function au2=hummingFeaExtract(au, vsdOpt, showPlot)
% hummingFeaExtract: Wave to feature conversion for s/u/v detection
%
%	Usage:
%		au2=hummingFeaExtract(au, showPlot)
%
%	Example:
%		auFile = 'test.wav';
%		auFile='d:/dataSet/childSong/waveFile/2007-音訊處理與辨識/19461108任佳王民/十個印第安人_不詳_0.wav';
%		vsdOpt=vsdOptSet;
%		showPlot=1;
%		au2=hummingFeaExtract(auFile, vsdOpt, showPlot);

% Roger Jang, 20040910, 20070417, 20130201, 20200112

if nargin<1, selfdemo; return; end
if nargin<2, vsdOpt=vsdOptSet; end
if nargin<3, showPlot=0; end
%% Read the given audio file
if ischar(au), au=myAudioRead(au); end		% au is actual the wave file name
%au=waveFormatConvert(au, 8000, 8, 1);		% Format conversion to mono, 8KHz, 8bits
%% Read the given pitch file
pvFile=[au.file(1:end-3), 'pv'];
if ~exist(pvFile, 'file')	% Cannot find the pv file to specify the groundtruth
	warning('Cannot fine the pv file %s!', pvFile);
	feature=[]; frameClass=[]; other=[];
	au2=au;
	au2.feature=feature;
	au2.tOutput=frameClass;
	au2.other=other;
	return
end
%% Frame blocking and featuer extraction
frameSize=vsdOpt.frameSize;
overlap=vsdOpt.overlap;
frameMat=buffer2(au.signal(:, vsdOpt.usedChannel), frameSize, overlap);
frameNum=size(frameMat, 2);
% ====== zero-mean for each frame
frameMat=frameZeroMean(frameMat, vsdOpt.frameZeroMeanOrder);

feature=[];
inputName={};

% ====== Feature: volume
vol = frame2volume(frameMat);
feature=[feature; vecRatio(vol)];
inputName{end+1}='vol';
% ====== Feature: zcr
zcr = frame2zcr(frameMat, 2);
feature=[feature; vecRatio(zcr)];
inputName{end+1}='zcr';
% ====== Feature: frame local max count
frameLocalMaxIndex=localMax(frameMat);
frameLocalMaxCount=sum(frameLocalMaxIndex);
feature=[feature; vecRatio(frameLocalMaxCount)];
inputName{end+1}='frameLocalMaxCount';
% ====== Feature: acf clarity
[acfClarity, acfMat]=frame2clarity(frameMat, au.fs, 'acf');
feature=[feature; vecRatio(acfClarity)];
inputName{end+1}='acfClarity';
% ====== Feature: local max count of acf
acfLocalMaxIndex=localMax(acfMat);
acfLocalMaxCount=sum(acfLocalMaxIndex);
feature=[feature; vecRatio(acfLocalMaxCount)];
inputName{end+1}='acfLocalMaxCount';
% ====== Feature: nsdf clarity
[nsdfClarity, nsdfMat]=frame2clarity(frameMat, au.fs, 'nsdf');
feature=[feature; vecRatio(nsdfClarity)];
inputName{end+1}='nsdfClarity';
% ====== Feature: local max count of nsdf
nsdfLocalMaxIndex=localMax(nsdfMat);
nsdfLocalMaxCount=sum(nsdfLocalMaxIndex);
feature=[feature; vecRatio(nsdfLocalMaxCount)];
inputName{end+1}='nsdfLocalMaxCount';
% ====== Feature: amdf clarity
[amdfClarity, amdfMat]=frame2clarity(frameMat, au.fs, 'amdf');
feature=[feature; vecRatio(amdfClarity)];
inputName{end+1}='amdfClarity';
% ====== Feature: local max count of amdf
amdfLocalMaxIndex=localMax(amdfMat);
amdfLocalMaxCount=sum(amdfLocalMaxIndex);
feature=[feature; vecRatio(amdfLocalMaxCount)];
inputName{end+1}='amdfLocalMaxCount';
% ====== Feature: hod
ashod = frame2ashod(frameMat, 4);
feature=[feature; vecRatio(ashod)];
inputName{end+1}='hod';
% ====== Feature: volume-weighted clarity
%vwClarity=volRatio.*clarityRatio;
%feature=[feature; vwClarity];
%inputName={inputName{:}, 'vwClarity'};

% ====== add annotation
annotation=cell(frameNum);
for i=1:frameNum
	annoStr=sprintf('%s\n%s', au.file, int2str(i));
	annoStr=strrep(annoStr, '\', '/');
	annoStr=strrep(annoStr, '_', '\_');
	annotation{i}=annoStr;
end
other.annotation=annotation;

% ====== Read human-labeled pitch file
frameClass=[];
if exist(pvFile, 'file')
	targetPitch=asciiRead(pvFile);
	if length(targetPitch)>frameNum, targetPitch=targetPitch(1:frameNum); end	% Due to the difference between buffer.m (used before) and buffer2.m (used now)
	frameClass=targetPitch>0;
	frameClass=frameClass+1;	% {0,1} ===> {1,2}
	frameClass=frameClass(:)';
end
other.inputName=inputName;
other.frameTime=frame2sampleIndex(1:frameNum, frameSize, overlap)/au.fs;
other.tPitch=targetPitch;
% === Put everything together for output
au2=au;
au2.feature=feature;
au2.tOutput=frameClass;
au2.other=other;

if showPlot
	plotTitle=strrep(au.file, '\', '/'); plotTitle=strrep(plotTitle, '_', '\_');
	DS.input=feature;
	DS.output=frameClass;
	DS.inputName=other.inputName;
	DS.outputName=vsdOpt.outputName;
	DS.annotation=other.annotation;
	subplot(211);
	dsScatterPlot(DS); title(['Raw data: ', plotTitle]);
	feature2=inputNormalize(feature(1:2, :));
	DS.input=feature2(1:2, :);
	DS.output=frameClass;
	subplot(212);
	dsScatterPlot(DS); title(['Normalized data: ', plotTitle]);
end

% ====== Self demo
function selfdemo
mObj=mFileParse(which(mfilename));
strEval(mObj.example);