THE AUDITORY MODELING TOOLBOX
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EXP_TAKANEN2013 - Figures from Takanen, Santala, Pulkki (2013a,2013b)
Program code:
function output = exp_takanen2013(varargin)
%EXP_TAKANEN2013 Figures from Takanen, Santala, Pulkki (2013a,2013b)
% Usage: output = exp_takanen2013(flag)
%
% EXP_TAKANEN2013(flag) reproduces the figure given by flag either from
% the Takanen et al. (2013) book chapter or the Takanen et al. (2014)
% manuscript. The format of its output depends on the chosen figure.
% Optionally, pre-computed cochlear model outputs for the different
% scenarios can be applied to significantly reduce the required
% computation time. The pre-computed cochlear model outputs can be
% obtained from the authors.
%
% The following flags can be specified:
%
% 'binsig' This option computes the figures from the binaural
% signals.
%
% 'cochlea' This option uses pre-computed cochlea-model outputs
% and thus reduces the computation time. (Default).
%
% 'fig8' Figure 8 from the book chapter Takanen et al. (2013). Binaural activity
% maps obtained with the model for an off-sweet-spot
% listening scenario with different audio coding
% techniques.
%
% 'fig9' Figure 9 from the book chapter Takanen et al. (2013). Activation
% distributions obtained with the model for (a) the
% reference scenario of incoherent pink noise emitted
% from twelve azimuth directions, and (b)-(d) the
% reproduction of such a scenario with an eight-channel
% loudspeaker system employing signals obtained with
% different audio coding techniques. Additionally, the
% the distributions when DirAC is used in audio coding
% of 5.0 surround signal having incoherent pink noise
% in each channel with (e) the straightforward method
% and (f) the even-layout method.
%
% 'fig7_takanen2014' Figure 7 from the article Takanen et al. (2014). Binaural activity maps
% for four binaural listening scenarios, namely (a)
% HRTF-processed pink noise, (b) pink noise with ITD,
% (c) anti-phasic sinusoidal sweep, and (d) band-
% limited noise centered around 500 Hz with an ITD of
% 1.5 ms.
%
% 'fig8_takanen2014' Figure 8 from the article Takanen et al. (2014). Binaural activity maps
% for four binaural listening scenarios, namely (a)
% S_pi N_0 with different signal-to-noise ratios,
% (b) binaural interference, (c) precedence effect, and
% (d) binaural room impulse response.
%
% If no flag is given, the function will print the list of valid flags.
%
% Requirements and installation:
% ------------------------------
%
% 1) Functioning model verhulst2012 (see the corresponding requirements)
%
% 2) Data from www.acoustics.hut.fi/publications/papers/AMTool2013-bam/ in amtbase/signals
%
% 3) at least 3 GB of RAM
%
% Examples:
% ---------
%
% To display Figure 8 from the book chapter Takanen et al. (2013) using pre-computed cochlea
% model outputs use:
%
% exp_takanen2013('fig8','cochlea');
%
% To display Figure 9 from the book chapter Takanen et al. (2013) using pre-computed cochlea
% model outputs use:
%
% exp_takanen2013('fig9','cochlea');
%
% To display Figure 7 from the article Takanen et al. (2014) using pre-computed cochlea
% model outputs use:
%
% exp_takanen2013('fig7_takanen2014','cochlea');
%
% To display Figure 8 the article Takanen et al. (2014) using pre-computed cochlea
% model outputs use:
%
% exp_takanen2013('fig8_takanen2014','cochlea');
%
% References:
% M. Takanen, O. Santala, and V. Pulkki. Visualization of functional
% count-comparison-based binaural auditory model output. Hearing
% research, 309:147--163, 2014. PMID: 24513586.
%
% M. Takanen, O. Santala, and V. Pulkki. Perceptually encoded signals and
% their assessment. In J. Blauert, editor, The technology of binaural
% listening. Springer, 2013.
%
%
% Url: http://amtoolbox.sourceforge.net/amt-0.10.0/doc/experiments/exp_takanen2013.php
% Copyright (C) 2009-2020 Piotr Majdak and the AMT team.
