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Deleting matlab files

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git-svn-id: http://webrtc.googlecode.com/svn/trunk@646 4adac7df-926f-26a2-2b94-8c16560cd09d
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turajs@google.com
2011-09-23 21:47:25 +00:00
parent 610f478705
commit 13335ccd7a
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src/modules/audio_processing/aec/main/matlab/fullaec.m
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% Partitioned block frequency domain adaptive filtering NLMS and
% standard time-domain sample-based NLMS
%fid=fopen('aecFar-samsung.pcm', 'rb'); % Load far end
fid=fopen('aecFar.pcm', 'rb'); % Load far end
%fid=fopen(farFile, 'rb'); % Load far end
rrin=fread(fid,inf,'int16');
fclose(fid);
%rrin=loadsl('data/far_me2.pcm'); % Load far end
%fid=fopen('aecNear-samsung.pcm', 'rb'); % Load near end
fid=fopen('aecNear.pcm', 'rb'); % Load near end
%fid=fopen(nearFile, 'rb'); % Load near end
ssin=fread(fid,inf,'int16');
%ssin = [zeros(1024,1) ; ssin(1:end-1024)];
fclose(fid);
rand('state',13);
fs=16000;
mult=fs/8000;
%rrin=rrin(fs*0+1:round(fs*120));
%ssin=ssin(fs*0+1:round(fs*120));
if fs == 8000
cohRange = 2:3;
elseif fs==16000
cohRange = 2;
end
% Flags
NLPon=1; % NLP
CNon=1; % Comfort noise
PLTon=1; % Plotting
M = 16; % Number of partitions
N = 64; % Partition length
L = M*N; % Filter length
if fs == 8000
mufb = 0.6;
else
mufb = 0.5;
end
%mufb=1;
VADtd=48;
alp = 0.1; % Power estimation factor alc = 0.1; % Coherence estimation factor
beta = 0.9; % Plotting factor
%% Changed a little %%
step = 0.3;%0.1875; % Downward step size
%%
if fs == 8000
threshold=2e-6; % DTrob threshold
else
%threshold=0.7e-6;
threshold=1.5e-6; end
if fs == 8000
echoBandRange = ceil(300*2/fs*N):floor(1800*2/fs*N);
%echoBandRange = ceil(1500*2/fs*N):floor(2500*2/fs*N);
else
echoBandRange = ceil(300*2/fs*N):floor(1800*2/fs*N);
%echoBandRange = ceil(300*2/fs*N):floor(1800*2/fs*N);
end
%echoBandRange = ceil(1600*2/fs*N):floor(1900*2/fs*N);
%echoBandRange = ceil(2000*2/fs*N):floor(4000*2/fs*N);
suppState = 1;
transCtr = 0;
Nt=1;
vt=1;
ramp = 1.0003; % Upward ramp
rampd = 0.999; % Downward ramp
cvt = 20; % Subband VAD threshold;
nnthres = 20; % Noise threshold
