MAP

% function [P dt lags SACF]= testACF

% testACF is a *script* to demonstrate the smoothed ACF of

% Balaguer-Ballestera, E. Denham, S.L. and Meddis, R. (2008).

%

% Convert this to a *function* by uncommenting the first line

%  The function returns the P matrix plotted in Figure 96.

%  If a function is used, the following outputs are returned:

%   P:  smoothed SACF (lags x time matrix)

%   dt: time interval between successive columns of P

%   lags: lags used in computing P

%   SACF: unsmoothed SACFs

%

% A range of options are supplied in the early part of the program

%

% #1

% Identify the model parameter file (in 'MAPparamsName')

%

% #2

% Identify the kind of model required (in 'AN_spikesOrProbability')

%  'probability' is recommended for ACF work

%

% #3

% Choose between a harmonic complex or file input

%  by commenting out unwanted code

%

% #4

% Set the signal rms level (in leveldBSPL)

%

% #5

% Identify the model channel BFs in the vector 'BFlist'.

%

% #6

% Last minute changes to the model parameters can be made using

%  the cell array of strings 'paramChanges'.

%  This is used here to control the details of the ACF computations

%  Read the notes in this section for more information

%

% displays:

% Figure 97 shows the AN response to the stimulus. this is a channel x time

%  display. The z-axis (and colour) is the AN fiber firing rate

%

% Figure 96 shows the P-matrix, the smoothed SACF.

%

% Figure 89 shows a summary of the evolution of the unsmoothed SACF

%  over time. If you wish to take a snapshot of the P-matrix at a

%  particular time, this figure can help identify when to take it.

%  The index on the y-axis, identifies the required row numbers

%    of the P or SACF matrix, e.g. P(:,2000)

%

%  On request, (filteredSACFParams.plotACFs=1) Figure 89 shows the channel

%   by channel ACFs at intervals during the computation as a movie.

%  The number of ACF displays is controlled by 'plotACFsInterval'

%   and the movie can be slowed or speeded up using 'plotMoviePauses'

%   (see paramChanges section below).

% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

% This global will find results from MAP1_14

global savedInputSignal ANprobRateOutput ANoutput dt dtSpikes savedBFlist

 % This global,from model parameter file

global filteredSACFParams

dbstop if error

restorePath=path;

addpath (['..' filesep 'MAP'],    ['..' filesep 'wavFileStore'], ...

    ['..' filesep 'utilities'])

% User sets up requirements

%%  #1 parameter file name

MAPparamsName='Normal';                 % recommended

%% #2 probability (fast) or spikes (slow) representation: select one

% AN_spikesOrProbability='spikes';

%   or

AN_spikesOrProbability='probability';   % recommended

%% #3 A. harmonic sequence or B. speech file input

% Comment out unwanted code

% A. harmonic tone (Hz) - useful to demonstrate a broadband sound

sampleRate= 44100;              % recommended 44100

signalType= 'tones';

duration=0.100;                 % seconds

beginSilence=0.020;

endSilence=0.020;

rampDuration=.005;              % raised cosine ramp (seconds)

% toneFrequency is a vector of component frequencies

F0=120;

toneFrequency= [3*F0 4*F0 5*F0];

%   or

% B. file input

signalType= 'file';

fileName='Oh No';

fileName='1z67931a_44kHz';

%% #4 rms level

leveldBSPL= 60;                  % dB SPL (80 for Lieberman)

%% #5 number of channels in the model

%   21-channel model (log spacing of BFs)

numChannels=21;

lowestBF=150; 	highestBF= 6000;

BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels));

%% #6 change model parameters

% Parameter changes can be used to change one or more model parameters

%  *after* the MAPparams file has been read (see manual)

% Take control of ACF parameters

%  The filteredACF parameters are set in the MAPparamsNormal file

%  However, it is convenient to change them here leving the file intacta

    minPitch=	80; maxPitch=	500; numPitches=50;

    minPitch=	200; maxPitch=	4000; numPitches=40;

    maxLag=1/minPitch; minLag=1/maxPitch;

    lags= linspace(minLag, maxLag, numPitches);

paramChanges={...

    'filteredSACFParams.lags=lags;     % autocorrelation lags vector;',...

    'filteredSACFParams.acfTau=	2;     % (Wiegrebe) time constant ACF;',...

    'filteredSACFParams.lambda=	0.12;  % slower filter to smooth ACF;',...

    'filteredSACFParams.plotACFs=1;    % plot ACFs while computing;',...

    'filteredSACFParams.plotACFsInterval=0.01;',...

    'filteredSACFParams.plotMoviePauses=.1;  ',...

    'filteredSACFParams.usePressnitzer=0; % attenuates ACF at  long lags;',...

