wolffd@0: % --
wolffd@0: % This class loads and hanles the aufdio features included with the MTT
wolffd@0: % Library
wolffd@0: % --- 
wolffd@0: 
wolffd@0: 
wolffd@0: classdef AudioFeatureRAW < MTTAudioFeature & handle
wolffd@0:     
wolffd@0:     
wolffd@0:     properties(Constant = true)
wolffd@0:         
wolffd@0:     end
wolffd@0:     
wolffd@0:     properties
wolffd@0:         % ---
wolffd@0:         % Set default parameters
wolffd@0:         % ---
wolffd@0:         my_params = struct([]);
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 = AudioFeatureRAW(varargin)
wolffd@0: 
wolffd@0:            feature = feature@MTTAudioFeature(varargin{:});
wolffd@0: 
wolffd@0:         end
wolffd@0:         
wolffd@0:         function [a1, a2, a3] = visualise(feature)
wolffd@0:         % ---
wolffd@0:         % plots the different data types collected in this feature
wolffd@0:         % ---
wolffd@0:             for i = 1:numel(feature)
wolffd@0:                 clip = feature(i).data.info.owner;
wolffd@0:                 
wolffd@0:                 % ---
wolffd@0:                 % labels for chroma data
wolffd@0:                 % ---
wolffd@0:                 chroma_labels = {'c', 'c#', 'd','d#', 'e', 'f','f#', 'g','g#', 'a','a#', 'b'};
wolffd@0:                 mode_labels = {'minor', 'major'};
wolffd@0:                 
wolffd@0:                 % hange labels to reflect detected mode
wolffd@0:                 chroma_labels{feature(i).data.key + 1} = ...
wolffd@0:                     sprintf('(%s) %s',mode_labels{feature(i).data.mode + 1}, chroma_labels{feature(i).data.key + 1});
wolffd@0:                 
wolffd@0:               
wolffd@0:                 secs = [feature(i).data.sections_start; feature(i).data.sections_duration];
wolffd@0:                 
wolffd@0:                 h = figure;
wolffd@0:                 
wolffd@0:                 % number of subplots
wolffd@0:                 n = 3;
wolffd@0:                     
wolffd@0:                 % ---
wolffd@0:                 % chroma feature display
wolffd@0:                 % ---
wolffd@0:                 subplot(n,1,1);
wolffd@0:                 
wolffd@0:                 % get segment times and fix for same lengths for all plots
wolffd@0:                 % ---
wolffd@0:                 % NOTE: Last segment will appear longer
wolffd@0:                 % ---
wolffd@0:                 segments = [feature(i).data.segments_start];
wolffd@0:                 segments(end) = feature(i).data.duration;
wolffd@0:                 
wolffd@0:                 % display chroma vectors
wolffd@0:                 uimagesc(segments, 0:11, [feature(i).data.segments_pitches]);
wolffd@0:                 
wolffd@0:                 set(gca,'YTick',[0:11], 'YTickLabel', chroma_labels);
wolffd@0:                 
wolffd@0:                 axis xy
wolffd@0:                 colormap(hot)
wolffd@0:                 %colorbar;
wolffd@0:                 ylabel('chroma class');
wolffd@0:                 title(sprintf('clip %d: %s by %s, chromagram', ...
wolffd@0:                     clip.id, clip.title(),clip.artist()));
wolffd@0:                 
wolffd@0:                 % added sections
wolffd@0:                 axis([0 feature(i).data.duration -1 11.5]);
wolffd@0:                 hl = line([secs(1,:); sum(secs,1)],ones(2, size(secs,2)) * -0.8);
wolffd@0:                 set(hl,'LineWidth',6);
wolffd@0: 
wolffd@0:                 a1 = gca;
wolffd@0: 
wolffd@0:                 % ---
wolffd@0:                 % mfcc feature display
wolffd@0:                 %
wolffd@0:                 % NOTE: the first position of timbre is reduced in energy,
wolffd@0:                 % as this seems to introduce some corruption in lots of data
wolffd@0:                 % ---
wolffd@0:                 timbre = feature(i).data.segments_timbre(:,1);
wolffd@0:                 timbre = timbre/ max(max(abs(timbre))) * ...
wolffd@0:                     mean( mean( abs( feature(i).data.segments_timbre(:,2:min(end,5)))));
wolffd@0:                 
wolffd@0:                 subplot(n,1,2);
wolffd@0:                 uimagesc(segments, 0:11, [timbre feature(i).data.segments_timbre(:,2:end)]);
wolffd@0: 
wolffd@0:                 axis xy
wolffd@0:                 %colorbar;
wolffd@0:                 xlabel('time[s]');
wolffd@0:                 ylabel('mfcc coeff');
wolffd@0:                 title(sprintf('mfcc timbre features'));
wolffd@0:                    
wolffd@0:                 a2 = gca;
wolffd@0:                 
wolffd@0:                 % ---
wolffd@0:                 % beats and sections
wolffd@0:                 % ---
wolffd@0: 
wolffd@0:                 subplot(n,1,3);
wolffd@0:                 axis([0 feature(i).data.duration -0.6 1.2]);
wolffd@0: 
wolffd@0:                 hl = line([0 feature(i).data.duration],[0 0]);
wolffd@0:                 set(hl, 'Color','g');
wolffd@0:                 
wolffd@0:                 a3 = gca;
wolffd@0:                 
wolffd@0:                 if ~isfield(feature.data,'bars');
wolffd@0:                     return;
wolffd@0:                 end
wolffd@0:                 
wolffd@0:                 beats = feature.data.beats;
wolffd@0:                 bars = feature.data.bars;
wolffd@0:                 tatums = feature.data.tatums;
wolffd@0:                 % tatums
wolffd@0:                 hl = line([tatums(1,:); tatums(1,:)],[ones(1, size(tatums,2)) * -0.2; max(-0.1,tatums(2,:))]);
wolffd@0:                 set(hl,'LineWidth',1);
wolffd@0:                 set(hl, 'Color','k');
wolffd@0:                 
wolffd@0:                 % beats
wolffd@0:                 hl = line([beats(1,:); beats(1,:)],[ones(1, size(beats,2)) * -0.4; max(-0.1,beats(2,:))]);
wolffd@0:                 set(hl,'LineWidth',2);
wolffd@0:                 set(hl, 'Color','b');
wolffd@0: 
wolffd@0:                 % bars
wolffd@0:                 hl = line([bars(1,:); bars(1,:)],[ones(1, size(bars,2)) * -0.5; max(-0.1,bars(2,:))]);
wolffd@0:                 set(hl,'LineWidth',4);
wolffd@0:                 set(hl, 'Color','r');
wolffd@0: 
wolffd@0:                 % sections
wolffd@0:                 hl = line([secs(1,:); sum(secs,1)],ones(2, size(secs,2)) * -0.5);
wolffd@0:                 set(hl,'LineWidth',6);
wolffd@0:                 
wolffd@0:                 ylabel('confidence');                
wolffd@0:                 title(sprintf('rhythmic features @%3.1f BPM, %d/4 meter',...
wolffd@0:                     feature(i).data.tempo, feature(i).data.timeSignature));
wolffd@0:                 
wolffd@0:             end
wolffd@0:         end
wolffd@0:     end
wolffd@0: 
wolffd@0: end