shh=logspace(-1.3,-2.2,N+1)';
sh=[shh;flipud(shh(2:end-1))]; % Suppression profile
len=length(ssin);
w=zeros(L,1); % Sample-based TD NLMS
WFb=zeros(N+1,M); % Block-based FD NLMS
WFbOld=zeros(N+1,M); % Block-based FD NLMS
YFb=zeros(N+1,M);
erfb=zeros(len,1);
erfb3=zeros(len,1);
ercn=zeros(len,1);
zm=zeros(N,1);
XFm=zeros(N+1,M);
YFm=zeros(N+1,M);
pn0=10*ones(N+1,1);
pn=zeros(N+1,1);
NN=len;
Nb=floor(NN/N)-M;
erifb=zeros(Nb+1,1)+0.1;
erifb3=zeros(Nb+1,1)+0.1;
ericn=zeros(Nb+1,1)+0.1;
dri=zeros(Nb+1,1)+0.1;
start=1;
xo=zeros(N,1);
do=xo;
eo=xo;
echoBands=zeros(Nb+1,1);
cohxdAvg=zeros(Nb+1,1);
cohxdSlow=zeros(Nb+1,N+1);
cohedSlow=zeros(Nb+1,N+1);
%overdriveM=zeros(Nb+1,N+1);
cohxdFastAvg=zeros(Nb+1,1);
cohxdAvgBad=zeros(Nb+1,1);
cohedAvg=zeros(Nb+1,1);
cohedFastAvg=zeros(Nb+1,1);
hnledAvg=zeros(Nb+1,1);
hnlxdAvg=zeros(Nb+1,1);
ovrdV=zeros(Nb+1,1);
dIdxV=zeros(Nb+1,1);
SLxV=zeros(Nb+1,1);
hnlSortQV=zeros(Nb+1,1);
hnlPrefAvgV=zeros(Nb+1,1);
mutInfAvg=zeros(Nb+1,1);
%overdrive=zeros(Nb+1,1);
hnled = zeros(N+1, 1);
weight=zeros(N+1,1);
hnlMax = zeros(N+1, 1);
hnl = zeros(N+1, 1);
overdrive = ones(1, N+1);
xfwm=zeros(N+1,M);
dfm=zeros(N+1,M);
WFbD=ones(N+1,1);
fbSupp = 0;
hnlLocalMin = 1;
cohxdLocalMin = 1;
hnlLocalMinV=zeros(Nb+1,1);
cohxdLocalMinV=zeros(Nb+1,1);
hnlMinV=zeros(Nb+1,1);
dkEnV=zeros(Nb+1,1);
ekEnV=zeros(Nb+1,1);
ovrd = 2;
ovrdPos = floor((N+1)/4);
ovrdSm = 2;
hnlMin = 1;
minCtr = 0;
SeMin = 0;
SdMin = 0;
SeLocalAvg = 0;
SeMinSm = 0;
divergeFact = 1;
dIdx = 1;
hnlMinCtr = 0;
hnlNewMin = 0;
divergeState = 0;
Sy=ones(N+1,1);
Sym=1e7*ones(N+1,1);
wins=[0;sqrt(hanning(2*N-1))];
ubufn=zeros(2*N,1);
ebuf=zeros(2*N,1);
ebuf2=zeros(2*N,1);
ebuf4=zeros(2*N,1);
mbuf=zeros(2*N,1);
cohedFast = zeros(N+1,1);
cohxdFast = zeros(N+1,1);
cohxd = zeros(N+1,1);
Se = zeros(N+1,1);
Sd = zeros(N+1,1);
Sx = zeros(N+1,1);
SxBad = zeros(N+1,1);
Sed = zeros(N+1,1);
Sxd = zeros(N+1,1);
SxdBad = zeros(N+1,1);
hnledp=[];
cohxdMax = 0;
%hh=waitbar(0,'Please wait...');
progressbar(0);
%spaces = ' ';
%spaces = repmat(spaces, 50, 1);
%spaces = ['[' ; spaces ; ']'];
%fprintf(1, spaces);
%fprintf(1, '\n');
for kk=1:Nb
pos = N * (kk-1) + start;
% FD block method
% ---------------------- Organize data
xk = rrin(pos:pos+N-1);
dk = ssin(pos:pos+N-1);
xx = [xo;xk];
xo = xk;
