Mercurial > hg > camir-aes2014

annotate toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirdist.m @ 0:e9a9cd732c1e tip

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
parents
children
rev   line source
wolffd@0 1 function d = mirdist(x,y,dist)
wolffd@0 2 % d = mirdist(x,y) evaluates the distance between x and y.
wolffd@0 3 % x is the feature values corresponding to one audio file, and y is the
wolffd@0 4 % values (for the same feature) corrdponding to one (or several)
wolffd@0 5 % audio files.
wolffd@0 6 % If x and y are not decomposed into frames,
wolffd@0 7 % d = mirdist(x,y,f) specifies distance function.
wolffd@0 8 % Default value: f = 'Cosine'
wolffd@0 9 % If x and y are composed of clustered frames (using mircluster), the
wolffd@0 10 % cluster signatures are compared using Earth Mover Distance.
wolffd@0 11 % (Logan, Salomon, 2001)
wolffd@0 12 % If x and y contains peaks, the vectors representing the peak
wolffd@0 13 % distributions are compared using Euclidean distance.
wolffd@0 14 % (used with mirnovelty in Jacobson, 2006)
wolffd@0 15 %
wolffd@0 16 % The Earth Mover Distance is based on the implementation by Yossi Rubner,
wolffd@0 17 % wrapped for Matlab by Elias Pampalk.
wolffd@0 18
wolffd@0 19 if not(isa(x,'mirdata'))
wolffd@0 20 x = miraudio(x);
wolffd@0 21 end
wolffd@0 22 if not(isa(y,'mirdata'))
wolffd@0 23 y = miraudio(y);
wolffd@0 24 end
wolffd@0 25
wolffd@0 26 clx = get(x,'Clusters');
wolffd@0 27 if isempty(clx{1})
wolffd@0 28 px = get(x,'PeakPos');
wolffd@0 29 if not(iscell(px)) || isempty(px{1}) || ...
wolffd@0 30 not(iscell(px{1})) || isempty(px{1}{1}) || not(iscell(px{1}{1}))
wolffd@0 31 if nargin < 3
wolffd@0 32 dist = 'Cosine';
wolffd@0 33 end
wolffd@0 34
wolffd@0 35 d = get(x,'Data');
wolffd@0 36 dd = d{1}{1};
wolffd@0 37 if iscell(dd)
wolffd@0 38 dd = dd{1};
wolffd@0 39 end
wolffd@0 40 if size(dd,2)>1
wolffd@0 41 if size(dd,1)>1
wolffd@0 42 error('ERROR IN MIRDIST: If the input is decomposed into frames, they should first be clustered.');
wolffd@0 43 else
wolffd@0 44 dd = dd';
wolffd@0 45 end
wolffd@0 46 end
wolffd@0 47
wolffd@0 48 e = get(y,'Data');
wolffd@0 49 dt = cell(1,length(e));
wolffd@0 50 for h = 1:length(e)
wolffd@0 51 ee = e{h}{1};
wolffd@0 52 if iscell(ee)
wolffd@0 53 ee = ee{1};
wolffd@0 54 end
wolffd@0 55 if size(ee,2)>1
wolffd@0 56 if size(ee,1)>1
wolffd@0 57 error('ERROR IN MIRDIST: If the input is decomposed into frames, they should first be clustered.');
wolffd@0 58 else
wolffd@0 59 ee = ee';
wolffd@0 60 end
wolffd@0 61 end
wolffd@0 62 if isempty(ee)
wolffd@0 63 if isempty(dd)
wolffd@0 64 dt{h}{1} = 0;
wolffd@0 65 else
wolffd@0 66 dt{h}{1} = Inf;
wolffd@0 67 end
wolffd@0 68 else
wolffd@0 69 if length(dd)<length(ee)
wolffd@0 70 dd(length(ee)) = 0;
wolffd@0 71 %ee = ee(1:length(d));
wolffd@0 72 elseif length(ee)<length(dd)
wolffd@0 73 ee(length(dd)) = 0;
wolffd@0 74 %dd = dd(1:length(ee));
wolffd@0 75 end
wolffd@0 76 if length(dd) == 1
wolffd@0 77 dt{h}{1} = abs(dd-ee);
wolffd@0 78 elseif norm(dd) && norm(ee)
wolffd@0 79 dt{h}{1} = pdist([dd(:)';ee(:)'],dist);
wolffd@0 80 else
wolffd@0 81 dt{h}{1} = NaN;
wolffd@0 82 end
wolffd@0 83 end
wolffd@0 84 end
wolffd@0 85 else
wolffd@0 86 % Euclidean distance between vectors to compare data with peaks
wolffd@0 87 % (used with mirnovelty in Jacobson, 2006).
wolffd@0 88 sig = pi/4;
wolffd@0 89 dx = get(x,'Data');
wolffd@0 90 nx = length(px{1}{1}{1});
wolffd@0 91 cx = mean(px{1}{1}{1}/length(dx{1}{1}));
wolffd@0 92 dy = get(y,'Data');
wolffd@0 93 py = get(y,'PeakPos');
wolffd@0 94 dt = cell(1,length(py));
wolffd@0 95 for h = 1:length(py)
wolffd@0 96 ny = length(py{h}{1}{1});
wolffd@0 97 cy = mean(py{h}{1}{1}/length(dy{h}{1}));
wolffd@0 98 dt{h}{1} = sqrt((nx*cos(sig*cx)-ny*cos(sig*cy))^2 ...
wolffd@0 99 +(nx*sin(sig*cx)-ny*sin(sig*cy))^2);
wolffd@0 100 end
wolffd@0 101 end
wolffd@0 102 else
wolffd@0 103 % Earth Mover's Distance to compare clustered data.
wolffd@0 104 cly = get(y,'Clusters');
wolffd@0 105 dt = cell(1,length(cly));
wolffd@0 106 for h = 1:length(cly)
wolffd@0 107 cost = zeros(length(clx{1}.weight),length(cly{h}.weight));
wolffd@0 108 for i = 1:length(clx{1}.weight)
wolffd@0 109 for j = 1:length(cly{h}.weight)
wolffd@0 110 covx = clx{1}.covar(:,i);
wolffd@0 111 covy = cly{h}.covar(:,j);
wolffd@0 112 mux = clx{1}.centr(:,i);
wolffd@0 113 muy = cly{h}.centr(:,j);
wolffd@0 114 cost(i,j) = sum(covx./covy + covy./covx + ...
wolffd@0 115 (mux-muy).^2.*(1./covx + 1./covy) - 2);
wolffd@0 116 end
wolffd@0 117 end
wolffd@0 118 dt{h}{1} = emd_wrapper(cost,clx{1}.weight,cly{h}.weight);
wolffd@0 119 end
wolffd@0 120 end
wolffd@0 121 d = mirscalar(y,'Data',dt,'Title',[get(y,'Title'),' Distance'],...
wolffd@0 122 'Name',get(x,'Name'),'Name2',get(y,'Name'));