% This file is part of Auditory Modeling Toolbox (AMT) version 1.0.0
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
% AUTHOR: Marko Takanen, Olli Santala, Ville Pulkki
%
% COPYRIGHT (C) 2013 Aalto University
% School of Electrical Engineering
% Department of Signal Processing and Acoustics
% Espoo, Finland
definput.import={'amt_cache'};
definput.flags.type={'missingflag','fig8','fig9','fig7_takanen2014','fig8_takanen2014'};
definput.flags.dataType={'cochlea','binsig'};
[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;
%% Setting of parameters
fs = 48000;
printFigs = 0;
printMap =0;
compType =1;
h = figure;
%% Figure 8 from the book chapter
if flags.do_fig8
% if the user wishes to compute the cochlear model outputs, binaural
% input signals are used
if flags.do_binsig
data=amt_load('takanen2013','fig8_binsig.mat','testsize');
for ind=1:data.testsize
[output,outputMtrx,tit]=amt_cache('get', ['fig8_binsig_' num2str(ind)], flags.cachemode);
if isempty(output)
data=amt_load('takanen2013','fig8_binsig.mat',['test' num2str(ind)]);
tit=data.(['test' num2str(ind)]).case;
insig=data.(['test' num2str(ind)]).insig;
clear data
% compute the binaural activity map with the model
output = takanen2013(insig,fs,compType,printFigs,printMap);
nXBins= length(output.levels)*(size(output.colorMtrx,1)-1);
dim = size(output.activityMap);
output.colorGains(output.colorGains>1) =1;
outputMtrx = zeros(dim(1),nXBins,3,'single');
for colorInd=1:size(output.colorMtrx,1)
temp = uint32(find((output.activityMap==(colorInd-1))==1));
outputMtrx(temp) = output.colorGains(temp)*output.colorMtrx(colorInd,1);
outputMtrx(temp+dim(1)*nXBins) = output.colorGains(temp)*output.colorMtrx(colorInd,2);
outputMtrx(temp+2*dim(1)*nXBins) = output.colorGains(temp)*output.colorMtrx(colorInd,3);
end
amt_cache('set', ['fig8_binsig_' num2str(ind)], output, outputMtrx, tit);
end
g(ind)= subplot(3,2,ind);
imagesc(output.levels./90,((dim(1)-1):-20:0)/fs,outputMtrx(1:20:end,:,:));
clear output outputMtrx
title(tit);
set(gca,'YTick',.0:.5:2.5);
set(gca,'YTickLabel',2.5:-0.5:0);
set(gca,'Xtick',-1:0.4:1);
xlabel('Activation location');
ylabel('Time [s]');
end
end
%otherwise pre-computed cochlea model outputs are used
if flags.do_cochlea
testsize=amt_load('takanen2013','fig8_cochlea.mat','testsize');
for ind=1:testsize.testsize
[output,outputMtrx,tit]=amt_cache('get', ['fig8_cochlea_' num2str(ind)], flags.cachemode);
if isempty(output)
data=amt_load('takanen2013','fig8_cochlea.mat',['test' num2str(ind)]);
tit=data.(['test' num2str(ind)]).case;
insig=data.(['test' num2str(ind)]).cochlear;
clear data
% compute the binaural activity map with the model
output = takanen2013(insig,fs,compType,printFigs,printMap);
nXBins= length(output.levels)*(size(output.colorMtrx,1)-1);
dim = size(output.activityMap);
output.colorGains(output.colorGains>1) =1;
outputMtrx = zeros(dim(1),nXBins,3,'single');
for colorInd=1:size(output.colorMtrx,1)
temp = uint32(find((output.activityMap==(colorInd-1))==1));
outputMtrx(temp) = output.colorGains(temp)*output.colorMtrx(colorInd,1);
outputMtrx(temp+dim(1)*nXBins) = output.colorGains(temp)*output.colorMtrx(colorInd,2);
outputMtrx(temp+2*dim(1)*nXBins) = output.colorGains(temp)*output.colorMtrx(colorInd,3);
end
amt_cache('set', ['fig8_cochlea_' num2str(ind)], output, outputMtrx, tit);