tmp = fft(xx);
XX = tmp(1:N+1);
dd = [do;dk]; % Overlap
do = dk;
tmp = fft(dd); % Frequency domain
DD = tmp(1:N+1);
% ------------------------ Power estimation
pn0 = (1 - alp) * pn0 + alp * real(XX.* conj(XX));
pn = pn0;
%pn = (1 - alp) * pn + alp * M * pn0;
if (CNon)
Yp = real(conj(DD).*DD); % Instantaneous power
Sy = (1 - alp) * Sy + alp * Yp; % Averaged power
mm = min(Sy,Sym);
diff = Sym - mm;
if (kk>50)
Sym = (mm + step*diff) * ramp; % Estimated background noise power
end
end
% ---------------------- Filtering
XFm(:,1) = XX;
for mm=0:(M-1)
m=mm+1;
YFb(:,m) = XFm(:,m) .* WFb(:,m);
end
yfk = sum(YFb,2);
tmp = [yfk ; flipud(conj(yfk(2:N)))];
ykt = real(ifft(tmp));
ykfb = ykt(end-N+1:end);
% ---------------------- Error estimation
ekfb = dk - ykfb;
%if sum(abs(ekfb)) < sum(abs(dk))
%ekfb = dk - ykfb;
% erfb(pos:pos+N-1) = ekfb;
%else
%ekfb = dk;
% erfb(pos:pos+N-1) = dk;
%end
%(kk-1)*(N*2)+1
erfb(pos:pos+N-1) = ekfb;
tmp = fft([zm;ekfb]); % FD version for cancelling part (overlap-save)
Ek = tmp(1:N+1);
% ------------------------ Adaptation
Ek2 = Ek ./(M*pn + 0.001); % Normalized error
%Ek2 = Ek ./(pn + 0.001); % Normalized error
%Ek2 = Ek ./(100*pn + 0.001); % Normalized error
absEf = max(abs(Ek2), threshold);
absEf = ones(N+1,1)*threshold./absEf;
Ek2 = Ek2.*absEf;
mEk = mufb.*Ek2;
PP = conj(XFm).*(ones(M,1) * mEk')';
tmp = [PP ; flipud(conj(PP(2:N,:)))];
IFPP = real(ifft(tmp));
PH = IFPP(1:N,:);
tmp = fft([PH;zeros(N,M)]);
FPH = tmp(1:N+1,:);
WFb = WFb + FPH;
if mod(kk, 10*mult) == 0
WFbEn = sum(real(WFb.*conj(WFb)));
%WFbEn = sum(abs(WFb));
[tmp, dIdx] = max(WFbEn);
WFbD = sum(abs(WFb(:, dIdx)),2);
%WFbD = WFbD / (mean(WFbD) + 1e-10);
WFbD = min(max(WFbD, 0.5), 4);
end
dIdxV(kk) = dIdx;
% NLP
if (NLPon)
ee = [eo;ekfb];
eo = ekfb;
window = wins;
if fs == 8000
%gamma = 0.88;
gamma = 0.9;
else
%gamma = 0.92;
gamma = 0.93;
end
%gamma = 0.9;
tmp = fft(xx.*window);
xf = tmp(1:N+1);
tmp = fft(dd.*window);
df = tmp(1:N+1);
tmp = fft(ee.*window);
ef = tmp(1:N+1);
xfwm(:,1) = xf;
xf = xfwm(:,dIdx);
%fprintf(1,'%d: %f\n', kk, xf(4));
dfm(:,1) = df;
SxOld = Sx;
Se = gamma*Se + (1-gamma)*real(ef.*conj(ef));
Sd = gamma*Sd + (1-gamma)*real(df.*conj(df));
Sx = gamma*Sx + (1 - gamma)*real(xf.*conj(xf));
%xRatio = real(xfwm(:,1).*conj(xfwm(:,1))) ./ ...
