Mercurial > hg > camir-aes2014

annotate toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirclassify/mirclassify.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 c = mirclassify(a,da,t,dt,varargin)
wolffd@0 2 % c = mirclassify(test,features_test,train,features_train) classifies the
wolffd@0 3 % audio sequence(s) contained in the audio object test, along the
wolffd@0 4 % analytic feature(s) features_test, following the supervised
wolffd@0 5 % learning of a training set defined by the audio object train and
wolffd@0 6 % the corresponding analytic feature(s) features_train.
wolffd@0 7 % * The analytic feature(s) features_test should *not* be frame
wolffd@0 8 % decomposed. Frame-decomposed data should first be
wolffd@0 9 % summarized, using for instance mirmean or mirstd.
wolffd@0 10 % * Multiple analytic features have to be grouped into one array
wolffd@0 11 % of cells.
wolffd@0 12 % You can also integrate your own arrays of numbers computed outside
wolffd@0 13 % MIRtoolbox as part of the features. These arrays should be
wolffd@0 14 % given as matrices where each successive column is the analysis
wolffd@0 15 % of each successive file.
wolffd@0 16 % Example:
wolffd@0 17 % mirclassify(test, mfcc(test), train, mfcc(train))
wolffd@0 18 % mirclassify(test, {mfcc(test), centroid(test)}, ...
wolffd@0 19 % train, {mfcc(train), centroid(train)})
wolffd@0 20 % Optional argument:
wolffd@0 21 % mirclassify(...,'Nearest') uses the minimum distance strategy.
wolffd@0 22 % (by default)
wolffd@0 23 % mirclassify(...,'Nearest',k) uses the k-nearest-neighbour strategy.
wolffd@0 24 % Default value: k = 1, corresponding to the minimum distance
wolffd@0 25 % strategy.
wolffd@0 26 % mirclassify(...,'GMM',ng) uses a gaussian mixture model. Each class is
wolffd@0 27 % modeled by at most ng gaussians.
wolffd@0 28 % Default value: ng = 1.
wolffd@0 29 % Additionnally, the type of mixture model can be specified,
wolffd@0 30 % using the set of value proposed in the gmm function: i.e.,
wolffd@0 31 % 'spherical','diag','full' (default value) and 'ppca'.
wolffd@0 32 % (cf. help gmm)
wolffd@0 33 % Requires the Netlab toolbox.
wolffd@0 34
wolffd@0 35 lab = get(t,'Label');
wolffd@0 36 c.labtraining = lab;
wolffd@0 37 rlab = get(a,'Label');
wolffd@0 38 c.labtest = rlab;
wolffd@0 39 [k,ncentres,covartype,kmiter,emiter,d,norml,mahl] = scanargin(varargin);
wolffd@0 40 disp('Classifying...')
wolffd@0 41 if not(iscell(dt))
wolffd@0 42 dt = {dt};
wolffd@0 43 end
wolffd@0 44 lvt = length(get(t,'Data'));
wolffd@0 45 vt = [];
wolffd@0 46 for i = 1:length(dt)
wolffd@0 47 if isnumeric(dt{i})
wolffd@0 48 d = cell(1,size(dt{i},2));
wolffd@0 49 for j = 1:size(dt{i},2)
wolffd@0 50 d{j} = dt{i}(:,j);
wolffd@0 51 end
wolffd@0 52 else
wolffd@0 53 d = get(dt{i},'Data');
wolffd@0 54 end
wolffd@0 55 vt = integrate(vt,d,lvt,norml);
wolffd@0 56 if isa(dt{i},'scalar')
wolffd@0 57 m = mode(dt{i});
wolffd@0 58 if not(isempty(m))
wolffd@0 59 vt = integrate(vt,m,lvt,norml);
