diff userProgramsTim/IPIHextract.m @ 38:c2204b18f4a2 tip

End nov big change

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/userProgramsTim/IPIHextract.m	Mon Nov 28 13:34:28 2011 +0000
@@ -0,0 +1,181 @@
+function [iih,IPIhisttime,IPIhistweight]=IPIHextract(IFRAN_pattern, sfreq)
+% 
+% tracks the formants according to an analysis proposed in Secker-Walker
+% JASA 1990, section V.A
+% Tim Jürgens, February 2011, code from Guy Brown included
+% 
+% input:  IFRAN_pattern: pattern of the auditory model (dependend on the number of modules used)
+%                        first dimension: frequency channel, 
+%                        second dimension: time (samples)
+%         sfreq:         sampling frequency
+% output: iih:           interpeak-interval histogram, matrix very similar
+%                        the plot 5 in the Secker-Walker paper
+%
+%
+%
+
+
+time_axis = 0:1/sfreq:(size(IFRAN_pattern,2)-1)/sfreq;
+
+%find how many samples of AN_pattern are 10ms and 3ms
+%one_sample_is_a_time_of = time_axis(2);
+[tmp, start_time_index] = min(abs(0-time_axis));
+[tmp, stop20_time_index] = min(abs(0.020-time_axis));
+number_of_samples20ms = stop20_time_index - start_time_index;
+
+[tmp, stop10_time_index] = min(abs(0.010-time_axis));
+number_of_samples10ms = stop10_time_index - start_time_index;
+every_10ms = 1:number_of_samples10ms:size(IFRAN_pattern,2)-number_of_samples20ms;
+
+hamm_window = hamming(11);
+halfHamming = (length(hamm_window)-1)/2;
+
+% window normalization
+
+norm = conv(ones(1,number_of_samples20ms),hamm_window);
+norm = norm(5+1:end-5)';
+win_size = number_of_samples20ms;
+half_win_size = floor(win_size/2);
+hop_size = number_of_samples10ms;
+
+%parameters of the autocorrelation
+params.acfTau=0.1;
+params.lags=[0:1/sfreq:0.02-1/sfreq];
+sampledacf = runningACF(IFRAN_pattern,sfreq,params);
+sampledacf(sampledacf<0)=0;
+sampledacf = sqrt(sampledacf);
+
+
+%pre-allocation due to speed
+%Acorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1);
+%RAcorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1);
+%SRAcorr = zeros(size(IFRAN_pattern,1),size(every_3ms,2),number_of_samples10ms*2+1-10);
+IPIhisttime = zeros(size(IFRAN_pattern,1),size(every_10ms,2),3);
+IPIhistweight = zeros(size(IFRAN_pattern,1),size(every_10ms,2),3);  %maximum 3 peaks from the SRA
+iih = zeros(half_win_size,size(sampledacf,1));
+
+
+
+
+
+for iCounter = 1:size(sampledacf,2) %each channel
+    fprintf('Channel No. %i\n',iCounter);
+    %time_counter = 1;
+    %for jCounter = every_3ms %every 3ms time segment
+                    
+    for frame=1:size(sampledacf,1)
+        
+        %%debug
+        %if iCounter == 130
+        %    disp('here');
+        %end
+        
+        
+        sra = conv(squeeze(sampledacf(frame,iCounter,:)),hamm_window);
+        sra = sra(halfHamming+1:end-halfHamming)./norm;
+        df = [0 ; diff(sra)];
+        idx = find((df(1:end-1)>=0)&(df(2:end)<0));
+        % interpolate
+        a=df(idx);
+        b=df(idx+1);
+        idx = (idx-1+a./(a-b));
+        % get rid of a zero peak, if it exists
+        idx = idx(idx>1);
+        %include the zeroth peak
+        idx = [1 idx']';
+        % peak values corresponding to these intervals
+        amp = interp1(1:length(sra),sra,idx,'linear');
+        % if required, remove peaks that lie below the mean sra
+        % note that we disregard the value at zero delay
+        %if (params.removePeaksBelowMean)
+        valid = find(amp>1.2*mean(sra(1:floor(length(sra)/2))));
+        %valid = find(amp>mean(sra));
+        %just take the mean of the first half of the sra as a comparison
+        idx = idx(valid);
+        amp = amp(valid);
+        %end
+        % only use the first four peaks (three intervals)
+        idx = idx(1:min(4,length(idx)));
+        % find the intervals
+        interval = diff(idx);
+        % now histogram the intervals
+        if (~isempty(interval))
+            for k=1:length(interval)
+                if interval(k)<=half_win_size
