wolffd@0: % --
wolffd@0: % This class loads and hanles the aufdio features included with the MTT
wolffd@0: % Library
wolffd@0: % --- 
wolffd@0: classdef MTTMixedFeatureGenreBasicSmPCA < MTTAudioFeature & handle
wolffd@0:     
wolffd@0:     properties(Constant = true)
wolffd@0:         
wolffd@0:         my_revision = str2double(substr('$Rev$',  5, -1));
wolffd@0:     end
wolffd@0:     
wolffd@0:     properties
wolffd@0:         
wolffd@0:         % ---
wolffd@0:         % Set default parameters
wolffd@0:         % ---
wolffd@0:         my_basetype = 'MTTMixedFeatureGenreBasicSm';
wolffd@0:         
wolffd@0:         my_params = MTTAudioFeature.inherited_params(...
wolffd@0:             'MTTMixedFeatureGenreBasicSm', ...
wolffd@0:             'min_pca_var', 0, ... % % fraction of variance to keep
wolffd@0:             'max_pca_coeffs', 0, ...% max. number of final coefficients
wolffd@0:             'norm_pre_pca', 1, ...% normalise pca coefficients after transformation
wolffd@0:             'norm_post_pca', 1 ...
wolffd@0:         );
wolffd@0:     end
wolffd@0:     % ---
wolffd@0:     % member functions
wolffd@0:     % ---
wolffd@0:     methods
wolffd@0:         
wolffd@0:         % ---
wolffd@0:         % constructor: pointer to feature in database
wolffd@0:         % ---
wolffd@0:         function feature = MTTMixedFeatureGenreBasicSmPCA(varargin)
wolffd@0: 
wolffd@0:             feature = feature@MTTAudioFeature(varargin{:});
wolffd@0:         end
wolffd@0:         
wolffd@0:         % ---
wolffd@0:         % load feature data from xml file
wolffd@0:         % ---
wolffd@0:         function data = extract(feature, clip)
wolffd@0:         % load feature data by parsing xml
wolffd@0:             
wolffd@0:             global globalvars;
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % we extract the base features, and save 
wolffd@0:             % the pointers to these.
wolffd@0:             % the main work is then done in the define_global_transf
wolffd@0:             % and finalise functions.
wolffd@0:             % ---
wolffd@0:             data.basefeat = clip.features(feature.my_basetype,...
wolffd@0:                 feature.my_params); 
wolffd@0: 
wolffd@0:             % save info data
wolffd@0:             data.info.type = 'MTTMixedFeatureGenreBasicSmPCA';
wolffd@0:             data.info.owner_id = clip.id;
wolffd@0:             data.info.creatorrev = feature.my_revision;
wolffd@0:             
wolffd@0:             % save param data
wolffd@0:             data.info.params = feature.my_params;
wolffd@0:             
wolffd@0:             % prepare field for final features
wolffd@0:             data.final.vector = [];
wolffd@0:             data.final.dim = 0;
wolffd@0:             data.final.vector_info.labels = {};
wolffd@0:         end
wolffd@0:         
wolffd@0:         function define_global_transform(features)
wolffd@0:             
wolffd@0:             if numel(features) == 1
wolffd@0:                 error ('Insert feature array for this method');
wolffd@0:             end
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % We collect all the relevant genretag 
wolffd@0:             % features and get the transform on this basis.
wolffd@0:             % ---
wolffd@0:             for i = 1:numel(features)
wolffd@0:             	basef(i) = features(i).data.basefeat;
wolffd@0:             end
wolffd@0:             
wolffd@0:             % call the features own transsform function
wolffd@0:             basef.define_global_transform();
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % finalise the basic features, and 
wolffd@0:             % get the feature vectors;
wolffd@0:             % ---
wolffd@0:             basef.finalise();
wolffd@0:             X = basef.vector();
wolffd@0:             
wolffd@0:             % check dataset dimension
wolffd@0:             if numel(features) < basef.dim;
wolffd@0:                 
wolffd@0:                 error ('Not enough feature vectors for PCA calculation. need %d samples', ...
wolffd@0:                     basef.dim);
wolffd@0:             end
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % NOTE: should the data be normalised and scaled to -1:1 
wolffd@0:             % instead of being in a range of 0-1 AND max-min = 1
wolffd@0:             % --- 
wolffd@0:             if features(1).my_params.norm_pre_pca == 1
wolffd@0:                                 
wolffd@0:                 [X, pstd] = mapminmax(X,-1,1);
wolffd@0:                 common.pca.pre_norm = pstd;
wolffd@0:             elseif features(1).my_params.norm_pre_pca == 2
wolffd@0:                 
wolffd@0:                 [X, pstd] = mapstd(X,0,1);
wolffd@0:                 common.pca.pre_norm = pstd;
wolffd@0:             end
wolffd@0: 
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % get and apply the principal component analysis
wolffd@0:             % NOTE: the variance percentile is applied here, too.
