diff toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miremotion/miremotion.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miremotion/miremotion.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,470 @@
+function varargout = miremotion(orig,varargin)
+% Predicts emotion along three dimensions and five basic concepts.
+% Optional parameters:
+%   miremotion(...,'Dimensions',0) excludes all three dimensions.
+%   miremotion(...,'Dimensions',3) includes all three dimensions (default).
+%   miremotion(...,'Activity') includes the 'Activity' dimension. 
+%   miremotion(...,'Valence') includes the 'Valence' dimension. 
+%   miremotion(...,'Tension') includes the 'Tension' dimension. 
+%   miremotion(...,'Dimensions',2) includes 'Activity' and 'Valence'.
+%   miremotion(...,'Arousal') includes 'Activity' and 'Tension'.
+%   miremotion(...,'Concepts',0) excludes all five concepts.
+%   miremotion(...,'Concepts') includes all five concepts (default).
+%   miremotion(...,'Happy') includes the 'Happy' concept.
+%   miremotion(...,'Sad') includes the 'Sad' concept.
+%   miremotion(...,'Tender') includes the 'Tender' concept.
+%   miremotion(...,'Anger') includes the 'Anger' concept.
+%   miremotion(...,'Fear') includes the 'Fear' concept.
+%   miremotion(...,'Frame',...) predict emotion frame by frame.
+%
+% Selection of features and coefficients are taken from a study: 
+%         Eerola, T., Lartillot, O., and Toiviainen, P. 
+%            (2009). Prediction of multidimensional emotional ratings in 
+%            music from audio using multivariate regression models. 
+%            In Proceedings of 10th International Conference on Music Information Retrieval 
+%            (ISMIR 2009), pages 621-626.
+%
+% The implemented models are based on multiple linear regression with 5 best
+% predictors (MLR option in the paper). The box-cox transformations have now been 
+% removed until the normalization values have been established with a large sample of music.
+% 
+% TODO: Revision of coefficients to (a) force the output range between 0 - 1 and 
+%    (b) to be based on alternative models and materials (training sets). 
+%
+% Updated 03.05.2010 TE
+%
+        frame.key = 'Frame';
+        frame.type = 'Integer';
+        frame.number = 2;
+        frame.default = [0 0];
+        frame.keydefault = [1 1];
+    option.frame = frame;
+
+        dim.key = 'Dimensions';
+        dim.type = 'Integer';
+        dim.default = NaN;
+        dim.keydefault = 3;
+    option.dim = dim;
+
+        activity.key = 'Activity';
+        activity.type = 'Boolean';
+        activity.default = NaN;
+    option.activity = activity;
+
+        valence.key = 'Valence';
+        valence.type = 'Boolean';
+        valence.default = NaN;
+    option.valence = valence;
+
+        tension.key = 'Tension';
+        tension.type = 'Boolean';
+        tension.default = NaN;
+    option.tension = tension;
+    
+        arousal.key = 'Arousal';
+        arousal.type = 'Boolean';
+        arousal.default = NaN;
+    option.arousal = arousal;
+    
+        concepts.key = 'Concepts';
+        concepts.type = 'Boolean';
+        concepts.default = NaN;
+    option.concepts = concepts;
+
+        happy.key = 'Happy';
+        happy.type = 'Boolean';