wolffd@0 60 end
wolffd@0 61 end
wolffd@0 62 end
wolffd@0 63 c.training = vt;
wolffd@0 64 dim = size(vt,1);
wolffd@0 65 if not(iscell(da))
wolffd@0 66 da = {da};
wolffd@0 67 end
wolffd@0 68 lva = length(get(a,'Data'));
wolffd@0 69 va = [];
wolffd@0 70 for i = 1:length(da)
wolffd@0 71 if isnumeric(da{i})
wolffd@0 72 d = cell(1,size(da{i},2));
wolffd@0 73 for j = 1:size(da{i},2)
wolffd@0 74 d{j} = da{i}(:,j);
wolffd@0 75 end
wolffd@0 76 else
wolffd@0 77 d = get(da{i},'Data');
wolffd@0 78 end
wolffd@0 79 va = integrate(va,d,lva,norml);
wolffd@0 80 if isa(da{i},'scalar')
wolffd@0 81 m = mode(da{i});
wolffd@0 82 if not(isempty(m))
wolffd@0 83 va = integrate(va,m,lva,norml);
wolffd@0 84 end
wolffd@0 85 end
wolffd@0 86 end
wolffd@0 87 c.test = va;
wolffd@0 88 c.nbobs = lvt;
wolffd@0 89 totva = [vt va];
wolffd@0 90 mahl = cov(totva');
wolffd@0 91 if k % k-Nearest Neighbour
wolffd@0 92 c.nbparam = lvt;
wolffd@0 93 for l = 1:lva
wolffd@0 94 [sv,idx] = sort(distance(va(:,l),vt,d,mahl));
wolffd@0 95 labs = cell(0); % Class labels
wolffd@0 96 founds = []; % Number of found elements in each class
wolffd@0 97 for i = idx(1:k)
wolffd@0 98 labi = lab{i};
wolffd@0 99 found = 0;
wolffd@0 100 for j = 1:length(labs)
wolffd@0 101 if isequal(labi,labs{j})
wolffd@0 102 found = j;
wolffd@0 103 end
wolffd@0 104 end
wolffd@0 105 if found
wolffd@0 106 founds(found) = founds(found)+1;
wolffd@0 107 else
wolffd@0 108 labs{end+1} = labi;
wolffd@0 109 founds(end+1) = 1;
wolffd@0 110 end
wolffd@0 111 end
wolffd@0 112 [b ib] = max(founds);
wolffd@0 113 c.classes{l} = labs{ib};
wolffd@0 114 end
wolffd@0 115 elseif ncentres % Gaussian Mixture Model
wolffd@0 116 labs = cell(0); % Class labels
wolffd@0 117 founds = cell(0); % Elements associated to each label.
wolffd@0 118 for i = 1:lvt
wolffd@0 119 labi = lab{i};
wolffd@0 120 found = 0;
wolffd@0 121 for j = 1:length(labs)
wolffd@0 122 if isequal(labi,labs{j})
wolffd@0 123 founds{j}(end+1) = i;
wolffd@0 124 found = 1;
wolffd@0 125 end
wolffd@0 126 end
wolffd@0 127 if not(found)
wolffd@0 128 labs{end+1} = labi;
wolffd@0 129 founds{end+1} = i;
wolffd@0 130 end
wolffd@0 131 end
wolffd@0 132 options = zeros(1, 18);
wolffd@0 133 options(2:3) = 1e-4;
wolffd@0 134 options(4) = 1e-6;
wolffd@0 135 options(16) = 1e-8;
wolffd@0 136 options(17) = 0.1;
wolffd@0 137 options(1) = 0; %Prints out error values, -1 else
wolffd@0 138 c.nbparam = 0;
wolffd@0 139 OK = 0;
wolffd@0 140 while not(OK)
wolffd@0 141 OK = 1;
wolffd@0 142 for i = 1:length(labs)
wolffd@0 143 options(14) = kmiter;
wolffd@0 144 try
wolffd@0 145 mix{i} = gmm(dim,ncentres,covartype);
wolffd@0 146 catch
wolffd@0 147 error('ERROR IN CLASSIFY: Netlab toolbox not installed.');
wolffd@0 148 end
wolffd@0 149 mix{i} = netlabgmminit(mix{i},vt(:,founds{i})',options);
wolffd@0 150 options(5) = 1;
wolffd@0 151 options(14) = emiter;
wolffd@0 152 try
wolffd@0 153 mix{i} = gmmem(mix{i},vt(:,founds{i})',options);
wolffd@0 154 c.nbparam = c.nbparam + ...