% (real(xfwm(:,2).*conj(xfwm(:,2))) + 1e-10);
%xRatio = Sx ./ (SxOld + 1e-10);
%SLx = log(1/(N+1)*sum(xRatio)) - 1/(N+1)*sum(log(xRatio));
%SLxV(kk) = SLx;
%freqSm = 0.9;
%Sx = filter(freqSm, [1 -(1-freqSm)], Sx);
%Sx(end:1) = filter(freqSm, [1 -(1-freqSm)], Sx(end:1));
%Se = filter(freqSm, [1 -(1-freqSm)], Se);
%Se(end:1) = filter(freqSm, [1 -(1-freqSm)], Se(end:1));
%Sd = filter(freqSm, [1 -(1-freqSm)], Sd);
%Sd(end:1) = filter(freqSm, [1 -(1-freqSm)], Sd(end:1));
%SeFast = ef.*conj(ef);
%SdFast = df.*conj(df);
%SxFast = xf.*conj(xf);
%cohedFast = 0.9*cohedFast + 0.1*SeFast ./ (SdFast + 1e-10);
%cohedFast(find(cohedFast > 1)) = 1;
%cohedFast(find(cohedFast > 1)) = 1 ./ cohedFast(find(cohedFast>1));
%cohedFastAvg(kk) = mean(cohedFast(echoBandRange));
%cohedFastAvg(kk) = min(cohedFast);
%cohxdFast = 0.8*cohxdFast + 0.2*log(SdFast ./ (SxFast + 1e-10));
%cohxdFastAvg(kk) = mean(cohxdFast(echoBandRange));
% coherence
Sxd = gamma*Sxd + (1 - gamma)*xf.*conj(df);
Sed = gamma*Sed + (1-gamma)*ef.*conj(df);
%Sxd = filter(freqSm, [1 -(1-freqSm)], Sxd);
%Sxd(end:1) = filter(freqSm, [1 -(1-freqSm)], Sxd(end:1));
%Sed = filter(freqSm, [1 -(1-freqSm)], Sed);
%Sed(end:1) = filter(freqSm, [1 -(1-freqSm)], Sed(end:1));
cohed = real(Sed.*conj(Sed))./(Se.*Sd + 1e-10);
%cohedAvg(kk) = mean(cohed(echoBandRange));
%cohedAvg(kk) = cohed(6);
%cohedAvg(kk) = min(cohed);
cohxd = real(Sxd.*conj(Sxd))./(Sx.*Sd + 1e-10);
%freqSm = 0.5;
%cohxd(3:end) = filter(freqSm, [1 -(1-freqSm)], cohxd(3:end));
%cohxd(end:3) = filter(freqSm, [1 -(1-freqSm)], cohxd(end:3));
%cohxdAvg(kk) = mean(cohxd(echoBandRange));
%cohxdAvg(kk) = (cohxd(32));
%cohxdAvg(kk) = max(cohxd);
%xf = xfm(:,dIdx);
%SxBad = gamma*SxBad + (1 - gamma)*real(xf.*conj(xf));
%SxdBad = gamma*SxdBad + (1 - gamma)*xf.*conj(df);
%cohxdBad = real(SxdBad.*conj(SxdBad))./(SxBad.*Sd + 0.01);
%cohxdAvgBad(kk) = mean(cohxdBad);
%for j=1:N+1
% mutInf(j) = 0.9*mutInf(j) + 0.1*information(abs(xfm(j,:)), abs(dfm(j,:)));
%end
%mutInfAvg(kk) = mean(mutInf);
%hnled = cohedFast;
%xIdx = find(cohxd > 1 - cohed);
%hnled(xIdx) = 1 - cohxd(xIdx);
%hnled = 1 - max(cohxd, 1-cohedFast);
hnled = min(1 - cohxd, cohed);
%hnled = 1 - cohxd;
%hnled = max(1 - (cohxd + (1-cohedFast)), 0);
%hnled = 1 - max(cohxd, 1-cohed);
if kk > 1
cohxdSlow(kk,:) = 0.99*cohxdSlow(kk-1,:) + 0.01*cohxd';
cohedSlow(kk,:) = 0.99*cohedSlow(kk-1,:) + 0.01*(1-cohed)';
end
if 0
%if kk > 50
%idx = find(hnled > 0.3);
hnlMax = hnlMax*0.9999;
%hnlMax(idx) = max(hnlMax(idx), hnled(idx));