+        happy.default = NaN;
+    option.happy = happy;
+
+        sad.key = 'Sad';
+        sad.type = 'Boolean';
+        sad.default = NaN;
+    option.sad = sad;
+
+        tender.key = 'Tender';
+        tender.type = 'Boolean';
+        tender.default = NaN;
+    option.tender = tender;
+
+        anger.key = 'Anger';
+        anger.type = 'Boolean';
+        anger.default = NaN;
+    option.anger = anger;
+
+        fear.key = 'Fear';
+        fear.type = 'Boolean';
+        fear.default = NaN;
+    option.fear = fear;
+    
+specif.option = option;
+specif.defaultframelength = 1;
+%specif.defaultframehop = .5;
+
+specif.combinechunk = {'Average',@nothing};
+specif.extensive = 1;
+
+varargout = mirfunction(@miremotion,orig,varargin,nargout,specif,@init,@main);
+
+
+%%
+function [x type] = init(x,option)
+
+option = process(option);
+
+if option.frame.length.val
+    hop = option.frame.hop.val;
+    if strcmpi(option.frame.hop.unit,'Hz')
+        hop = 1/hop;
+        option.frame.hop.unit = 's';
+    end
+    if strcmpi(option.frame.hop.unit,'s')
+        hop = hop*get(x,'Sampling');
+    end
+    if strcmpi(option.frame.hop.unit,'%')
+        hop = hop/100;
+        option.frame.hop.unit = '/1';
+    end
+    if strcmpi(option.frame.hop.unit,'/1')
+        hop = hop*option.frame.length.val;
+    end
+    frames = 0:hop:1000000;
+    x = mirsegment(x,[frames;frames+option.frame.length.val]);
+elseif isa(x,'mirdesign')
+    x = set(x,'NoChunk',1);
+end
+rm = mirrms(x,'Frame',.046,.5);
+
+le = 0; %mirlowenergy(rm,'ASR');
+
+o = mironsets(x,'Filterbank',15,'Contrast',0.1);
+at = mirattacktime(o);
+as = 0; %mirattackslope(o);
+ed = 0; %mireventdensity(o,'Option1');
+
+fl = mirfluctuation(x,'Summary');
+fp = mirpeaks(fl,'Total',1);
+fc = 0; %mircentroid(fl);
+
+tp = 0; %mirtempo(x,'Frame',2,.5,'Autocor','Spectrum');
+pc = mirpulseclarity(x,'Frame',2,.5); %%%%%%%%%%% Why 'Frame'?? 
+
+s = mirspectrum(x,'Frame',.046,.5);
+sc = mircentroid(s);
+ss = mirspread(s);
+sr = mirroughness(s);
+
+%ps = mirpitch(x,'Frame',.046,.5,'Tolonen');
+
+c = mirchromagram(x,'Frame','Wrap',0,'Pitch',0);    %%%%%%%%%%%%%%%%%%%% Previous frame size was too small.
+cp = mirpeaks(c,'Total',1);
+ps = 0;%cp;
+ks = mirkeystrength(c);
+[k kc] = mirkey(ks);
+mo = mirmode(ks);
+hc = mirhcdf(c);
+
+se = mirentropy(mirspectrum(x,'Collapsed','Min',40,'Smooth',70,'Frame',1.5,.5)); %%%%%%%%% Why 'Frame'?? 
+
+ns = mirnovelty(mirspectrum(x,'Frame',.1,.5,'Max',5000),'Normal',0);
+nt = mirnovelty(mirchromagram(x,'Frame',.2,.25),'Normal',0);    %%%%%%%%%%%%%%%%%%%% Previous frame size was too small.
+nr = mirnovelty(mirchromagram(x,'Frame',.2,.25,'Wrap',0),'Normal',0);   %%%%%%%%%%%%%%%%%%%% Previous frame size was too small.
+
+
+
+x = {rm,le, at,as,ed, fp,fc, tp,pc, sc,ss,sr, ps, cp,kc,mo,hc, se, ns,nt,nr};
+
+type = {'miremotion','mirscalar','mirscalar',...
+                     'mirscalar','mirscalar','mirscalar',...
+                     'mirspectrum','mirscalar',...
+                     'mirscalar','mirscalar',...
+                     'mirscalar','mirscalar','mirscalar',...
+                     'mirscalar',...
+                     'mirchromagram','mirscalar','mirscalar','mirscalar',...
+                     'mirscalar',...
