annotate Code/Descriptors/yin/private/yink.m @ 4:92ca03a8fa99 tip

Update to ICASSP 2013 benchmark

function r=yink(p,fileinfo) 
% YINK - fundamental frequency estimator
% new version (feb 2003)
%
%

%global jj;
%jj=0;
% process signal a chunk at a time
idx=p.range(1)-1;
totalhops=round((p.range(2)-p.range(1)+1) / p.hop);
r1=nan*zeros(1,totalhops);r2=nan*zeros(1,totalhops);
r3=nan*zeros(1,totalhops);r4=nan*zeros(1,totalhops);
idx2=0+round(p.wsize/2/p.hop);
while (1)
	start = idx+1;
	stop = idx+p.bufsize;
	stop=min(stop, p.range(2));
	xx=sf_wave(fileinfo, [start, stop], []);
% 	if size(xx,1) == 1; xx=xx'; end
	xx=xx(:,1); 			% first channel if multichannel
	[prd,ap0,ap,pwr]=yin_helper(xx,p);
	n=size(prd ,2);
	if (~n) break; end;
	idx=idx+n*p.hop;

	r1(idx2+1:idx2+n)= prd;	
	r2(idx2+1:idx2+n)= ap0;
	r3(idx2+1:idx2+n)= ap;
	r4(idx2+1:idx2+n)= pwr;
	idx2=idx2+n;
end
r.r1=r1; % period estimate
r.r2=r2; % gross aperiodicity measure
r.r3=r3; % fine aperiodicity measure
r.r4=r4; % power
sf_cleanup(fileinfo);
% end of program



% Estimate F0 of a chunk of signal
function [prd,ap0,ap,pwr]=yin_helper(x,p,dd)
smooth=ceil(p.sr/p.lpf);
x=rsmooth(x,smooth); 	% light low-pass smoothing
x=x(smooth:end-smooth+1);
[m,n]=size(x);
maxlag = ceil(p.sr/p.minf0); 	
minlag = floor(p.sr/p.maxf0);
mxlg = maxlag+2; % +2 to improve interp near maxlag


hops=floor((m-mxlg-p.wsize)/p.hop);
prd=zeros(1,hops);
ap0=zeros(1,hops);
ap=zeros(1,hops);
pwr=zeros(1,hops);
if hops<1; return; end

% difference function matrix
dd=zeros(floor((m-mxlg-p.hop)/p.hop),mxlg);
if p.shift == 0		 % windows shift both ways
	lags1=round(mxlg/2) + round((0:mxlg-1)/2); 
	lags2=round(mxlg/2) - round((1:mxlg)/2);
	lags=[lags1; lags2];
elseif p.shift == 1 % one window fixed, other shifts right
	lags=[zeros(1,mxlg); 1:mxlg]; 
elseif p.shift == -1 % one window fixed, other shifts right
	lags=[mxlg-1:-1:0; mxlg*ones(1,mxlg)]; 
else
	error (['unexpected shift flag: ', num2str(p.shift)]);
end
rdiff_inplace(x,x,dd,lags,p.hop);
rsum_inplace(dd,round(p.wsize/p.hop));
dd=dd';
[dd,ddx]=minparabolic(dd); 	% parabolic interpolation near min
cumnorm_inplace(dd);;		% cumulative mean-normalize

% first period estimate
%global jj;
for j=1:hops
	d=dd(:,j);
	if p.relflag
		pd=dftoperiod2(d,[minlag,maxlag],p.thresh); 
	else
		pd=dftoperiod(d,[minlag,maxlag],p.thresh); 
	end
	ap0(j)=d(pd+1);
	prd(j)=pd;
end

% replace each estimate by best estimate in range
range = 2*round(maxlag/p.hop);
if hops>1; prd=prd(mininrange(ap0,range*ones(1,hops))); end
%prd=prd(mininrange(ap0,prd)); 	


% refine estimate by constraining search to vicinity of best local estimate
margin1=0.6; 		
margin2=1.8; 		
for j=1:hops
	d=dd(:,j);
	dx=ddx(:,j);
	pd=prd(j);
	lo=floor(pd*margin1); lo=max(minlag,lo);
	hi=ceil(pd*margin2); hi=min(maxlag,hi);
	pd=dftoperiod(d,[lo,hi],0); 
	ap0(j)=d(pd+1);
	pd=pd+dx(pd+1)+1;	% fine tune based on parabolic interpolation
	prd(j)=pd;

	% power estimates
	idx=(j-1)*p.hop;
	k=(1:ceil(pd))';
	x1=x(k+idx);
	x2=k+idx+pd-1;
	interp_inplace(x,x2);
	x3=x2-x1;
	x4=x2+x1;
	x1=x1.^2; rsum_inplace(x1,pd);
	x3=x3.^2; rsum_inplace(x3,pd);
	x4=x4.^2; rsum_inplace(x4,pd);
	
	x1=x1(1)/pd;
	x2=x2(1)/pd;
	x3=x3(1)/pd;
	x4=x4(1)/pd;
	% total power
	pwr(j)=x1;

	% fine aperiodicity
	ap(j)=(eps+x3)/(eps+(x3+x4)); % accurate, only for valid min

	%ap(j)
	%plot(min(1, d)); pause

	prd(j)=pd;
end



%cumulative mean-normalize 
function y=cumnorm(x)
[m,n]=size(x);
y = cumsum(x);
y = (y)./ (eps+repmat((1:m)',1,n)); % cumulative mean
y = (eps+x) ./  (eps+y);