wolffd@0: function r = mirfeatures(x,varargin)
wolffd@0: %   f = mirfeatures(x) computes a large set of features from one or several
wolffd@0: %       audio files. x can be either the name of an audio file, or the
wolffd@0: %       'Folder' keyword.
wolffd@0: %   mirfeatures(...,'Stat') returns the statistics of the features instead
wolffd@0: %       of the complete features themselves.
wolffd@0: %   mirfeatures(...,'Segment',t) segments the audio sequence at the 
wolffd@0: %       temporal positions indicated in the array t (in s.), and analyzes
wolffd@0: %       each segment separately.
wolffd@0: 
wolffd@0: %(not available yet)
wolffd@0: %   mirfeatures(...,'Filterbank',nc) computes the analysis on each channel
wolffd@0: %       of a filterbank decomposition.
wolffd@0: %       Default value: nc = 5
wolffd@0: %   mirfeatures(...,'Frame',...) 
wolffd@0: %   mirfeatures(...,'Normal')
wolffd@0: %   mirfeatures(...,'Sampling',s)
wolffd@0: %   miraudio options (Extract, ...)
wolffd@0: 
wolffd@0: [stat,nchan,segm,feat] = scanargin(varargin);
wolffd@0: 
wolffd@0: if isa(x,'miraudio') || isa(x,'mirdesign')
wolffd@0:     a = miraudio(x,'Normal'); % normalize with respect to RMS energy 
wolffd@0:                               % in order to consider timbre independently of
wolffd@0:                              % energy
wolffd@0: else
wolffd@0:     a = miraudio('Design','Normal');
wolffd@0: end
wolffd@0: 
wolffd@0: if not(isempty(segm))
wolffd@0:     a = mirsegment(a,segm);
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: 
wolffd@0: % DYNAMICS
wolffd@0: % --------
wolffd@0: 
wolffd@0: r.dynamics.rms = mirrms(a,'Frame');
wolffd@0: % Perceived dynamics: spectral slope?
wolffd@0: 
wolffd@0: % RHYTHM
wolffd@0: % ------
wolffd@0: 
wolffd@0: r.fluctuation = mirstruct;
wolffd@0: r.fluctuation.tmp.f = mirfluctuation(a,'Summary');
wolffd@0: r.fluctuation.peak = mirpeaks(r.fluctuation.tmp.f,'Total',1);%only one?
wolffd@0: r.fluctuation.centroid = mircentroid(r.fluctuation.tmp.f);
wolffd@0: 
wolffd@0: r.rhythm = mirstruct;
wolffd@0: r.rhythm.tmp.onsets = mironsets(a);
wolffd@0: 
wolffd@0: %r.rhythm.eventdensity = ...
wolffd@0: 
wolffd@0: r.rhythm.tempo = mirtempo(r.rhythm.tmp.onsets,'Frame');
wolffd@0: %r.rhythm.pulseclarity = mirpulseclarity(r.tmp.onsets,'Frame');
wolffd@0:     % Should use the second output of mirtempo.