+                     'mirscalar','mirscalar','mirscalar'};
+                 
+
+%%
+function e = main(x,option,postoption)
+
+warning('WARNING IN MIRENOTION: The current model of miremotion is not correctly calibrated with this version of MIRtoolbox (but with version 1.3 only).');
+
+option = process(option);
+rm = get(x{1},'Data');
+%le = get(x{2},'Data');
+at = get(x{3},'Data');
+%as = get(x{4},'Data');
+%ed = get(x{5},'Data');
+%fpp = get(x{6},'PeakPosUnit');
+fpv = get(x{6},'PeakVal');
+%fc = get(x{7},'Data');
+%tp = get(x{8},'Data');
+pc = get(x{9},'Data');
+sc = get(x{10},'Data');
+ss = get(x{11},'Data');
+rg = get(x{12},'Data');
+%ps = get(x{13},'PeakPosUnit');
+cp = get(x{14},'PeakPosUnit');
+kc = get(x{15},'Data');
+mo = get(x{16},'Data');
+hc = get(x{17},'Data');
+se = get(x{18},'Data');
+ns = get(x{19},'Data');
+nt = get(x{20},'Data');
+nr = get(x{21},'Data');
+
+
+e.dim = {};
+e.dimdata = mircompute(@initialise,rm);
+if option.activity == 1
+    [e.dimdata e.activity_fact] = mircompute(@activity,e.dimdata,rm,fpv,sc,ss,se);
+    e.dim = [e.dim,'Activity'];
+else   
+    e.activity_fact = NaN;
+end
+if option.valence == 1
+    [e.dimdata e.valence_fact] = mircompute(@valence,e.dimdata,rm,fpv,kc,mo,ns);
+    e.dim = [e.dim,'Valence'];
+else
+    e.valence_fact = NaN;
+end
+if option.tension == 1
+    [e.dimdata e.tension_fact] = mircompute(@tension,e.dimdata,rm,fpv,kc,hc,nr);
+    e.dim = [e.dim,'Tension'];
+else
+    e.tension_fact = NaN;
+end
+
+e.class = {};
+e.classdata = mircompute(@initialise,rm);
+if option.happy == 1
+    [e.classdata e.happy_fact] = mircompute(@happy,e.classdata,fpv,ss,cp,kc,mo);
+    e.class = [e.class,'Happy'];
+else
+    e.happy_fact = NaN;
+end
+if option.sad == 1
+    [e.classdata e.sad_fact] = mircompute(@sad,e.classdata,ss,cp,mo,hc,nt);
+    e.class = [e.class,'Sad'];
+else
+    e.sad_fact = NaN;
+end
+if option.tender == 1
+    [e.classdata e.tender_fact] = mircompute(@tender,e.classdata,sc,rg,kc,hc,ns);
+    e.class = [e.class,'Tender'];
+else
+    e.tender_fact = NaN;
+end
+if option.anger == 1
+    [e.classdata e.anger_fact] = mircompute(@anger,e.classdata,rg,kc,se,nr);
+    e.class = [e.class,'Anger'];
+else
+    e.anger_fact = NaN;
+end
+if option.fear == 1
+    [e.classdata e.fear_fact] = mircompute(@fear,e.classdata,rm,at,fpv,kc,mo);
+    e.class = [e.class,'Fear'];
+else
+    e.fear_fact = NaN;
+end
+
+e = class(e,'miremotion',mirdata(x{1}));
+e = purgedata(e);
+fp = mircompute(@noframe,get(x{1},'FramePos'));
+e = set(e,'Title','Emotion','Abs','emotions','Ord','magnitude','FramePos',fp);
+      
+%%      
+function option = process(option)
+if option.arousal==1
+    option.activity = 1;
+    option.tension = 1;
+    if isnan(option.dim)
+        option.dim = 0; 
+    end
