wolffd@0: function varargout = mirattackslope(orig,varargin)
wolffd@0: %   a = mirattackslope(x) estimates the average slope of each note attack. 
wolffd@0: %   Optional arguments:
wolffd@0: %   a = mirattackslope(x,m) specifies a method for slope computation.
wolffd@0: %       Possible values:
wolffd@0: %           m = 'Diff': ratio between the magnitude difference at the 
wolffd@0: %               beginning and the ending of the attack period, and the
wolffd@0: %               corresponding time difference.
wolffd@0: %           m = 'Gauss': average of the slope, weighted by a gaussian
wolffd@0: %               curve that emphasizes values at the middle of the attack
wolffd@0: %               period. (similar to Peeters 2004).
wolffd@0: %       mirattackslope(...,'Contrast',c) specifies the 'Contrast' parameter
wolffd@0: %           used in mironsets for event detection through peak picking.
wolffd@0: %           Same default value as in mironsets.
wolffd@0: %
wolffd@0: % Peeters. G. (2004). A large set of audio features for sound description
wolffd@0: % (similarity and classification) in the CUIDADO project. version 1.0
wolffd@0: 
wolffd@0:         meth.type = 'String';
wolffd@0:         meth.choice = {'Diff','Gauss'};
wolffd@0:         meth.default = 'Diff';
wolffd@0:     option.meth = meth;
wolffd@0:     
wolffd@0:         cthr.key = 'Contrast';
wolffd@0:         cthr.type = 'Integer';
wolffd@0:         cthr.default = NaN;
wolffd@0:     option.cthr = cthr;
wolffd@0:     
wolffd@0: specif.option = option;
wolffd@0: 
wolffd@0: varargout = mirfunction(@mirattackslope,orig,varargin,nargout,specif,@init,@main);
wolffd@0: 
wolffd@0: 
wolffd@0: function [o type] = init(x,option)
wolffd@0: o = mironsets(x,'Attack','Contrast',option.cthr);
wolffd@0: type = mirtype(x);
wolffd@0: 
wolffd@0: 
wolffd@0: function sl = main(o,option,postoption)
wolffd@0: if iscell(o)
wolffd@0:     o = o{1};
wolffd@0: end
wolffd@0: po = get(o,'PeakPos');
wolffd@0: pa = get(o,'AttackPos');
wolffd@0: pou = get(o,'PeakPosUnit');
wolffd@0: pau = get(o,'AttackPosUnit');
wolffd@0: sr = get(o,'Sampling');
wolffd@0: d = get(o,'Data');
wolffd@0: sl = mircompute(@algo,po,pa,pou,pau,d,option.meth,sr);
wolffd@0: fp = mircompute(@frampose,pau,pou);
wolffd@0: sl = mirscalar(o,'Data',sl,'FramePos',fp,'Title','Attack Slope');
wolffd@0: sl = {sl,o};
wolffd@0: 
wolffd@0: 
wolffd@0: function fp = frampose(pa,po)
wolffd@0: pa = sort(pa{1});
wolffd@0: po = sort(po{1});
wolffd@0: fp = [pa';po'];
wolffd@0: 
wolffd@0: 
wolffd@0: function sl = algo(po,pa,pou,pau,d,meth,sr)
wolffd@0: pa = sort(pa{1});
wolffd@0: po = sort(po{1});
wolffd@0: pau = sort(pau{1});
wolffd@0: pou = sort(pou{1});
wolffd@0: sl = zeros(1,length(pa));
wolffd@0: for i = 1:length(pa)
wolffd@0:     switch meth
wolffd@0:         case 'Diff'
wolffd@0:             sl(i) = (d(po(i))-d(pa(i)))/(pou(i)-pau(i));
wolffd@0:         case 'Gauss'
wolffd@0:             l = po(i)-pa(i);
wolffd@0:             h = ceil(l/2);
wolffd@0:             gauss = exp(-(1-h:l-h).^2/(l/4)^2);
wolffd@0:             dat = diff(d(pa(i):po(i))).*gauss';
wolffd@0:             sl(i) = mean(dat)*sr;
wolffd@0:     end
wolffd@0: end