wolffd@0 155 length(mix{i}.centres(:)) + length(mix{i}.covars(:));
wolffd@0 156 catch
wolffd@0 157 err = lasterr;
wolffd@0 158 warning('WARNING IN CLASSIFY: Problem when calling GMMEM:');
wolffd@0 159 disp(err);
wolffd@0 160 disp('Let us try again...');
wolffd@0 161 OK = 0;
wolffd@0 162 end
wolffd@0 163 end
wolffd@0 164 end
wolffd@0 165 pr = zeros(lva,length(labs));
wolffd@0 166 for i = 1:length(labs)
wolffd@0 167 prior = length(founds{i})/lvt;
wolffd@0 168 pr(:,i) = prior * gmmprob(mix{i},va');
wolffd@0 169 %c.post{i} = gmmpost(mix{i},va');
wolffd@0 170 end
wolffd@0 171 [mm ib] = max(pr');
wolffd@0 172 for i = 1:lva
wolffd@0 173 c.classes{i} = labs{ib(i)};
wolffd@0 174 end
wolffd@0 175 end
wolffd@0 176 if isempty(rlab)
wolffd@0 177 c.correct = NaN;
wolffd@0 178 else
wolffd@0 179 correct = 0;
wolffd@0 180 for i = 1:lva
wolffd@0 181 if isequal(c.classes{i},rlab{i})
wolffd@0 182 correct = correct + 1;
wolffd@0 183 end
wolffd@0 184 end
wolffd@0 185 c.correct = correct / lva;
wolffd@0 186 end
wolffd@0 187 c = class(c,'mirclassify');
wolffd@0 188
wolffd@0 189
wolffd@0 190 function vt = integrate(vt,v,lvt,norml)
wolffd@0 191 vtl = [];
wolffd@0 192 for l = 1:lvt
wolffd@0 193 vl = v{l};
wolffd@0 194 if iscell(vl)
wolffd@0 195 vl = vl{1};
wolffd@0 196 end
wolffd@0 197 if iscell(vl)
wolffd@0 198 vl = vl{1};
wolffd@0 199 end
wolffd@0 200 if size(vl,2) > 1
wolffd@0 201 mirerror('MIRCLASSIFY','The analytic features guiding the classification should not be frame-decomposed.');
wolffd@0 202 end
wolffd@0 203 vtl(:,l) = vl;
wolffd@0 204 end
wolffd@0 205 if norml
wolffd@0 206 dnom = repmat(std(vtl,0,2),[1 size(vtl,2)]);
wolffd@0 207 dnom = dnom + (dnom == 0); % In order to avoid division by 0
wolffd@0 208 vtl = (vtl - repmat(mean(vtl,2),[1 size(vtl,2)])) ./ dnom;
wolffd@0 209 end
wolffd@0 210 vt(end+1:end+size(vtl,1),:) = vtl;
wolffd@0 211
wolffd@0 212
wolffd@0 213 function [k,ncentres,covartype,kmiter,emiter,d,norml,mahl] = scanargin(v)
wolffd@0 214 k = 1;
wolffd@0 215 d = 0;
wolffd@0 216 i = 1;
wolffd@0 217 ncentres = 0;
wolffd@0 218 covartype = 'full';
wolffd@0 219 kmiter = 10;
wolffd@0 220 emiter = 100;
wolffd@0 221 norml = 1;
wolffd@0 222 mahl = 1;
wolffd@0 223 while i <= length(v)
wolffd@0 224 arg = v{i};
wolffd@0 225 if ischar(arg) && strcmpi(arg,'Nearest')
wolffd@0 226 k = 1;
wolffd@0 227 if length(v)>i && isnumeric(v{i+1})
wolffd@0 228 i = i+1;
wolffd@0 229 k = v{i};
wolffd@0 230 end
wolffd@0 231 elseif ischar(arg) && strcmpi(arg,'GMM')
wolffd@0 232 k = 0;
wolffd@0 233 ncentres = 1;
wolffd@0 234 if length(v)>i
wolffd@0 235 if isnumeric(v{i+1})
wolffd@0 236 i = i+1;
wolffd@0 237 ncentres = v{i};
wolffd@0 238 if length(v)>i && ischar(v{i+1})
wolffd@0 239 i = i+1;
wolffd@0 240 covartype = v{i};
wolffd@0 241 end
wolffd@0 242 elseif ischar(v{i+1})
wolffd@0 243 i = i+1;
wolffd@0 244 covartype = v{i};
wolffd@0 245 if length(v)>i && isnumeric(v{i+1})
wolffd@0 246 i = i+1;
wolffd@0 247 ncentres = v{i};
wolffd@0 248 end
wolffd@0 249 end
wolffd@0 250 end
wolffd@0 251 elseif isnumeric(arg)
wolffd@0 252 k = v{i};
wolffd@0 253 else
wolffd@0 254 error('ERROR IN MIRCLASSIFY: Syntax error. See help mirclassify.');
wolffd@0 255 end
wolffd@0 256 i = i+1;
wolffd@0 257 end
wolffd@0 258
wolffd@0 259
wolffd@0 260 function y = distance(a,t,d,mahl)
wolffd@0 261
wolffd@0 262 for i = 1:size(t,2)
wolffd@0 263 if det(mahl) > 0 % more generally, uses cond
wolffd@0 264 lham = inv(mahl);
wolffd@0 265 else
wolffd@0 266 lham = pinv(mahl);
wolffd@0 267 end
wolffd@0 268 y(i) = sqrt((a - t(:,i))'*lham*(a - t(:,i)));
wolffd@0 269 end
wolffd@0 270 %y = sqrt(sum(repmat(a,[1,size(t,2)])-t,1).^2);