+end
+if option.activity==1 || option.valence==1 || option.tension==1
+    if isnan(option.activity)
+        option.activity = 0;
+    end
+    if isnan(option.valence)
+        option.valence = 0;
+    end
+    if isnan(option.tension)
+        option.tension = 0;
+    end
+    if isnan(option.concepts)
+        option.concepts = 0;
+    end
+end
+if not(isnan(option.dim)) && option.dim
+    if isnan(option.concepts)
+        option.concepts = 0;
+    end
+end
+if not(isnan(option.concepts)) && option.concepts
+    if isnan(option.dim)
+        option.dim = 0;
+    end
+end
+if not(isnan(option.dim))
+    switch option.dim 
+        case 0
+            if isnan(option.activity)
+                option.activity = 0;
+            end
+            if isnan(option.valence)
+                option.valence = 0;
+            end
+            if isnan(option.tension)
+                option.tension = 0;
+            end
+        case 2
+            option.activity = 1;
+            option.valence = 1;
+            if isnan(option.tension)
+                option.tension = 0;
+            end
+        case 3
+            option.activity = 1;
+            option.valence = 1;
+            option.tension = 1;
+    end
+end
+if isnan(option.activity)
+    option.activity = 1;
+end
+if isnan(option.valence)
+    option.valence = 1;
+end
+if isnan(option.tension)
+    option.tension = 1;
+end
+if isnan(option.concepts)
+    option.concepts = 1;
+end
+if option.concepts
+    option.happy = 1;
+    option.sad = 1;
+    option.tender = 1;
+    option.anger = 1;
+    option.fear = 1;
+end
+if option.happy==1 || option.sad==1 || option.tender==1 ...
+        || option.anger==1 || option.fear==1
+    if isnan(option.happy)
+        option.happy = 0;
+    end
+    if isnan(option.sad)
+        option.sad = 0;
+    end
+    if isnan(option.tender)
+        option.tender = 0;
+    end
+    if isnan(option.anger)
+        option.anger = 0;
+    end
+    if isnan(option.fear)
+        option.fear = 0;
+    end
+end
+
+
+%%
+function e = initialise(rm)
+e = [];
+
+      
+function e = activity(e,rm,fpv,sc,ss,se) % without the box-cox transformation, revised coefficients
+af = zeros(5,1);
+af(1) = 0.6664* ((mean(rm) - 0.0559)/0.0337); % 
+af(2) =  0.6099 * ((mean(fpv{1}) - 13270.1836)/10790.655);
+af(3) = 0.4486*((mean(cell2mat(sc)) - 1677.7)./570.34);
+af(4) = -0.4639*((mean(cell2mat(ss)) - 250.5574)./205.3147);
+af(5) = 0.7056*((mean(se) - 0.954)./0.0258);
+af(isnan(af)) = [];
+e(end+1,:) = sum(af)+5.4861;
+e = {e af};
+
+function e = valence(e,rm,fpv,kc,mo,ns) % without the box-cox transformation, revised coefficients
+vf = zeros(5,1);
+vf(1) = -0.3161 * ((std(rm) - 0.024254)./0.015667);
+vf(2) =  0.6099 * ((mean(fpv{1}) - 13270.1836)/10790.655);