hnlMax = max(hnlMax, hnled);
%overdrive(idx) = max(log(hnlMax(idx))/log(0.99), 1);
avgHnl = mean(hnlMax(echoBandRange));
if avgHnl > 0.3
overdrive = max(log(avgHnl)/log(0.99), 1);
end
weight(4:end) = max(hnlMax) - hnlMax(4:end);
end
%[hg, gidx] = max(hnled);
%fnrg = Sx(gidx) / (Sd(gidx) + 1e-10);
%[tmp, bidx] = find((Sx / Sd + 1e-10) > fnrg);
%hnled(bidx) = hg;
%cohed1 = mean(cohed(cohRange)); % range depends on bandwidth
%cohed1 = cohed1^2;
%echoBands(kk) = length(find(cohed(echoBandRange) < 0.25))/length(echoBandRange);
%if (fbSupp == 0)
% if (echoBands(kk) > 0.8)
% fbSupp = 1;
% end
%else
% if (echoBands(kk) < 0.6)
% fbSupp = 0;
% end
%end
%overdrive(kk) = 7.5*echoBands(kk) + 0.5;
% Factor by which to weight other bands
%if (cohed1 < 0.1)
% w = 0.8 - cohed1*10*0.4;
%else
% w = 0.4;
%end
% Weight coherence subbands
%hnled = w*cohed1 + (1 - w)*cohed;
%hnled = (hnled).^2;
%cohed(floor(N/2):end) = cohed(floor(N/2):end).^2;
%if fbSupp == 1
% cohed = zeros(size(cohed));
%end
%cohed = cohed.^overdrive(kk);
%hnled = gamma*hnled + (1 - gamma)*cohed;
% Additional hf suppression
%hnledp = [hnledp ; mean(hnled)];
%hnled(floor(N/2):end) = hnled(floor(N/2):end).^2;
%ef = ef.*((weight*(min(1 - hnled)).^2 + (1 - weight).*(1 - hnled)).^2);
cohedMean = mean(cohed(echoBandRange));
%aggrFact = 4*(1-mean(hnled(echoBandRange))) + 1;
%[hnlSort, hnlSortIdx] = sort(hnled(echoBandRange));
[hnlSort, hnlSortIdx] = sort(1-cohxd(echoBandRange));
[xSort, xSortIdx] = sort(Sx);
%aggrFact = (1-mean(hnled(echoBandRange)));
%hnlSortQ = hnlSort(qIdx);
hnlSortQ = mean(1 - cohxd(echoBandRange));
%hnlSortQ = mean(1 - cohxd);
[hnlSort2, hnlSortIdx2] = sort(hnled(echoBandRange));
%[hnlSort2, hnlSortIdx2] = sort(hnled);
hnlQuant = 0.75;
hnlQuantLow = 0.5;
qIdx = floor(hnlQuant*length(hnlSort2));
qIdxLow = floor(hnlQuantLow*length(hnlSort2));
hnlPrefAvg = hnlSort2(qIdx);
hnlPrefAvgLow = hnlSort2(qIdxLow);
%hnlPrefAvgLow = mean(hnled);
%hnlPrefAvg = max(hnlSort2);
%hnlPrefAvgLow = min(hnlSort2);
%hnlPref = hnled(echoBandRange);
%hnlPrefAvg = mean(hnlPref(xSortIdx((0.5*length(xSortIdx)):end)));
%hnlPrefAvg = min(hnlPrefAvg, hnlSortQ);
%hnlSortQIdx = hnlSortIdx(qIdx);
%SeQ = Se(qIdx + echoBandRange(1) - 1);
%SdQ = Sd(qIdx + echoBandRange(1) - 1);
%SeQ = Se(qIdxLow + echoBandRange(1) - 1);
%SdQ = Sd(qIdxLow + echoBandRange(1) - 1);
%propLow = length(find(hnlSort < 0.1))/length(hnlSort);
%aggrFact = min((1 - hnlSortQ)/2, 0.5);
%aggrTerm = 1/aggrFact;
%hnlg = mean(hnled(echoBandRange));
%hnlg = hnlSortQ;