+                    iih(round(interval(k)),frame) = iih(round(interval(k)),frame)+amp(k);
+                    IPIhisttime(iCounter,frame,k) = interval(k)/sfreq;
+                    IPIhistweight(iCounter,frame,k) = amp(k);
+                end
+            end
+        end
+        
+    end
+    
+    
+    
+    
+    %% end Guy's code
+    
+    
+    %         %take the autocorrelation (ACF) of a 10ms-segment of each channel
+    %         Acorr(iCounter,time_counter,:) = xcorr(IFRAN_pattern(iCounter,jCounter:number_of_samples10ms+jCounter),'biased'); %biased scales the ACF by the reciprocal of the length of the segment
+    %         %root calculation
+    %         RAcorr(iCounter,time_counter,:) = sqrt(abs(Acorr(iCounter,time_counter,:)));
+    %
+    %         %smoothing using the 11-point hamming window
+    %         for kCounter = 6:size(RAcorr(iCounter,time_counter,:),3)-5 %start with 6 and end with 5 samples
+    %             %less the length of time_axis not to get in conflict with the length of
+    %             %the hamm_window
+    %             SRAcorr(iCounter,time_counter,kCounter-5) = ...
+    %                 squeeze(RAcorr(iCounter,time_counter,(kCounter-5):(kCounter+5)))'*hamm_window./sum(hamm_window);
+    %         end
+    %
+    %         %mean value of actual SRA
+    %         SRA_mean = mean(SRAcorr(iCounter,time_counter,:));
+    %
+    %         %find signed zero-crossings of the first derivative (=difference)
+    %         z_crossings_indices = find(diff(sign(diff(squeeze(SRAcorr(iCounter,time_counter,:))))) < 0)+1; %+1 is necessary, because diff shortens vector by 1
+    %         middle_index = ceil(size(SRAcorr(iCounter,time_counter,:),3)/2);
+    %
+    %         validCounter = 1;
+    %         valid_z_crossings_indices = [];
+    %         %find valid zero-crossings (peak higher than meanvalue and within first 5 ms of SRA)
+    %         for lCounter = 1:length(z_crossings_indices)
+    %             if (SRAcorr(iCounter,time_counter,z_crossings_indices(lCounter)) > SRA_mean) && ...
+    %                     (abs(z_crossings_indices(lCounter)-middle_index) < round(number_of_samples10ms/2));
+    %                 valid_z_crossings_indices(validCounter) = z_crossings_indices(lCounter);
+    %                 validCounter = validCounter+1;
+    %             end
+    %         end
+    %
+    %         %find main peak in the ACF
+    %         [tmp,index_of_z_crossings_main_index] = min(abs(middle_index-valid_z_crossings_indices));
+    %         if ~tmp == 0
+    %             disp('middle peak not appropriately found');
+    %         end
+    %
+    %         %%% for debugging
+    % %         if iCounter == 130
+    % %             disp('here');
+    % %             figure, plot(squeeze(SRAcorr(iCounter,time_counter,:)));
+    % %             hold on, plot([1 length(squeeze(SRAcorr(iCounter,time_counter,:)))],[SRA_mean SRA_mean],'r-');
+    % %         end
+    %         %%%
+    %
+    %         %generate IPI-histogram: take the first 3 intervals of SRAcorr
+    %         %(positive delay) in the first 5 ms
+    %         histcounter = 1;
+    %         for lCounter = index_of_z_crossings_main_index+1:min([length(valid_z_crossings_indices(index_of_z_crossings_main_index+1:end)) 3])+index_of_z_crossings_main_index
+    %             sampledifference = abs(valid_z_crossings_indices(lCounter)-valid_z_crossings_indices(lCounter-1));
+    %             %the difference between two adjacent peaks in the SRA is taken
+    %             %as IPI estimate
+    %             IPIhisttime(iCounter,time_counter,histcounter) = sampledifference*one_sample_is_a_time_of;
+    %             %the amplitude of the SRA at the start of the SRA interval is
+    %             %taken as the IPIweight
+    %             IPIhistweight(iCounter,time_counter,histcounter) = SRAcorr(iCounter,time_counter,valid_z_crossings_indices(lCounter-1));
+    %             histcounter = histcounter + 1;
+    %         end
+    
+    %time_counter = time_counter+1;
+end
+