wolffd@0: function varargout = mireventdensity(x,varargin)
wolffd@0: %   e = mireventdensity(x) estimate the mean frequency of events (i.e., how
wolffd@0: %       many note onsets per second) in the temporal data x.
wolffd@0: 
wolffd@0: %   Optional arguments: Option1, Option2
wolffd@0: % Tuomas Eerola, 14.08.2008
wolffd@0: %
wolffd@0:         normal.type = 'String';
wolffd@0:         normal.choice = {'Option1','Option2'};
wolffd@0:         normal.default = 'Option1';
wolffd@0:     option.normal = normal;
wolffd@0: 
wolffd@0:         frame.key = 'Frame';
wolffd@0:         frame.type = 'Integer';
wolffd@0:         frame.number = 2;
wolffd@0:         frame.default = [0 0];
wolffd@0:         frame.keydefault = [10 1];
wolffd@0:     option.frame = frame;
wolffd@0:     
wolffd@0: specif.option = option;
wolffd@0: 
wolffd@0: specif.defaultframelength = 1.00;
wolffd@0: specif.defaultframehop = 0.5;
wolffd@0: 
wolffd@0: %specif.eachchunk = 'Normal';
wolffd@0: specif.combinechunk = {'Average','Concat'};
wolffd@0: 
wolffd@0: varargout = mirfunction(@mireventdensity,x,varargin,nargout,specif,@init,@main);
wolffd@0: 
wolffd@0: 
wolffd@0: function [x type] = init(x,option)
wolffd@0: if not(isamir(x,'mirenvelope'))
wolffd@0:     if option.frame.length.val
wolffd@0:         x = mironsets(x,'Klapuri99', 'Frame',option.frame.length.val,...
wolffd@0:                                               option.frame.length.unit,...
wolffd@0:                                               option.frame.hop.val,...
wolffd@0:                                               option.frame.hop.unit);
wolffd@0:     else
wolffd@0:         x = mironsets(x,'Klapuri99');
wolffd@0:     end
wolffd@0: end
wolffd@0: type = 'mirscalar';
wolffd@0: 
wolffd@0: 
wolffd@0: function e = main(o,option,postoption)
wolffd@0: if iscell(o)
wolffd@0:     o = o{1};
wolffd@0: end
wolffd@0: sr = get(o,'Sampling');
wolffd@0: p = mirpeaks(o); %%%%<<<<<<< MORE OPTIONS HERE
wolffd@0: pv = get(p,'PeakVal');
wolffd@0: v = mircompute(@algo,pv,o,option,sr);
wolffd@0: e = mirscalar(o,'Data',v,'Title','Event density','Unit','per second');
wolffd@0: e = {e o};
wolffd@0: 
wolffd@0: 
wolffd@0: function e = algo(pv,o,option,sr)
wolffd@0: nc = size(o,2);
wolffd@0: nch = size(o,3);
wolffd@0: e = zeros(1,nc,nch);
wolffd@0: % for i = 1:nch
wolffd@0: %     for j = 1:nc
wolffd@0: %         if option.root
wolffd@0: %             e(1,j,i) = norm(d(:,j,i));
wolffd@0: %         else
wolffd@0: %             disp('do the calc...')
wolffd@0: %  %           e(1,j,i) = d(:,j,i)'*d(:,j,i);
wolffd@0: %             %tmp = mironsets(d,'Filterbank',10,'Contrast',0.1); % Change by TE, was only FB=20, no other params
wolffd@0: %             e2 = mirpeaks(e)
wolffd@0: %             [o1,o2] = mirgetdata(e);
wolffd@0: %             e(1,j,i) = length(o2)/mirgetdata(mirlength(d)); 
wolffd@0: %         end
wolffd@0: %     end
wolffd@0: % end
wolffd@0: for i = 1:nch
wolffd@0:     for j = 1:nc
wolffd@0:         e(1,j,i) = length(pv{1,j,i});
wolffd@0:         if strcmpi(option.normal,'Option1')
wolffd@0:             e(1,j,i) = e(1,j,i) *sr/size(o,1);
wolffd@0:         elseif strcmpi(option.normal,'Option2')
wolffd@0:             pvs = pv{1};
wolffd@0:             high_pvs = length(find(mean(pvs)>pvs));
wolffd@0:             e(1,j,i) = high_pvs(1,j,i) *sr/size(o,1); % only those which are larger than mean
wolffd@0:         end
wolffd@0:     end
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: 
wolffd@0: %function [y orig] = eachchunk(orig,option,missing,postchunk)
wolffd@0: %y = mireventdensity(orig,option);
wolffd@0: 
wolffd@0: 
wolffd@0: %function y = combinechunk(old,new)
wolffd@0: %do = mirgetdata(old);
wolffd@0: %dn = mirgetdata(new);
wolffd@0: %y = set(old,'ChunkData',do+dn);