wolffd@0: classdef MTTMixedFeatureStober11 < MTTAudioFeature & handle
wolffd@0:     % ---
wolffd@0:     % This Class contains 
wolffd@0:     % features are extracted 
wolffd@0:     % as described in Slaney 08 - LEARNING A METRIC FOR MUSIC SIMILARITY
wolffd@0:     %
wolffd@0:     % The usual workflow for these features constist of three steps
wolffd@0:     % 1. extract: extracts the basic single-file dependent features
wolffd@0:     % 2. define_global_transform: calculates the global feature
wolffd@0:     %       transformation parameters
wolffd@0:     % 3. finalise: applies the common transformations to a specific feature
wolffd@0:     % ---
wolffd@0:     
wolffd@0:     properties(Constant = true)
wolffd@0:         
wolffd@0:         % svn hook
wolffd@0:         my_revision = str2double(substr('$Rev$',  5, -1));
wolffd@0:     end
wolffd@0: 
wolffd@0:     properties
wolffd@0:         % ---
wolffd@0:         % Set default parameters
wolffd@0:         % ---
wolffd@0:         my_params = struct(...
wolffd@0:             'stob_lowaudio', 1, ...
wolffd@0:             'stob_highaudio', 1, ... % 
wolffd@0:             'stob_tags', 1, ... %
wolffd@0:             'stob_norm', 1 ...
wolffd@0:             );
wolffd@0:     end
wolffd@0:     
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 = MTTMixedFeatureStober11(varargin)
wolffd@0: 
wolffd@0:             feature = feature@MTTAudioFeature(varargin{:});
wolffd@0: 
wolffd@0:         end
wolffd@0:         % ---
wolffd@0:         % extract feature data from raw audio features
wolffd@0:         % ---
wolffd@0:         function data = extract(feature, clip)
wolffd@0:             % ---
wolffd@0:             % get features. this includes possible
wolffd@0:             % local normalisations
wolffd@0:             % ---
wolffd@0:             
wolffd@0:             global globalvars;
wolffd@0:             global stobbase;
wolffd@0:             
wolffd@0:             if isempty(stobbase);
wolffd@0:                 stobbase = load('features_stober');
wolffd@0:             end
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % NOTE: we define feature sets which are included /
wolffd@0:             % excluded according to the specified parameters
wolffd@0:             % ---
wolffd@0:             
wolffd@0:             lowAudio = {'pitchMean', 'pitchSdev', 'timbreMean', 'timbreSdev'};
wolffd@0:             
wolffd@0:             % highAudio features more or less correspond to slaney 08 features
wolffd@0:             hiAudio = {'energy','key','loudness','timeSignature',...
wolffd@0:                 'danceability', 'mode', 'tempo'};
wolffd@0:             
wolffd@0:             metadat = {'tags'};
wolffd@0:             
wolffd@0:             allowedFeat = {};
wolffd@0:             
wolffd@0:             % Select the features to keep
wolffd@0:             if feature(1).my_params.stob_lowaudio
wolffd@0:                 allowedFeat = {allowedFeat{:},  lowAudio{:}};
wolffd@0:             end
wolffd@0:             if feature(1).my_params.stob_highaudio
wolffd@0:                 allowedFeat = {allowedFeat{:},  hiAudio{:}};
wolffd@0:             end
wolffd@0:             if feature(1).my_params.stob_tags
wolffd@0:                 allowedFeat = {allowedFeat{:},  metadat{:}};
wolffd@0:             end
wolffd@0:             
wolffd@0:             % get the actual clip id
wolffd@0:             idx = find(stobbase.clipIds == clip.id);
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % NOTE: we just copy everything in a big matrix and then
wolffd@0:             % normalise the data later
wolffd@0:             % ---
wolffd@0:             data.vector_info = {};
wolffd@0:             data.stobraw = [];
wolffd@0:             fields = fieldnames(stobbase);
wolffd@0:             for i = 1:numel(fields)
wolffd@0:                 
wolffd@0:                 % skip clip ID field
wolffd@0:                 if strcmp(fields{i},'clipIds');
wolffd@0:                     continue;
wolffd@0:                 end
wolffd@0:                 
wolffd@0:                 % skip unwanted features
wolffd@0:                 if isempty(strcellfind(allowedFeat, fields{i}))
wolffd@0:                     continue;
wolffd@0:                 end
wolffd@0:                 
wolffd@0:                 % ---
wolffd@0:                 % TODO: special case for tag features, including
wolffd@0:                 % the tag names
wolffd@0:                 % ---
wolffd@0:                 
wolffd@0:                 % put field info into right position
wolffd@0:                 data.vector_info{numel(data.stobraw)+1} = fields{i};
wolffd@0:                 
wolffd@0:                 % add data to feature
wolffd@0:                 if size(stobbase.(fields{i}),1) == 1
wolffd@0:                     data.stobraw(end+1) = stobbase.(fields{i})(idx);
wolffd@0:                 else
wolffd@0:                     % concatenate vector
wolffd@0:                     tmpdat = stobbase.(fields{i})(idx,:);
wolffd@0:                     data.stobraw(end+1:end+numel(tmpdat)) = tmpdat;
wolffd@0:                 end
wolffd@0:             end
wolffd@0:             % padd further info struct
wolffd@0:             data.vector_info(end+1:numel(data.stobraw)) =...