wolffd@0:             % ---
wolffd@0: 
wolffd@0:             [Y, ps] = processpca(X, 0);
wolffd@0:             common.pca.transform = ps;
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % get cumulative sum of variance, and decide on cutoff
wolffd@0:             % point
wolffd@0:             % --- 
wolffd@0:             v = cumsum(var(Y'));
wolffd@0:             v = v / max(v);
wolffd@0:             common.pca.transform.varpart = v;
wolffd@0:             
wolffd@0:             if features(1).my_params.min_pca_var > 0
wolffd@0:                             
wolffd@0:                 min_pca_idx = find(v >= features(1).my_params.min_pca_var,1, 'first');
wolffd@0:                 
wolffd@0:                 % save into pca structure
wolffd@0:                 common.pca.transform.yrows = min_pca_idx;
wolffd@0:                 
wolffd@0:             end 
wolffd@0:             
wolffd@0:             % normalise pca values after processing
wolffd@0:             if features(1).my_params.norm_post_pca
wolffd@0:                 
wolffd@0:                 [Y,pmm] = mapminmax(Y,0,1);
wolffd@0:                 common.pca.post_norm = pmm;
wolffd@0:                 
wolffd@0:             end
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % set common feature values for mixed features
wolffd@0:             % ---
wolffd@0:             features(1).my_db.set_common(common);            
wolffd@0:             
wolffd@0:             % save the transformed features straight away!
wolffd@0:             features.finalise(Y);
wolffd@0:         end
wolffd@0:         
wolffd@0:         function finalise(feature, final)
wolffd@0:         % applies a final transformation and
wolffd@0:         % collects the information of this feature within a single vector
wolffd@0:         % see info for types in specific dimensions
wolffd@0:         
wolffd@0:             % determine size
wolffd@0:             if feature(1).my_params.max_pca_coeffs > 0
wolffd@0: 
wolffd@0:                 max_size = min(feature(1).common.pca.transform.yrows, ...
wolffd@0:                     feature(1).my_params.max_pca_coeffs);
wolffd@0:             else
wolffd@0: 
wolffd@0:                 max_size = feature(1).common.pca.transform.yrows;
wolffd@0:             end
wolffd@0: 
wolffd@0: 
wolffd@0:             % prepare information
wolffd@0:             info = {'PCA'};
wolffd@0:             if isfield(feature(1).common.pca.transform, 'varpart')
wolffd@0:                 info(2:max_size) = num2cell(feature(1).common.pca.transform.varpart(2:max_size));
wolffd@0:             else
wolffd@0:                 info(2:max_size) = num2cell(2:max_size);
wolffd@0:             end
wolffd@0: 
wolffd@0:         
wolffd@0:             % check if features have been finalised already
wolffd@0:             if nargin == 2 && isempty(final)
wolffd@0:                 
wolffd@0:                 % the final vector etc already are set to zero;
wolffd@0:                 return;
wolffd@0:                 
wolffd@0:             elseif nargin == 2 && (numel(feature) == size(final, 2))
wolffd@0:                     
wolffd@0:                 for i = 1:numel(feature)
wolffd@0:                     
wolffd@0:                     % save final vector and description
wolffd@0:                     feature(i).data.final.vector = final(1:max_size,i);
wolffd@0:                     feature(i).data.final.dim = max_size;
wolffd@0:                     feature(i).data.final.vector_info.labels = info;
wolffd@0:                 end
wolffd@0:                 
wolffd@0:             else
wolffd@0:             % features have to be transformed first
wolffd@0:             % ---
wolffd@0:             % TODO: this code remains untested
wolffd@0:             % ---
wolffd@0:             
wolffd@0:             % check for neccesary parameters
wolffd@0:                 if isempty(feature(1).my_db.commondb)
wolffd@0: 
wolffd@0:                     error('Define the global transformation first')
wolffd@0:                     return;
wolffd@0:                 end
wolffd@0:              
wolffd@0: 
wolffd@0:                 for i = 1:numel(feature)
wolffd@0: 
wolffd@0:                     % check for neccesary parameters
wolffd@0:                     if isempty(feature(i).my_db.commondb)
wolffd@0: 
wolffd@0:                         error('Define the global transformation first')
wolffd@0:                     end
wolffd@0:                     
wolffd@0:                     % ---
wolffd@0:                     % get feature vector and apply transformation
wolffd@0:                     % ---
wolffd@0:                     X = feature(i).data.basefeat.vector();
wolffd@0:                     
wolffd@0:                     % ---
wolffd@0:                     % apply normalisation used for removing mean 
wolffd@0:                     % in training data
wolffd@0:                     % ---
wolffd@0:                     if feature(1).my_params.norm_pre_pca == 1
wolffd@0:                                 
wolffd@0:                         X = mapminmax('apply', X, feature(1).common.pca.pre_norm);
wolffd@0:                     elseif feature(1).my_params.norm_pre_pca == 2
wolffd@0: 
wolffd@0:                         X = mapstd('apply', X, feature(1).common.pca.pre_norm);
wolffd@0:                     end
wolffd@0:  
wolffd@0:                     % apply PCA transform
wolffd@0:                     vec = processpca('apply', X, feature(1).common.pca.transform);
wolffd@0:                     
wolffd@0:                     % normalise pca values after transformation
wolffd@0:                     if feature(1).my_params.norm_post_pca
wolffd@0: 
wolffd@0:                         vec = mapminmax('apply', vec,...
wolffd@0:                             feature(1).common.pca.post_norm);
wolffd@0:                     end
wolffd@0:                     
wolffd@0:                     % ---
wolffd@0:                     % cut vector to final size. 
wolffd@0:                     % NOTE: this should be done before
wolffd@0:                     % transformation to reduce computation time
wolffd@0:                     % ---
wolffd@0:                     vec = vec(1:max_size);
wolffd@0:                     
wolffd@0:                     % save final vector and description
wolffd@0:                     feature(i).data.final.vector = vec;
wolffd@0:                     feature(i).data.final.dim = numel(vec);
wolffd@0:                     feature(i).data.final.vector_info.labels = info;
wolffd@0:                 end
wolffd@0:             end
wolffd@0:         end
wolffd@0:         
wolffd@0:     end
wolffd@0: end