%if suppState == 0
% if hnlg < 0.05
% suppState = 2;
% transCtr = 0;
% elseif hnlg < 0.75
% suppState = 1;
% transCtr = 0;
% end
%elseif suppState == 1
% if hnlg > 0.8
% suppState = 0;
% transCtr = 0;
% elseif hnlg < 0.05
% suppState = 2;
% transCtr = 0;
% end
%else
% if hnlg > 0.8
% suppState = 0;
% transCtr = 0;
% elseif hnlg > 0.25
% suppState = 1;
% transCtr = 0;
% end
%end
%if kk > 50
if cohedMean > 0.98 & hnlSortQ > 0.9
%if suppState == 1
% hnled = 0.5*hnled + 0.5*cohed;
% %hnlSortQ = 0.5*hnlSortQ + 0.5*cohedMean;
% hnlPrefAvg = 0.5*hnlPrefAvg + 0.5*cohedMean;
%else
% hnled = cohed;
% %hnlSortQ = cohedMean;
% hnlPrefAvg = cohedMean;
%end
suppState = 0;
elseif cohedMean < 0.95 | hnlSortQ < 0.8
%if suppState == 0
% hnled = 0.5*hnled + 0.5*cohed;
% %hnlSortQ = 0.5*hnlSortQ + 0.5*cohedMean;
% hnlPrefAvg = 0.5*hnlPrefAvg + 0.5*cohedMean;
%end
suppState = 1;
end
if hnlSortQ < cohxdLocalMin & hnlSortQ < 0.75
cohxdLocalMin = hnlSortQ;
end
if cohxdLocalMin == 1
ovrd = 3;
hnled = 1-cohxd;
hnlPrefAvg = hnlSortQ;
hnlPrefAvgLow = hnlSortQ;
end
if suppState == 0
hnled = cohed;
hnlPrefAvg = cohedMean;
hnlPrefAvgLow = cohedMean;
end
%if hnlPrefAvg < hnlLocalMin & hnlPrefAvg < 0.6
if hnlPrefAvgLow < hnlLocalMin & hnlPrefAvgLow < 0.6
%hnlLocalMin = hnlPrefAvg;
%hnlMin = hnlPrefAvg;
hnlLocalMin = hnlPrefAvgLow;
hnlMin = hnlPrefAvgLow;
hnlNewMin = 1;
hnlMinCtr = 0;
%if hnlMinCtr == 0
% hnlMinCtr = hnlMinCtr + 1;
%else
% hnlMinCtr = 0;
% hnlMin = hnlLocalMin;
%SeLocalMin = SeQ;
%SdLocalMin = SdQ;
%SeLocalAvg = 0;
%minCtr = 0;
% ovrd = max(log(0.0001)/log(hnlMin), 2);
%divergeFact = hnlLocalMin;
end
if hnlNewMin == 1
hnlMinCtr = hnlMinCtr + 1;
end
if hnlMinCtr == 2
hnlNewMin = 0;
hnlMinCtr = 0;
%ovrd = max(log(0.0001)/log(hnlMin), 2);
ovrd = max(log(0.00001)/(log(hnlMin + 1e-10) + 1e-10), 3);
%ovrd = max(log(0.00000001)/(log(hnlMin + 1e-10) + 1e-10), 5);
%ovrd = max(log(0.0001)/log(hnlPrefAvg), 2);
%ovrd = max(log(0.001)/log(hnlMin), 2);
end
hnlLocalMin = min(hnlLocalMin + 0.0008/mult, 1);
cohxdLocalMin = min(cohxdLocalMin + 0.0004/mult, 1);
%divergeFact = hnlSortQ;
%if minCtr > 0 & hnlLocalMin < 1
% hnlMin = hnlLocalMin;
% %SeMin = 0.9*SeMin + 0.1*sqrt(SeLocalMin);
% SdMin = sqrt(SdLocalMin);
% %SeMin = sqrt(SeLocalMin)*hnlSortQ;
% SeMin = sqrt(SeLocalMin);
% %ovrd = log(100/SeMin)/log(hnlSortQ);
% %ovrd = log(100/SeMin)/log(hnlSortQ);
% ovrd = log(0.01)/log(hnlMin);
% ovrd = max(ovrd, 2);
% ovrdPos = hnlSortQIdx;
% %ovrd = max(ovrd, 1);
% %SeMin = sqrt(SeLocalAvg/5);
