diff toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirbeatspectrum.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirbeatspectrum.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,75 @@
+function varargout = mirbeatspectrum(orig,varargin)
+%   n = mirbeatspectrum(m) evaluates the beat spectrum.
+%   [n,m] = mirbeatspectrum(m) also return the similarity matrix on which
+%       the estimation is made.
+%   Optional argument: 
+%       mirbeatspectrum(...,s) specifies the estimation method.
+%           Possible values:
+%           s = 'Diag', summing simply along the diagonals of the matrix.
+%           s = 'Autocor', based on the autocorrelation of the matrix.
+%       mirbeatspectrum(...,'Distance',f) specifies the name of a dissimilarity
+%           distance function, from those proposed in the Statistics Toolbox
+%               (help pdist).
+%           default value: f = 'cosine'
+%   J. Foote, M. Cooper, U. Nam, "Audio Retrieval by Rhythmic Similarity",
+%   ISMIR 2002.
+
+
+        dist.key = 'Distance';
+        dist.type = 'String';
+        dist.default = 'cosine';
+    option.dist = dist;
+
+        meth.type = 'String';
+        meth.choice = {'Diag','Autocor'};
+        meth.default = 'Autocor';
+    option.meth = meth;
+
+specif.option = option;
+varargout = mirfunction(@mirbeatspectrum,orig,varargin,nargout,specif,@init,@main);
+    
+
+function [x type] = init(x,option)
+if not(isamir(x,'mirscalar'))
+    if isamir(x,'miraudio')
+        x = mirmfcc(x,'frame',.025,'s',.01,'s','Rank',8:30);
+    end
+    x = mirsimatrix(x,'Distance',option.dist,'Similarity');
+end
+type = 'mirscalar';
+
+
+function y = main(orig,option,postoption)
+if iscell(orig)
+    orig = orig{1};
+end
+fp = get(orig,'FramePos');
+if not(isa(orig,'mirscalar'))
+    s = get(orig,'Data');
+    total = cell(1,length(s));
+    for k = 1:length(s)
+        for h = 1:length(s{k})
+            maxfp = find(fp{k}{h}(2,:)>4,1);
+            if isempty(maxfp)
+                maxfp = Inf;
+            else
+                fp{k}{h}(:,maxfp+1:end) = [];
+            end
+            l = min(length(s{k}{h}),maxfp);
+            total{k}{h} = zeros(1,l);
+            if strcmpi(option.meth,'Diag')
+                for i = 1:l
+                    total{k}{h}(i) = mean(diag(s{k}{h},i-1));
+                end
+            else
+                for i = 1:l
+                    total{k}{h}(i) = mean(mean(s{k}{h}(:,1:l-i+1).*s{k}{h}(:,i:l)));
+                end
+            end
+        end
+    end
+else
+    total = get(orig,'Data');
+end
+n = mirscalar(orig,'Data',total,'FramePos',fp,'Title','Beat Spectrum'); 
+y = {n orig};
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