wolffd@0: 
wolffd@0: attacks = mironsets(r.rhythm.tmp.onsets,'Attacks');
wolffd@0: r.rhythm.attack.time = mirattacktime(attacks);
wolffd@0: r.rhythm.attack.slope = mirattackslope(attacks);
wolffd@0: 
wolffd@0: % TIMBRE
wolffd@0: % ------
wolffd@0: 
wolffd@0: f = mirframe(a,.05,.5);
wolffd@0: r.spectral = mirstruct;
wolffd@0: r.spectral.tmp.s = mirspectrum(f);
wolffd@0: %pitch = mirpitch(a,'Frame',.05,.5);
wolffd@0: 
wolffd@0: r.spectral.centroid = mircentroid(r.spectral.tmp.s);
wolffd@0: r.spectral.brightness = mirbrightness(r.spectral.tmp.s);
wolffd@0: r.spectral.spread = mirspread(r.spectral.tmp.s);
wolffd@0: r.spectral.skewness = mirskewness(r.spectral.tmp.s);
wolffd@0: r.spectral.kurtosis = mirkurtosis(r.spectral.tmp.s);
wolffd@0: r.spectral.rolloff95 = mirrolloff(r.spectral.tmp.s,95);
wolffd@0: r.spectral.rolloff85 = mirrolloff(r.spectral.tmp.s,85);
wolffd@0: r.spectral.spectentropy = mirentropy(r.spectral.tmp.s);
wolffd@0: r.spectral.flatness = mirflatness(r.spectral.tmp.s);
wolffd@0: 
wolffd@0: r.spectral.roughness = mirroughness(r.spectral.tmp.s);
wolffd@0: r.spectral.irregularity = mirregularity(r.spectral.tmp.s);
wolffd@0: %r.spectral.inharmonicity = mirinharmonicity(r.spectral.tmp.s,'f0',pitch);
wolffd@0: 
wolffd@0: r.spectral.mfcc = mirmfcc(r.spectral.tmp.s);
wolffd@0: r.spectral.dmfcc = mirmfcc(r.spectral.mfcc,'Delta');
wolffd@0: r.spectral.ddmfcc = mirmfcc(r.spectral.dmfcc,'Delta');
wolffd@0: 
wolffd@0: r.timbre.zerocross = mirzerocross(f);
wolffd@0: r.timbre.lowenergy = mirlowenergy(f);
wolffd@0: r.timbre.spectralflux = mirflux(f);
wolffd@0: 
wolffd@0: % PITCH
wolffd@0: % -----
wolffd@0: 
wolffd@0: r.tonal = mirstruct;
wolffd@0: r.tonal.tmp.chromagram = mirchromagram(a,'Frame','Wrap',0,'Pitch',0);
wolffd@0: r.tonal.chromagram.peak=mirpeaks(r.tonal.tmp.chromagram,'Total',1);
wolffd@0: r.tonal.chromagram.centroid=mircentroid(r.tonal.tmp.chromagram);
wolffd@0: 
wolffd@0: % TONALITY/HARMONY
wolffd@0: % ----------------
wolffd@0: 
wolffd@0: keystrengths = mirkeystrength(r.tonal.tmp.chromagram);
wolffd@0: [k r.tonal.keyclarity] = mirkey(keystrengths,'Total',1);
wolffd@0: %r.tonal.keyclarity = k{2};
wolffd@0: r.tonal.mode = mirmode(keystrengths);
wolffd@0: r.tonal.hcdf = mirhcdf(r.tonal.tmp.chromagram);
wolffd@0: 
wolffd@0: if stat
wolffd@0:     r = mirstat(r);
wolffd@0:     % SHOULD COMPUTE STAT OF CURVES FROM FRAMED_DECOMPOSED HIGH FEATURES
wolffd@0: end
wolffd@0:     
wolffd@0: if not(isa(x,'miraudio')) && not(isa(x,'mirdesign'))
wolffd@0:     r = mireval(r,x);
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: function [stat,nchan,segm,feat] = scanargin(v)
wolffd@0: stat = 0;
wolffd@0: nchan = 1;
wolffd@0: segm = [];
wolffd@0: feat = {};
wolffd@0: i = 1;
wolffd@0: while i <= length(v)
wolffd@0:     arg = v{i};
wolffd@0:     if ischar(arg) && strcmpi(arg,'Filterbank')
wolffd@0:         i = i+1;
wolffd@0:         if i <= length(v)
wolffd@0:             nchan = v{i};
wolffd@0:         else
wolffd@0:             nchan = 10;
wolffd@0:         end
wolffd@0:     elseif ischar(arg) && strcmpi(arg,'Stat')
wolffd@0:         i = i+1;
wolffd@0:         if i <= length(v)
wolffd@0:             stat = v{i};
wolffd@0:         else
wolffd@0:             stat = 1;
wolffd@0:         end
wolffd@0:     elseif ischar(arg) && strcmpi(arg,'Segment')
wolffd@0:         i = i+1;
wolffd@0:         if i <= length(v)
wolffd@0:             segm = v{i};
wolffd@0:         else
wolffd@0:             segm = 1;
wolffd@0:         end
wolffd@0:     else
wolffd@0:         feat{end+1} = arg;
wolffd@0:     end    
wolffd@0:     i = i+1;
wolffd@0: end