end
g(ind)=subplot(3,2,ind);
imagesc(output.levels./90,((size(output.activityMap,1)-1):-20:0)/fs,outputMtrx(1:20:end,:,:));
clear output outputMtrx
title(tit);
set(gca,'YTick',.0:.5:2.5);
set(gca,'YTickLabel',2.5:-0.5:0);
set(gca,'Xtick',-1:0.4:1);
ylabel('Time [s]');
xlabel('Activation location');
end
end
end
%% Figure 9 from the book chapter
if flags.do_fig9
probDist = zeros(6,19);
% if the user wishes to compute the cochlear model outputs, binaural
% input signals are used
if flags.do_binsig
data=amt_load('takanen2013','fig9_binsig.mat', 'testsize');
for ind=1:data.testsize
[output,outputMtrx,tit]=amt_cache('get', ['fig9_binsig_' num2str(ind)], flags.cachemode);
if isempty(output)
data=amt_load('takanen2013','fig9_binsig.mat',['test' num2str(ind)]);
tit=data.(['test' num2str(ind)]).case;
insig=data.(['test' num2str(ind)]).insig;
clear data
% compute the binaural activity map with the model
output = takanen2013(insig,fs,compType,printFigs,printMap);
for i=1:6
probDist(i,:) = sum(output.colorGains(:,i:6:end));
end
temp = probDist./(max(probDist,[],2)*ones(1,size(probDist,2)));
outputMtrx = zeros(size(temp,1),size(temp,2),3,'single');
for colorInd=2:size(output.colorMtrx,1)
outputMtrx(colorInd-1,:,1) = temp(colorInd-1,:)*output.colorMtrx(colorInd,1);
outputMtrx(colorInd-1,:,2) = temp(colorInd-1,:)*output.colorMtrx(colorInd,2);
outputMtrx(colorInd-1,:,3) = temp(colorInd-1,:)*output.colorMtrx(colorInd,3);
end
amt_cache('set', ['fig9_binsig_' num2str(ind)], output, outputMtrx, tit);
end
g(ind)= subplot(3,2,ind);
imagesc(output.levels./90,6:-1:1,outputMtrx);
clear output outputMtrx
title(tit);
set(gca,'YTick',1:6);
set(gca,'YTickLabel',6:-1:1);
set(gca,'Xtick',-1:0.4:1);
ylabel('Frequency area');
xlabel('Distribution of activation');
end
end
%otherwise pre-computed cochlea model outputs are used
if flags.do_cochlea
data=amt_load('takanen2013','fig9_cochlea.mat','testsize');
for ind=1:data.testsize
[output,outputMtrx,tit]=amt_cache('get', ['fig9_cochlea_' num2str(ind)], flags.cachemode);
if isempty(output)
data=amt_load('takanen2013','fig9_cochlea.mat',['test' num2str(ind)]);
tit=data.(['test' num2str(ind)]).case;
insig=data.(['test' num2str(ind)]).cochlear;
clear data
% compute the binaural activity map with the model
output = takanen2013(insig,fs,compType,printFigs,printMap);
for i=1:6
probDist(i,:) = sum(output.colorGains(:,i:6:end));
end
temp = probDist./(max(probDist,[],2)*ones(1,size(probDist,2)));
outputMtrx = zeros(size(temp,1),size(temp,2),3,'single');
for colorInd=2:size(output.colorMtrx,1)
outputMtrx(colorInd-1,:,1) = temp(colorInd-1,:)*output.colorMtrx(colorInd,1);
outputMtrx(colorInd-1,:,2) = temp(colorInd-1,:)*output.colorMtrx(colorInd,2);
outputMtrx(colorInd-1,:,3) = temp(colorInd-1,:)*output.colorMtrx(colorInd,3);
end
amt_cache('set', ['fig9_cochlea_' num2str(ind)], output, outputMtrx, tit);
end
g(ind)= subplot(3,2,ind);
imagesc(output.levels./90,6:-1:1,outputMtrx);
clear output outputMtrx
title(tit);
set(gca,'YTick',1:6);
set(gca,'YTickLabel',6:-1:1);
set(gca,'Xtick',-1:0.4:1);
ylabel('Frequency area');
xlabel('Distribution of activation');
end
end
end
%% Figure 7 from takanen2014
if flags.do_fig7_takanen2014
% compute the cochlear model outputs, load the binaural input signals
