close all; clear all;
load mir-st010.mat
%% Process a given song
songId=6;
note=ds(songId).noteGt;
pv.pitch=ds(songId).fea.vocal_pitch;
pv.time=ds(songId).fea.time;
pv.name='隱形的翅膀';
%timeInterval=[27 33.5];
%pv=pvSubsequence(pv, timeInterval);
%pv.name='「隱形的翅膀」的第一句';
%figure; pvPlot(pv);
%% Note
%note=noteSubsequence(note, timeInterval);
%figure; notePlot(note);
%% Note to PV
myTic0=tic;
stdScale=1:100;
stdNoteCount=length(stdScale);
pv2=note2pv(note, 8000/256);
toc(myTic0)
%% Compute transition probability
myTic0=tic;
pitch2=pv2.pitch;
pitch2(pitch2==0)=1;	% For easy indexing
pvCount=length(pitch2);
transProb=zeros(stdNoteCount);
for i=1:pvCount-1
	pos1=pitch2(i);
	pos2=pitch2(i+1);
	transProb(pos1, pos2)=transProb(pos1, pos2)+1;
end
toc(myTic0)
temp=transProb; temp(temp==0)=nan;
figure; imagesc(temp); axis image; colorbar
%% Compute state probablility
myTic0=tic;
observed=cell(stdNoteCount);
for i=1:stdNoteCount
	observed{i}=[];
end
for i=1:stdNoteCount
	index=find(pitch2==i);
	observed{i}=[observed{i}, pv.pitch(index)];
end
observationCount=zeros(1, stdNoteCount);
for i=1:stdNoteCount
	observationCount(i)=length(observed{i});
end
toc(myTic0)
figure; bar(observationCount);
xlabel('Semitones'); ylabel('Pitch count');
%%
figure; plot(observed{61});
%%
figure; plot(observed{63});