+vf(3) = 0.8802 * ((mean(kc) - 0.5123)./0.091953);
+vf(4) = 0.4565 * ((mean(mo) - -0.0019958)./0.048664);
+ns(isnan(ns)) = [];
+vf(5) = 0.4015 * ((mean(ns) - 131.9503)./47.6463);
+vf(isnan(vf)) = [];
+e(end+1,:) = sum(vf)+5.2749;
+e = {e vf};
+
+function e = tension(e,rm,fpv,kc,hc,nr)
+tf = zeros(5,1);
+tf(1) = 0.5382 * ((std(rm) - 0.024254)./0.015667);
+tf(2) =  -0.5406 * ((mean(fpv{1}) - 13270.1836)/10790.655);
+tf(3) = -0.6808 * ((mean(kc) - 0.5124)./0.092);
+tf(4) = 0.8629 * ((mean(hc) - 0.2962)./0.0459);
+tf(5) = -0.5958 * ((mean(nr) - 71.8426)./46.9246);
+tf(isnan(tf)) = [];
+e(end+1,:) = sum(tf)+5.4679;
+e = {e tf};
+
+
+% BASIC EMOTION PREDICTORS
+
+function e = happy(e,fpv,ss,cp,kc,mo)
+ha_f = zeros(5,1);
+ha_f(1) = 0.7438*((mean(cell2mat(fpv)) - 13270.1836)./10790.655);
+ha_f(2) = -0.3965*((mean(cell2mat(ss)) - 250.5574)./205.3147);
+ha_f(3) = 0.4047*((std(cell2mat(cp)) - 8.5321)./2.5899);
+ha_f(4) = 0.7780*((mean(kc) - 0.5124)./0.092);
+ha_f(5) = 0.6220*((mean(mo) - -0.002)./0.0487);
+ha_f(isnan(ha_f)) = [];
+e(end+1,:) = sum(ha_f)+2.6166;
+e = {e ha_f};
+
+function e = sad(e,ss,cp,mo,hc,nt)
+sa_f = zeros(5,1);
+sa_f(1) = 0.4324*((mean(cell2mat(ss)) - 250.5574)./205.3147);
+sa_f(2) = -0.3137*((std(cell2mat(cp)) - 8.5321)./2.5899);
+sa_f(3) = -0.5201*((mean(mo) - -0.0020)./0.0487);
+sa_f(4) = -0.6017*((mean(hc) - 0.2962)./0.0459);
+sa_f(5) = 0.4493*((mean(nt) - 42.2022)./36.7782);
+sa_f(isnan(sa_f)) = [];
+e(end+1,:) = sum(sa_f)+2.9756;
+e = {e sa_f};
+
+function e = tender(e,sc,rg,kc,hc,ns)
+te_f = zeros(5,1);
+te_f(1) = -0.2709*((mean(cell2mat(sc)) - 1677.7106)./570.3432);
+te_f(2) = -0.4904*((std(rg) - 85.9387)./106.0767);
+te_f(3) = 0.5192*((mean(kc) - 0.5124)./0.0920);
+te_f(4) = -0.3995*((mean(hc) - 0.2962)./0.0459);
+te_f(5) = 0.3391*((mean(ns) - 131.9503)./47.6463);
+te_f(isnan(te_f)) = [];
+e(end+1,:) = sum(te_f)+2.9756;
+e = {e te_f};
+
+function e = anger(e,rg,kc,se,nr) % 
+an_f = zeros(5,1);
+%an_f(1) = -0.2353*((mean(pc) - 0.1462)./.1113);
+an_f(2) = 0.5517*((mean(rg) - 85.9387)./106.0767);
+an_f(3) = -.5802*((mean(kc) - 0.5124)./0.092);
+an_f(4) = .2821*((mean(se) - 0.954)./0.0258);
+an_f(5) = -.2971*((mean(nr) - 71.8426)./46.9246);
+an_f(isnan(an_f)) = [];
+e(end+1,:) = sum(an_f)+1.9767;
+e = {e an_f};
+
+function e = fear(e,rm,at,fpv,kc,mo)
+fe_f = zeros(5,1);
+fe_f(1) = 0.4069*((std(rm) - 0.0243)./0.0157);
+fe_f(2) = -0.6388*((mean(at) - 0.0707)./0.015689218536423);
+fe_f(3) = -0.2538*((mean(cell2mat(fpv)) - 13270.1836)./10790.655);
+fe_f(4) = -0.9860*((mean(kc) - 0.5124)./0.0920);
+fe_f(5) = -0.3144*((mean(mo) - -0.0019958)./0.048663550639094);
+fe_f(isnan(fe_f)) = [];
+e(end+1,:) = sum(fe_f)+2.7847;
+e = {e fe_f};
+
+function fp = noframe(fp)
+fp = [fp(1);fp(end)];
\ No newline at end of file