if flags.do_binsig, s='fig7_takanen2014_binsig'; end
% otherwise pre-computed cochlea model outputs are used
if flags.do_cochlea, s='fig7_takanen2014_cochlea'; end
data=amt_load('takanen2013',[s '.mat']);
data_tests=data.testsize;
siglen=zeros(data_tests,1);
data_tests_Data=zeros(data_tests,1);
for ind=1:data_tests
if flags.do_cochlea
data_tests_Data(ind)=length(data.(['test' num2str(ind)]).cochlearData);
for caseInd=1:data_tests_Data(ind)
siglen(ind)=siglen(ind)+length(data.(['test' num2str(ind)]).cochlearData(caseInd).cochlear.velocityLeft);
end
end
if flags.do_binsig
data_tests_Data(ind)=length(data.(['test' num2str(ind)]).binSignals);
for caseInd=1:data_tests_Data(ind)
siglen(ind)=siglen(ind)+length(data.(['test' num2str(ind)]).binSignals(caseInd).insig);
end
end
end
clear data % release unused memory
for ind=1:data_tests
idx=1;
%some scenarios consist of multiple test cases that are
%processed separately
[levels,activityMap,outputMtrx,scenario,ytickPos,ytickLab,ylab]=amt_cache('get', [s '_' num2str(ind)], flags.cachemode);
if isempty(levels)
activityMap=zeros(siglen(ind),114);
gains=zeros(siglen(ind),114);
for caseInd=1:data_tests_Data(ind)
data=amt_load('takanen2013',[s '.mat'],['test' num2str(ind)]);
if flags.do_cochlea
insig=data.(['test' num2str(ind)]).cochlearData(caseInd).cochlear;
len=size(insig.velocityLeft,1);
end
if flags.do_binsig
insig=data.(['test' num2str(ind)]).binSignals(caseInd).insig;
len=size(insig,1);
end
ylab=data.(['test' num2str(ind)]).ylab;
scenario=data.(['test' num2str(ind)]).scenario;
ytickPos=data.(['test' num2str(ind)]).ytickPos;
ytickLab=data.(['test' num2str(ind)]).ytickLab(end:-1:1);
clear data % release unused memory
% compute the binaural activity map with the model
output = takanen2013(insig,fs,compType,printFigs,printMap);
%concatenate the separate activity maps into one map
activityMap(idx:idx+len-1,:)=output.activityMap;
gains(idx:idx+len-1,:)=output.colorGains;
idx=idx+len;
colorMtrx=output.colorMtrx;
levels=output.levels;
clear output insig % release unused memory
end
%the anti-phasic sweep contains also frequencies below the
%frequency range of the model. Hence, the first 0.5 s of the
%activity map are removed
if(strcmp('Anti-phasic sinusoidal sweep',scenario)==1)
activityMap = activityMap(0.5*fs+1:end,:);
gains = gains(0.5*fs+1:end,:);
end
nXBins= length(levels)*(size(colorMtrx,1)-1);
dim = size(activityMap);
gains(gains>1) =1;
outputMtrx = zeros(dim(1),nXBins,3,'single');
for colorInd=1:size(colorMtrx,1)
temp = uint32(find((activityMap==(colorInd-1))==1));
outputMtrx(temp) = gains(temp)*colorMtrx(colorInd,1);
outputMtrx(temp+dim(1)*nXBins) = gains(temp)*colorMtrx(colorInd,2);
outputMtrx(temp+2*dim(1)*nXBins) = gains(temp)*colorMtrx(colorInd,3);
end
amt_cache('set', [s '_' num2str(ind)], levels, activityMap, outputMtrx, scenario, ytickPos, ytickLab, ylab);
end
g(ind)= subplot(2,2,ind);
imagesc(levels./90,((size(activityMap,1)-1):-20:0)/fs,outputMtrx(1:20:end,:,:));
clear outputMtrx gains activityMap
title(scenario);
set(gca,'YTick',ytickPos);
set(gca,'YTickLabel',ytickLab);
set(gca,'Xtick',-1:0.4:1);
ylabel(ylab);
xlabel('Activation location');
end
end
%% Figure 8 from takanen2014
if flags.do_fig8_takanen2014
% compute the cochlear model outputs, load the binaural input signals
if flags.do_binsig, s='fig8_takanen2014_binsig'; end
