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1 function [num_compares] = get_comparison_stats()
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2 % [num_compares] = get_comparison_stats()
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3 %
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4 % - retrieves number of comparisons for each pair of data clips
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5 % - extracts genre associations and statistics
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6
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7 global db_magnaclips;
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8 global comparison;
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9 global comparison_ids;
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10
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11 num_compares = sparse(numel(comparison_ids), numel(comparison_ids));
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12
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13 for i = 1:size(comparison,1)
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14 abc = sort(comparison(i,1:3));
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15 a = abc(1);
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16 b = abc(2);
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17 c = abc(3);
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18 num_compares(a,b) = num_compares(a,b) +1;
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19 num_compares(b,c) = num_compares(b,c) +1;
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20 num_compares(a,c) = num_compares(a,c) +1;
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21 end
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22
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23 % ---
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24 % check for repeated pairs
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25 % ---
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26 % [i,j] = ind2sub(size(num_compares), find(num_compares > 1))
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27 % num = 300;
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28 % a = find((comparison(:,1) == i(num) | comparison(:,2) == i(num) | comparison(:,3) == i(num))...)
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29 % & (comparison(:,1) == j(num) | comparison(:,2) == j(num) | comparison(:,3) == j(num)))
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30 %
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31 % comparison(a,:)
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32
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33
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34 % ------------------------------------------------------------
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35 % compare genre frequency
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36 % ---
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37 clips = MTTClip(comparison_ids);
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38
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39 % comparison genres
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40 [genres, scores, gid] = clips.genres();
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41
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42 % all genres
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43 [genres2, scores2, gid2] = db_magnaclips.genredb.stats;
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44
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45 % ---
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46 % plot top genres
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47 % ---
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48
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49 figure;
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50 bar(scores(1:20))
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51 set(gca, 'XTick',1:20,'XTickLabel',genres(1:20))
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52
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53 figure;
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54 subplot(2,1,1)
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55 bar(scores(1:10) / max(scores))
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56 set(gca, 'XTickLabel',genres)
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57 title 'comparison subset'
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58
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59 subplot(2,1,2)
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60 bar(scores2(1:10) / max(scores2))
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61 set(gca, 'XTickLabel',genres2,'FontSize', 8)
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62 title 'Full MTT data set'
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63
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64 % ---
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65 % evaluate differences in distribution for all genres
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66 % shown are changes in relation to the whole database(genres2)
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67 % ( relative to the summed tags )
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68 % ---
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69 genrediff = zeros(numel(genres2),1);
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70 for i=1:numel(genres2)
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71
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72 % mgen = strcellfind(genres, genres2(i));
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73 mgen = find(gid == gid2(i));
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74
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75 if ~isempty(mgen)
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76 % genrediff(i,1) = scores(mgen)/sum(scores) - scores2(i)/sum(scores2);
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77 genrediff(i,1) = 1 - ( (scores2(i)/sum(scores2)) / (scores(mgen)/sum(scores)));
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78
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79 else
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80 % genrediff(i) = inf;
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81 error 'genre ids not consistent'
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82 end
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83 end
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84 genrediff = genrediff * 100;
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85
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86 % visualise difference
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87 figure
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88 bar(genrediff);
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89 set(gca, 'XTick',1:44,'XTickLabel',genres2)
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90 axis([0 45 -200 100]);
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91 title 'relative loss for each genre considering comparison as an excerpt from MTT'
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92 ylabel 'loss(%)'
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93
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94 % ---
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95 % get distinc genre music sets:
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96 % we try the following sets of 'similar' genre tags
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97 % ---
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98 cgdb = db_magnaclips.genredb.subset(clips.id);
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99
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100 cids = cgdb.owner({'Classical', 'Baroque'}, 'or');
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101 cgdb = cgdb.exclude(cids);
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102
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103 eids = cgdb.owner({'Electronica', 'New Age', 'Ambient'}, 'or');
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104 cgdb = cgdb.exclude(eids);
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105
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106 rids = cgdb.owner({'Rock', 'Alt Rock', 'Hard Rock', 'Metal'}, 'or');
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107 cgdb = cgdb.exclude(rids);
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108
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109 % get reverse indexing for comparisons
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110 rev_compid = sparse(comparison_ids, 1, 1:numel(comparison_ids));
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111
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112 % 8 triples entirely classical
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113 cfit = get_comparison_linfits(comparison, rev_compid(cids));
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114
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115 % 43 triples entirely electronic
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116 efit = get_comparison_linfits(comparison, rev_compid(eids));
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117
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118 % 6 triples entirely rock
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119 rfit = get_comparison_linfits(comparison, rev_compid(rids));
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120
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121 cgdb.stats
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122
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123 end
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124 % this function returns for each comparison line
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125 % the num. of appearance of given selection of clip ids
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126 function out = get_comparison_linfits(comparison, goodset)
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127
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128 out = zeros(1, size(comparison,1));
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129 % for each line
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130 for i = 1:size(comparison,1)
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131
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132 out(i) = numel(intersect(comparison(i,1:3), goodset'));
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133 end
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134 end
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135
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136
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