wolffd@0: classdef MTTMixedFeatureSon < MTTAudioFeature & handle
wolffd@0:     % ---
wolffd@0:     % This Class contains a wrapper for sons feature extraction
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: 741 $',  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:             'son_filename','features_rbm_50x1010', ...
wolffd@0:             'son_conf', '' ...
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 = MTTMixedFeatureSon(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 comparison_ids;
wolffd@0:             global sonfeatbase;
wolffd@0:             global db_MTTClip;
wolffd@0:             
wolffd@0:             if isempty(sonfeatbase);
wolffd@0:                 sonfeatbase = load(feature.my_params.son_filename);
wolffd@0:             end
wolffd@0:             %
wolffd@0:             
wolffd@0:             % ---
wolffd@0:             % note: this should reference clip.my_db
wolffd@0:             % get the actual clip id
wolffd@0:             idx = db_MTTClip.comparison_ids(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:             if isfield(sonfeatbase,'nfvec')
wolffd@0:                 rawf = sonfeatbase.nfvec;
wolffd@0:             elseif isfield(sonfeatbase,'sonfeatures')
wolffd@0:                 rawf = sonfeatbase.sonfeatures';
wolffd@0:             end
wolffd@0:             
wolffd@0:             if idx <= size(rawf,2)
wolffd@0:                 
wolffd@0:                  % get the vector from loaded data
wolffd@0:                 data.sonraw = rawf(:,idx);
wolffd@0:                 
wolffd@0:             else
wolffd@0:                 % ---
wolffd@0:                 % CAVE: Clip indices outside 
wolffd@0:                 % the range  of the supplied mat file
wolffd@0:                 % are filled up with zeros
wolffd@0:                 % ---
wolffd@0:                 data.sonraw = zeros(size(rawf,1),1);
wolffd@0:             end
wolffd@0:             
wolffd@0:            
wolffd@0:             
wolffd@0:             data.vector_info = {'Sonfeat'};
wolffd@0:             % padd further info struct
wolffd@0:             data.vector_info(end+1:size(data.sonraw,1)) =...
wolffd@0:                 cell(size(data.sonraw,1) - 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 = 'MTTMixedFeatureSon';
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(size(features(1).data.sonraw,1), numel(features));
wolffd@0:             for i = 1:numel(features)
wolffd@0:                  if ~isempty(features(i).data.sonraw)
wolffd@0:                     final(:,i) = features(i).data.sonraw;
wolffd@0:                  end
wolffd@0:             end
wolffd@0:             
wolffd@0:             % set common to 1 to tell normalisation is done
wolffd@0:             features(1).my_db.set_common([1]);
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 = zeros(numel(features(1).data.sonraw), numel(features));
wolffd@0:                     for i = 1:numel(features)
wolffd@0:                          if ~isempty(features(i).data.sonraw)
wolffd@0:                             final(:,i) = features(i).data.sonraw;
wolffd@0:                          end
wolffd@0:                     end
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