    'filteredSACFParams.lagsProcedure=  ''useAllLags'';',...

    };

% Notes:

% acfTau:   time constant of unsmoothed ACF

% lambda:   time constant of smoothed ACFS

% plotACFs: plot ACFs during computation (0 to switch off, for speed)

% plotACFsInterval: sampling interval for plots

% plotMoviePauses:  pause duration between frames to allow viewing

% usePressnitzer:   gives low weights to long lags

% lagsProcedure:    used to fiddle with output (ignore)

%% delare 'showMap' options to control graphical output

% see UTIL_showMAP for more options

showMapOptions=[];

% showMapOptions.showModelOutput=0;     % plot of all stages

showMapOptions.surfAN=1;                % surface plot of HSR response

showMapOptions.PSTHbinwidth=0.001;      % smoothing for PSTH

if exist('fileName','var')

    % needed for labeling plot

    showMapOptions.fileName=fileName;

end

%% Generate stimuli

switch signalType

    case 'tones'

        % Create tone stimulus

        dt=1/sampleRate; % seconds

        time=dt: dt: duration;

        inputSignal=sum(sin(2*pi*toneFrequency'*time), 1);

        amp=10^(leveldBSPL/20)*28e-6;   % converts to Pascals (peak)

        inputSignal=amp*inputSignal;

        % apply ramps

        % catch rampTime error

        if rampDuration>0.5*duration, rampDuration=duration/2; end

        rampTime=dt:dt:rampDuration;

        ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ...

            ones(1,length(time)-length(rampTime))];

        inputSignal=inputSignal.*ramp;

        ramp=fliplr(ramp);

        inputSignal=inputSignal.*ramp;

        % add silence

        intialSilence= zeros(1,round(beginSilence/dt));

        finalSilence= zeros(1,round(endSilence/dt));

        inputSignal= [intialSilence inputSignal finalSilence];

    case 'file'

        %% file input simple or mixed

        [inputSignal sampleRate]=wavread(fileName);

        dt=1/sampleRate;

        inputSignal=inputSignal(:,1);

        targetRMS=20e-6*10^(leveldBSPL/20);

        rms=(mean(inputSignal.^2))^0.5;

        amp=targetRMS/rms;

        inputSignal=inputSignal*amp;

end

wavplay(inputSignal, sampleRate)

%% run the model

fprintf('\n')

disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)])

disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability])

disp('Computing ...')

MAP1_14(inputSignal, sampleRate, BFlist, ...

    MAPparamsName, AN_spikesOrProbability, paramChanges);

%% The model run is now complete. Now display the results

% display the AN response

UTIL_showMAP(showMapOptions)

% compute ACF

switch AN_spikesOrProbability

    case 'probability'

        % use only HSR fibers

        inputToACF=ANprobRateOutput(end-length(savedBFlist)+1:end,:);

    otherwise

        inputToACF=ANoutput;

        dt=dtSpikes;

end

disp ('computing ACF...')

% read paramChanges to get new filteredSACFParams

for i=1:length(paramChanges)

eval(paramChanges{i});

end

[P BFlist SACF]= filteredSACF(inputToACF, dt, savedBFlist, filteredSACFParams);

disp(' ACF done.')

%% plot original waveform on summary/smoothed ACF plot

figure(96), clf

subplot(3,1,3)

t=dt*(1:length(savedInputSignal));

plot(t,savedInputSignal, 'k')

xlim([0 t(end)])

title(['stimulus: ' num2str(leveldBSPL, '%4.0f') ' dB SPL']);

% plot SACF

figure(96)

subplot(2,1,1)

imagesc(P.^2)

colormap bone

ylabel('periodicities (Hz)'), xlabel('time (s)')

title('smoothed SACF. (periodicity x time)')

% y-axis specifies pitches (1/lags)

% Force MATLAB to show the lowest pitch

postedYvalues=[1 get(gca,'ytick')]; set(gca,'ytick',postedYvalues)

pitches=1./filteredSACFParams.lags;

set(gca,'ytickLabel', round(pitches(postedYvalues)))

% x-axis is time at which P is samples

[nCH nTimes]=size(P);

t=dt:dt:dt*nTimes;

tt=get(gca,'xtick');

set(gca,'xtickLabel', round(100*t(tt))/100)

%% On a new figure show a cascade of SACFs

figure(89), clf

% select 100 samples;

[r c]=size(SACF);

step=round(c/100);

idx=step:step:c;

UTIL_cascadePlot(SACF(:,idx)', 1./pitches)

xlabel('lag (s)'), ylabel('time pointer -->')

title(' SACF summary over time')

yValues=get(gca,'yTick');

set(gca,'yTickLabel', num2str(yValues'*100))

path(restorePath)