0.3320 0.8242];
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [env,idx_env] = local_Get_envelope2(insig,me)
method = 2;
switch method
case 1
error('Not used, tested on 7/4/2021')
case 2
env = [];
idx_env = [];
if nargin < 4
me = mean(insig); % prctile(insig,75);
end
L1 = [1:length(insig); insig'];
L2 = [1 length(insig); me me];
P = local_InterX(L1,L2);
N_P = size(P,2);
for j = 1:2:N_P-2
idx1 = round(P(1,j));
idx2 = round(P(1,j+1));
[env(end+1),idx_here] = max(insig(idx1:idx2));
idx_env(end+1) = idx_here+idx1-1;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [BW_ERB, Q_ERB, Q03, Q10, extra]= local_Get_ERB_estimation_multi(impulse_ch, CF, fs, N_section)
dispflag = amt_flags;
N_samples = length(impulse_ch);
insig = reshape(impulse_ch,N_samples/N_section, N_section);
N = size(insig,1);
K = floor(N/2); % K-non redundant points
freq_here = (1:K)*(fs/2)/K;
extra.freq_here = freq_here;
Amp_avg = zeros(K,1);
bUse_MA = 1;
for i = 1:N_section
impulse_ch = insig(:,i);
Amp = (2*abs(fft(impulse_ch,N))/N).^2;
Amp = Amp(:);
Amp = Amp(1:K);
Amp_raw(:,i) = Amp(1:K);
Amp_avg = Amp_avg + Amp(1:K);
end
Amp_avg = Amp_avg / N_section;
Amp_raw = 10*log10(Amp_raw);
Amp_dB = 10*log10(Amp_avg);
max_dB = max(Amp_dB(2:end)); % starts at second sample to exclude the DC
Amp_dB = Amp_dB-max_dB;
extra.Amp_raw = Amp_raw;
extra.Amp_dB = Amp_dB;
extra.max_dB = max_dB;
if bUse_MA
% N_moving = 15;
Moving_step = 0.3; % ERB_N
CF_for_step = audtofreq(freqtoaud(CF)-Moving_step);
df = freq_here(2)-freq_here(1);
N_moving = round((CF-CF_for_step)/df);
N_moving = 2*N_moving+1;
Amp_MA = local_moving_average(Amp_dB,N_moving);
end
if bUse_MA
max_MA = max(Amp_MA(2:end));
Amp_dB = Amp_MA-max_MA;
end
extra.Amp_dB_MA = Amp_dB;
extra.max_MA = max_MA;
%%%
Amp_avg = 10.^((Amp_dB+max_dB)/10); % reverting the maximum
[M,Mn] = max(Amp_avg);
E = sum(Amp_avg);
BW_ERB = (E./M)*fs/N;
Q_ERB = CF./BW_ERB;
%%%
L1 = [freq_here; transpose(Amp_dB)];
L2 = [min(freq_here) max(freq_here); [-10 -10]];
P = local_InterX(L1, L2);
if ~isempty(P) && size(P,1)==2
if size(P,2) > 2
P = local_check_values(P);
if size(P,2) > 2
disp('')
end
end
flow_10 = P(1,1);
fhigh_10 = P(1,2);
BW10 = P(1,2)-P(1,1);
Q10 = CF/BW10;
else
flow_10 = nan;
fhigh_10 = nan;
BW10 = nan;
Q10 = nan;
end
L2 = [min(freq_here) max(freq_here); [-3 -3]];
P = local_InterX(L1, L2);
if ~isempty(P) && size(P,1)==2
if size(P,2) > 2
P = local_check_values(P);
if size(P,2) > 2
if dispflag.do_verbose
amt_disp(sprintf('\tStill more than two cut-offs, keeping the ones with the largest BW (manual check: this can be the case for verhulst2018)...'));
end
[xx,idx] = max(diff(P(1,:)));
P = P(:,idx:idx+1);
end
end
try
flow_03 = P(1,1);
fhigh_03 = P(1,2);
catch
disp('')
end
BW03 = P(1,2)-P(1,1);
Q03 = CF/BW03;
if Q03 > 12
disp('')
end
else
flow_03 = nan;
fhigh_03 = nan;
BW03 = nan;
Q03 = nan;
end
extra.BW_ERB = BW_ERB;
extra.BW10 = BW10;
extra.BW03 = BW03;
extra.Q_ERB = Q_ERB;
extra.Q10 = Q10;
extra.Q03 = Q03;
extra.flow_03 = flow_03;
extra.flow_10 = flow_10;
extra.fhigh_03 = fhigh_03;
extra.fhigh_10 = fhigh_10;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function P = local_check_values(P)
idxs2remove = find(P(1,:) < 20);
P(:,idxs2remove) = [];
tolerance_val = 40;
idxs2remove = find(diff(P(1,:)) < tolerance_val);
P(:,idxs2remove+1) = [];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function Amp_Ma = local_moving_average(Amp_dB,N_Ma)
N_i = (N_Ma-1)/2 + 1;
step_downup = (N_Ma-1)/2;
Amp_Ma = Amp_dB;
for i = N_i:length(Amp_dB)-N_i
try
Amp_Ma(i) = mean(Amp_dB(i-step_downup:i+step_downup));
catch
disp('')
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function P = local_InterX(L1,L2)
%LOCAL_INTERX Intersection of curves
% P = LOCAL_INTERX(L1,L2) returns the intersection points of two curves L1
% and L2. The curves L1,L2 can be either closed or open and are described
% by two-row-matrices, where each row contains its x- and y- coordinates.
