wolffd@0: function varargout = mirrolloff(x,varargin)
wolffd@0: %   r = mirrolloff(s) calculates the spectral roll-off in Hz.
wolffd@0: %   Optional arguments:
wolffd@0: %   r = mirrolloff(s,'Threshold',p) specifies the energy threshold in 
wolffd@0: %       percentage. (Default: .85)
wolffd@0: %           p can be either a value between 0 and 1. But if p exceeds 1, it
wolffd@0: %               is understood as a percentage, i.e. between 1 and 100.  
wolffd@0: %           In other words, r is the frequency under which p percents
wolffd@0: %               of the spectral energy is distributed.
wolffd@0: %
wolffd@0: % Typical values for the energy threshold:
wolffd@0: %       85% in G. Tzanetakis, P. Cook. Musical genre classification of audio
wolffd@0: %           signals. IEEE Tr. Speech and Audio Processing, 10(5),293-302, 2002.
wolffd@0: %       95% in T. Pohle, E. Pampalk, G. Widmer. Evaluation of Frequently
wolffd@0: %           Used Audio Features for Classification of Music Into Perceptual
wolffd@0: %           Categories, ?
wolffd@0: 
wolffd@0:         p.key = 'Threshold';
wolffd@0:         p.type = 'Integer';
wolffd@0:         p.default = 85;
wolffd@0:         p.position = 2;
wolffd@0:     option.p = p;
wolffd@0:     
wolffd@0: specif.option = option;
wolffd@0: 
wolffd@0: varargout = mirfunction(@mirrolloff,x,varargin,nargout,specif,@init,@main);
wolffd@0: 
wolffd@0: 
wolffd@0: function [s type] = init(x,option)
wolffd@0: s = mirspectrum(x);
wolffd@0: type = 'mirscalar';
wolffd@0: 
wolffd@0: 
wolffd@0: function r = main(s,option,postoption)
wolffd@0: if iscell(s)
wolffd@0:     s = s{1};
wolffd@0: end
wolffd@0: m = get(s,'Magnitude');
wolffd@0: f = get(s,'Frequency');
wolffd@0: if option.p>1
wolffd@0:     option.p = option.p/100;
wolffd@0: end
wolffd@0: v = mircompute(@algo,m,f,option.p);
wolffd@0: r = mirscalar(s,'Data',v,'Title','Rolloff','Unit','Hz.');
wolffd@0: 
wolffd@0: 
wolffd@0: function v = algo(m,f,p)
wolffd@0: cs = cumsum(m);          % accumulation of spectrum energy
wolffd@0: thr = cs(end,:,:)*p;   % threshold corresponding to the rolloff point
wolffd@0: v = zeros(1,size(cs,2),size(cs,3));
wolffd@0: for l = 1:size(cs,3)
wolffd@0:     for k = 1:size(cs,2)
wolffd@0:         fthr = find(cs(:,k,l) >= thr(1,k,l)); % find the location of the threshold
wolffd@0:         if isempty(fthr)
wolffd@0:             v(1,k,l) = NaN;
wolffd@0:         else
wolffd@0:             v(1,k,l) = f(fthr(1));
wolffd@0:         end
wolffd@0:     end
wolffd@0: end