% minCtr = 0;
%else
% %SeLocalMin = 0.9*SeLocalMin +0.1*SeQ;
% SeLocalAvg = SeLocalAvg + SeQ;
% minCtr = minCtr + 1;
%end
if ovrd < ovrdSm
ovrdSm = 0.99*ovrdSm + 0.01*ovrd;
else
ovrdSm = 0.9*ovrdSm + 0.1*ovrd;
end
%end
%ekEn = sum(real(ekfb.^2));
%dkEn = sum(real(dk.^2));
ekEn = sum(Se);
dkEn = sum(Sd);
if divergeState == 0
if ekEn > dkEn
ef = df;
divergeState = 1;
%hnlPrefAvg = hnlSortQ;
%hnled = (1 - cohxd);
end
else
%if ekEn*1.1 < dkEn
%if ekEn*1.26 < dkEn
if ekEn*1.05 < dkEn
divergeState = 0;
else
ef = df;
end
end
if ekEn > dkEn*19.95
WFb=zeros(N+1,M); % Block-based FD NLMS
end
ekEnV(kk) = ekEn;
dkEnV(kk) = dkEn;
hnlLocalMinV(kk) = hnlLocalMin;
cohxdLocalMinV(kk) = cohxdLocalMin;
hnlMinV(kk) = hnlMin;
%cohxdMaxLocal = max(cohxdSlow(kk,:));
%if kk > 50
%cohxdMaxLocal = 1-hnlSortQ;
%if cohxdMaxLocal > 0.5
% %if cohxdMaxLocal > cohxdMax
% odScale = max(log(cohxdMaxLocal)/log(0.95), 1);
% %overdrive(7:end) = max(log(cohxdSlow(kk,7:end))/log(0.9), 1);
% cohxdMax = cohxdMaxLocal;
% end
%end
%end
%cohxdMax = cohxdMax*0.999;
%overdriveM(kk,:) = max(overdrive, 1);
%aggrFact = 0.25;
aggrFact = 0.3;
%aggrFact = 0.5*propLow;
%if fs == 8000
% wCurve = [0 ; 0 ; aggrFact*sqrt(linspace(0,1,N-1))' + 0.1];
%else
% wCurve = [0; 0; 0; aggrFact*sqrt(linspace(0,1,N-2))' + 0.1];
%end
wCurve = [0; aggrFact*sqrt(linspace(0,1,N))' + 0.1];
% For sync with C
%if fs == 8000
% wCurve = wCurve(2:end);
%else
% wCurve = wCurve(1:end-1);
%end
%weight = aggrFact*(sqrt(linspace(0,1,N+1)'));
%weight = aggrFact*wCurve;
weight = wCurve;
%weight = aggrFact*ones(N+1,1);
%weight = zeros(N+1,1);
%hnled = weight.*min(hnled) + (1 - weight).*hnled;
%hnled = weight.*min(mean(hnled(echoBandRange)), hnled) + (1 - weight).*hnled;
%hnled = weight.*min(hnlSortQ, hnled) + (1 - weight).*hnled;
%hnlSortQV(kk) = mean(hnled);
%hnlPrefAvgV(kk) = mean(hnled(echoBandRange));
hnled = weight.*min(hnlPrefAvg, hnled) + (1 - weight).*hnled;
%od = aggrFact*(sqrt(linspace(0,1,N+1)') + aggrTerm);
%od = 4*(sqrt(linspace(0,1,N+1)') + 1/4);
%ovrdFact = (ovrdSm - 1) / sqrt(ovrdPos/(N+1));
%ovrdFact = ovrdSm / sqrt(echoBandRange(floor(length(echoBandRange)/2))/(N+1));
%od = ovrdFact*sqrt(linspace(0,1,N+1))' + 1;
%od = ovrdSm*ones(N+1,1).*abs(WFb(:,dIdx))/(max(abs(WFb(:,dIdx)))+1e-10);
%od = ovrdSm*ones(N+1,1);
%od = ovrdSm*WFbD.*(sqrt(linspace(0,1,N+1))' + 1);
od = ovrdSm*(sqrt(linspace(0,1,N+1))' + 1);
%od = 4*(sqrt(linspace(0,1,N+1))' + 1);
%od = 2*ones(N+1,1);
%od = 2*ones(N+1,1);
%sshift = ((1-hnled)*2-1).^3+1;