%
% P has the same structure as L1 and L2, and its rows correspond to the
% x- and y- coordinates of the intersection points of L1 and L2. If no
% intersections are found, the returned P is empty.
%
% Two words about the algorithm: Most of the code is self-explanatory.
% The only trick lies in the calculation of C1 and C2. To be brief, this
% is essentially the two-dimensional analog of the condition that needs
% to be satisfied by a function F(x) that has a zero in the interval
% [a,b], namely
% F(a)*F(b) <= 0
% C1 and C2 exactly do this for each segment of curves 1 and 2
% respectively. If this condition is satisfied simultaneously for two
% segments then we know that they will cross at some point.
% Each factor of the 'C' arrays is essentially a matrix containing
% the numerators of the signed distances between points of one curve
% and line segments of the other.
%
% This function is based on InterX.m (v3.0, 21 Sept. 2010) by author 'NS'
% that is available within the Mathworks FileExchange.
% Reordering the data before intersecting L1 and L2:
x1 = L1(1,:)'; x2 = L2(1,:);
y1 = L1(2,:)'; y2 = L2(2,:);
dx1 = diff(x1); dy1 = diff(y1);
dx2 = diff(x2); dy2 = diff(y2);
%...Determine 'signed distances'
S1 = dx1.*y1(1:end-1) - dy1.*x1(1:end-1);
S2 = dx2.*y2(1:end-1) - dy2.*x2(1:end-1);
C1 = le(local_D(bsxfun(@times,dx1,y2)-bsxfun(@times,dy1,x2),S1),0);
C2 = le(local_D((bsxfun(@times,y1,dx2)-bsxfun(@times,x1,dy2))',S2'),0)';
%...Obtain the segments where an intersection is expected
[i,j] = find(C1 & C2);
if isempty(i),P = zeros(2,0);return; end;
%...Transpose and prepare for output
i=i'; dx2=dx2'; dy2=dy2'; S2 = S2';
L = dy2(j).*dx1(i) - dy1(i).*dx2(j);
i = i(L~=0); j=j(L~=0); L=L(L~=0); %...Avoid divisions by 0
%...Solve system of eqs to get the common points
P = unique([dx2(j).*S1(i) - dx1(i).*S2(j), ...
dy2(j).*S1(i) - dy1(i).*S2(j)]./[L L],'rows')';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function u = local_D(x,y)
u = bsxfun(@minus,x(:,1:end-1),y).*bsxfun(@minus,x(:,2:end),y);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [waveform,freq_array,phases] = local_Profile_Analysis(dur, rampdur, stim_dB, fs, freq_array, phases)
% function [waveform,freq_array,phases] = local_Profile_Analysis(dur, rampdur, stim_dB, fs, freq_array, phases)
%
% This function produces a vector of log-spaced components based on Lentz (JASA 2005)
%Variables:
% dur = duration in sec (e.g. 0.5 sec)
% ramp_time: onset/offset in seconds
% stim_dB = overall stimulus level in dB SPL (both inetervals are scaled to same dB SPL)
% fs = sampling rate (Hz)
%
% Code adapted from UR EAR function
L = round(dur * fs); % stimulus length (in samples)
t = (0:L-1)/fs; % time vector
waveform = 0; % initialize variable
if nargin < 6
phases = 2*pi * rand(1,length(freq_array));
end
for i = 1:length(freq_array) % step through each frequency component in the complex
f = freq_array(i);
phase = phases(i); % Randomly vary the starting phase of each component
waveform = waveform + cos(2 * pi * f * t + phase);
% end
end
waveform = tukeywin(L, 2*rampdur/dur)' .* waveform; % gate
waveform = 20e-6 * 10.^(stim_dB/20) * waveform/rms(waveform);% convert signal into Pascals
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