Daniel@0: function varargout = mirbeatspectrum(orig,varargin)
Daniel@0: %   n = mirbeatspectrum(m) evaluates the beat spectrum.
Daniel@0: %   [n,m] = mirbeatspectrum(m) also return the similarity matrix on which
Daniel@0: %       the estimation is made.
Daniel@0: %   Optional argument: 
Daniel@0: %       mirbeatspectrum(...,s) specifies the estimation method.
Daniel@0: %           Possible values:
Daniel@0: %           s = 'Diag', summing simply along the diagonals of the matrix.
Daniel@0: %           s = 'Autocor', based on the autocorrelation of the matrix.
Daniel@0: %       mirbeatspectrum(...,'Distance',f) specifies the name of a dissimilarity
Daniel@0: %           distance function, from those proposed in the Statistics Toolbox
Daniel@0: %               (help pdist).
Daniel@0: %           default value: f = 'cosine'
Daniel@0: %   J. Foote, M. Cooper, U. Nam, "Audio Retrieval by Rhythmic Similarity",
Daniel@0: %   ISMIR 2002.
Daniel@0: 
Daniel@0: 
Daniel@0:         dist.key = 'Distance';
Daniel@0:         dist.type = 'String';
Daniel@0:         dist.default = 'cosine';
Daniel@0:     option.dist = dist;
Daniel@0: 
Daniel@0:         meth.type = 'String';
Daniel@0:         meth.choice = {'Diag','Autocor'};
Daniel@0:         meth.default = 'Autocor';
Daniel@0:     option.meth = meth;
Daniel@0: 
Daniel@0: specif.option = option;
Daniel@0: varargout = mirfunction(@mirbeatspectrum,orig,varargin,nargout,specif,@init,@main);
Daniel@0:     
Daniel@0: 
Daniel@0: function [x type] = init(x,option)
Daniel@0: if not(isamir(x,'mirscalar'))
Daniel@0:     if isamir(x,'miraudio')
Daniel@0:         x = mirmfcc(x,'frame',.025,'s',.01,'s','Rank',8:30);
Daniel@0:     end
Daniel@0:     x = mirsimatrix(x,'Distance',option.dist,'Similarity');
Daniel@0: end
Daniel@0: type = 'mirscalar';
Daniel@0: 
Daniel@0: 
Daniel@0: function y = main(orig,option,postoption)
Daniel@0: if iscell(orig)
Daniel@0:     orig = orig{1};
Daniel@0: end
Daniel@0: fp = get(orig,'FramePos');
Daniel@0: if not(isa(orig,'mirscalar'))
Daniel@0:     s = get(orig,'Data');
Daniel@0:     total = cell(1,length(s));
Daniel@0:     for k = 1:length(s)
Daniel@0:         for h = 1:length(s{k})
Daniel@0:             maxfp = find(fp{k}{h}(2,:)>4,1);
Daniel@0:             if isempty(maxfp)
Daniel@0:                 maxfp = Inf;
Daniel@0:             else
Daniel@0:                 fp{k}{h}(:,maxfp+1:end) = [];
Daniel@0:             end
Daniel@0:             l = min(length(s{k}{h}),maxfp);
Daniel@0:             total{k}{h} = zeros(1,l);
Daniel@0:             if strcmpi(option.meth,'Diag')
Daniel@0:                 for i = 1:l
Daniel@0:                     total{k}{h}(i) = mean(diag(s{k}{h},i-1));
Daniel@0:                 end
Daniel@0:             else
Daniel@0:                 for i = 1:l
Daniel@0:                     total{k}{h}(i) = mean(mean(s{k}{h}(:,1:l-i+1).*s{k}{h}(:,i:l)));
Daniel@0:                 end
Daniel@0:             end
Daniel@0:         end
Daniel@0:     end
Daniel@0: else
Daniel@0:     total = get(orig,'Data');
Daniel@0: end
Daniel@0: n = mirscalar(orig,'Data',total,'FramePos',fp,'Title','Beat Spectrum'); 
Daniel@0: y = {n orig};