wolffd@0:                 cell(numel(data.stobraw) - numel(data.vector_info) , 1); 
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % prepare field for final features
wolffd@0:             % ---
wolffd@0:             data.final.vector = [];
wolffd@0:             data.final.vector_info = struct(); 
wolffd@0:             data.final.dim = 0;
wolffd@0:            
wolffd@0:             % save info data
wolffd@0:             data.info.type = 'MTTMixedFeatureStober11';
wolffd@0:             data.info.owner_id = clip.id;
wolffd@0:             data.info.creatorrev = feature.my_revision;
wolffd@0:             
wolffd@0:             % save parameters
wolffd@0:             data.info.params = feature.my_params;
wolffd@0:         end
wolffd@0:         
wolffd@0:         function define_global_transform(features)
wolffd@0:         % calculate and set normalization factors from the group of 
wolffd@0:         % input features. These features will be set for the full database
wolffd@0:          
wolffd@0:         final = zeros(numel(features(1).data.stobraw), numel(features));
wolffd@0:         for i = 1:numel(features)
wolffd@0:              if ~isempty(features(i).data.stobraw)
wolffd@0:                 final(:,i) = features(i).data.stobraw';
wolffd@0:              end
wolffd@0:         end
wolffd@0:             
wolffd@0:             if features(1).my_params.stob_norm
wolffd@0:                 if numel(features) == 1
wolffd@0:                     error ('Insert feature array for this method, or set normalisation to 0');
wolffd@0:                 end
wolffd@0: 
wolffd@0:                 % ---
wolffd@0:                 % here, we only need to define the post-normalisation
wolffd@0:                 % ---
wolffd@0:                 [final, pstd] = mapminmax(final,0,1);
wolffd@0:                 common.stobstats.post_norm = pstd;
wolffd@0:                 
wolffd@0:                 % ---
wolffd@0:                 % NOTE: whitening as in slaney?? 
wolffd@0:                 % Would make reading the
wolffd@0:                 % mahal matrices really hard
wolffd@0:                 % ---
wolffd@0:                 
wolffd@0:                 features(1).my_db.set_common(common);
wolffd@0:                 
wolffd@0:             else
wolffd@0:                 
wolffd@0:                 features(1).my_db.set_common([1]);
wolffd@0:             end
wolffd@0:             
wolffd@0:             % save the normalised features straight away!
wolffd@0:             features.finalise(final);
wolffd@0:         end
wolffd@0:         
wolffd@0:         
wolffd@0:         function finalise(features, 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:         % check if features have been finalised already
wolffd@0: 
wolffd@0:         % ---
wolffd@0:         % set feature labelling
wolffd@0:         % ---
wolffd@0:         
wolffd@0:         info = {};
wolffd@0:                 
wolffd@0:         % ---
wolffd@0:         % construct resulting feature vector out of features
wolffd@0:         % ---
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(features) == size(final, 2))
wolffd@0:                 % the features have already been preassembled
wolffd@0:                 
wolffd@0:                 for i = 1:numel(features)
wolffd@0: 
wolffd@0:                     % check for neccesary parameters
wolffd@0:                     if isempty(features(i).my_db.commondb)
wolffd@0: 
wolffd@0:                         error('Define the global transformation first')
wolffd@0:                         return;
wolffd@0:                     end
wolffd@0: 
wolffd@0:                     features(i).data.final.vector = final(:,i);
wolffd@0:                     features(i).data.final.dim = size(final,1);
wolffd@0: 
wolffd@0:                     % fill up info struct and append to feature
wolffd@0:                     features(i).data.final.vector_info.labels = ...
wolffd@0:                         features(i).data.vector_info;
wolffd@0:                 end
wolffd@0:             else
wolffd@0:                 % ---
wolffd@0:                 % if features have been added after gettin gnormalisation
wolffd@0:                 % parameters, ther should be still an option to include
wolffd@0:                 % them
wolffd@0:                 % ---
wolffd@0:                
wolffd@0:                  for i = 1:numel(features)
wolffd@0: 
wolffd@0:                     % check for neccesary parameters
wolffd@0:                     if isempty(features(i).my_db.commondb)
wolffd@0: 
wolffd@0:                         error('Define the global transformation first')
wolffd@0:                         return;
wolffd@0:                     end
wolffd@0:                     
wolffd@0:                     final = features(i).data.stobraw';
wolffd@0:                     
wolffd@0:                     if features(1).my_params.stob_norm == 1
wolffd@0:                                 
wolffd@0:                         [final] = mapminmax('apply', final, features(1).common.stobstats.post_norm);
wolffd@0:                     end
wolffd@0:                     
wolffd@0:                     features(i).data.final.vector = final;
wolffd@0:                     features(i).data.final.dim = size(final,1);
wolffd@0: 
wolffd@0:                     % fill up info struct and append to feature
wolffd@0:                     features(i).data.final.vector_info.labels = ...
wolffd@0:                         features(i).data.vector_info;
wolffd@0:                  end
wolffd@0: 
wolffd@0:             end
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % TODO: Maybe delete more basic features again at this point?
wolffd@0:             % ---
wolffd@0:         end
wolffd@0: 
wolffd@0:         % ---
wolffd@0:         % destructor: do we really want to remove this 
wolffd@0:         % from the database? No, but 
wolffd@0:         % TODO: create marker for unused objects in db, and a cleanup
wolffd@0:         %  function
wolffd@0:         % ---
wolffd@0:         function delete(feature)
wolffd@0:             
wolffd@0:         end
wolffd@0:     end
wolffd@0: end