% otherwise pre-computed cochlea model outputs are used
if flags.do_cochlea, s='fig8_takanen2014_cochlea'; end
data=amt_load('takanen2013',[s '.mat']);
data_tests=data.testsize;
siglen=zeros(data_tests,1);
data_tests_Data=zeros(data_tests,1);
for ind=1:data_tests
if flags.do_cochlea
data_tests_Data(ind)=length(data.(['test' num2str(ind)]).cochlearData);
for caseInd=1:data_tests_Data(ind)
siglen(ind)=siglen(ind)+length(data.(['test' num2str(ind)]).cochlearData(caseInd).cochlear.velocityLeft);
end
end
if flags.do_binsig
data_tests_Data(ind)=length(data.(['test' num2str(ind)]).binSignals);
for caseInd=1:data_tests_Data(ind)
siglen(ind)=siglen(ind)+length(data.(['test' num2str(ind)]).binSignals(caseInd).insig);
end
end
end
clear data % release unused memory
for ind=1:data_tests
idx=1;
%some scenarios consist of multiple test cases that are
%processed separately
[levels,activityMap,outputMtrx,scenario,ytickPos,ytickLab,ylab]=amt_cache('get', [s '_' num2str(ind)], flags.cachemode);
if isempty(levels)
activityMap=zeros(siglen(ind),114);
gains=zeros(siglen(ind),114);
for caseInd=1:data_tests_Data(ind)
data=amt_load('takanen2013', [s '.mat'], ['test' num2str(ind)]);
if flags.do_cochlea
insig=data.(['test' num2str(ind)]).cochlearData(caseInd).cochlear;
len=size(insig.velocityLeft,1);
end
if flags.do_binsig
insig=data.(['test' num2str(ind)]).binSignals(caseInd).insig;
len=size(insig,1);
end
ylab=data.(['test' num2str(ind)]).ylab;
scenario=data.(['test' num2str(ind)]).scenario;
ytickPos=data.(['test' num2str(ind)]).ytickPos;
ytickLab=data.(['test' num2str(ind)]).ytickLab(end:-1:1);
clear data % release unused memory
% compute the binaural activity map with the model
output = takanen2013(insig,fs,compType,printFigs,printMap);
%concatenate the separate activity maps into one map
activityMap(idx:idx+len-1,:)=output.activityMap;
gains(idx:idx+len-1,:)=output.colorGains;
idx=idx+len;
colorMtrx=output.colorMtrx;
levels=output.levels;
clear output % release unused memory
end
%in order to better visualize the clicks in the precedence
%effect scenario, most of the silent parts of the signal
%are removed
if(strcmp('Precedence effect',scenario)==1)
activityMap = activityMap([1500:3700 4500:6700 7500:9700 10500:12700 13500:15700 16500:18700 20200:22400],:);
gains = gains([1500:3700 4500:6700 7500:9700 10500:12700 13500:15700 16500:18700 20200:22400],:);
gains = 2*gains;
end
nXBins= length(levels)*(size(colorMtrx,1)-1);
dim = size(activityMap);
gains(gains>1) =1;
outputMtrx = zeros(dim(1),nXBins,3,'single');
for colorInd=1:size(colorMtrx,1)
temp = uint32(find((activityMap==(colorInd-1))==1));
outputMtrx(temp) = gains(temp)*colorMtrx(colorInd,1);
outputMtrx(temp+dim(1)*nXBins) = gains(temp)*colorMtrx(colorInd,2);
outputMtrx(temp+2*dim(1)*nXBins) = gains(temp)*colorMtrx(colorInd,3);
end
amt_cache('set', [s '_' num2str(ind)], levels,activityMap,outputMtrx,scenario,ytickPos,ytickLab,ylab);
end
g(ind)= subplot(2,2,ind);
imagesc(levels./90,((size(activityMap,1)-1):-20:0)/fs,outputMtrx(1:20:end,:,:));
clear outputMtrx gains activityMap
title(scenario);
set(gca,'YTick',ytickPos);
set(gca,'YTickLabel',ytickLab);
set(gca,'Xtick',-1:0.4:1);
ylabel(ylab);
xlabel('Activation location');
end
end
output = g;
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