sshift = ones(N+1,1);
hnled = hnled.^(od.*sshift);
%if hnlg > 0.75
%if (suppState ~= 0)
% transCtr = 0;
%end
% suppState = 0;
%elseif hnlg < 0.6 & hnlg > 0.2
% suppState = 1;
%elseif hnlg < 0.1
%hnled = zeros(N+1, 1);
%if (suppState ~= 2)
% transCtr = 0;
%end
% suppState = 2;
%else
% if (suppState ~= 2)
% transCtr = 0;
% end
% suppState = 2;
%end
%if suppState == 0
% hnled = ones(N+1, 1);
%elseif suppState == 2
% hnled = zeros(N+1, 1);
%end
%hnled(find(hnled < 0.1)) = 0;
%hnled = hnled.^2;
%if transCtr < 5
%hnl = 0.75*hnl + 0.25*hnled;
% transCtr = transCtr + 1;
%else
hnl = hnled;
%end
%hnled(find(hnled < 0.05)) = 0;
ef = ef.*(hnl);
%ef = ef.*(min(1 - cohxd, cohed).^2);
%ef = ef.*((1-cohxd).^2);
ovrdV(kk) = ovrdSm;
%ovrdV(kk) = dIdx;
%ovrdV(kk) = divergeFact;
%hnledAvg(kk) = 1-mean(1-cohedFast(echoBandRange));
hnledAvg(kk) = 1-mean(1-cohed(echoBandRange));
hnlxdAvg(kk) = 1-mean(cohxd(echoBandRange));
%hnlxdAvg(kk) = cohxd(5);
%hnlSortQV(kk) = mean(hnled);
hnlSortQV(kk) = hnlPrefAvgLow;
hnlPrefAvgV(kk) = hnlPrefAvg;
%hnlAvg(kk) = propLow;
%ef(N/2:end) = 0;
%ner = (sum(Sd) ./ (sum(Se.*(hnl.^2)) + 1e-10));
% Comfort noise
if (CNon)
snn=sqrt(Sym);
snn(1)=0; % Reject LF noise
Un=snn.*exp(j*2*pi.*[0;rand(N-1,1);0]);
% Weight comfort noise by suppression
Un = sqrt(1-hnled.^2).*Un;
Fmix = ef + Un;
else
Fmix = ef;
end
% Overlap and add in time domain for smoothness
tmp = [Fmix ; flipud(conj(Fmix(2:N)))];
mixw = wins.*real(ifft(tmp));
mola = mbuf(end-N+1:end) + mixw(1:N);
mbuf = mixw;
ercn(pos:pos+N-1) = mola;
end % NLPon
% Filter update
%Ek2 = Ek ./(12*pn + 0.001); % Normalized error
%Ek2 = Ek2 * divergeFact;
%Ek2 = Ek ./(pn + 0.001); % Normalized error
%Ek2 = Ek ./(100*pn + 0.001); % Normalized error
%divergeIdx = find(abs(Ek) > abs(DD));
%divergeIdx = find(Se > Sd);
%threshMod = threshold*ones(N+1,1);
%if length(divergeIdx) > 0
%if sum(abs(Ek)) > sum(abs(DD))
%WFb(divergeIdx,:) = WFb(divergeIdx,:) .* repmat(sqrt(Sd(divergeIdx)./(Se(divergeIdx)+1e-10))),1,M);
%Ek2(divergeIdx) = Ek2(divergeIdx) .* sqrt(Sd(divergeIdx)./(Se(divergeIdx)+1e-10));
%Ek2(divergeIdx) = Ek2(divergeIdx) .* abs(DD(divergeIdx))./(abs(Ek(divergeIdx))+1e-10);
%WFb(divergeIdx,:) = WFbOld(divergeIdx,:);
%WFb = WFbOld;
%threshMod(divergeIdx) = threshMod(divergeIdx) .* abs(DD(divergeIdx))./(abs(Ek(divergeIdx))+1e-10);
% threshMod(divergeIdx) = threshMod(divergeIdx) .* sqrt(Sd(divergeIdx)./(Se(divergeIdx)+1e-10));
%end
%absEf = max(abs(Ek2), threshold);
%absEf = ones(N+1,1)*threshold./absEf;
%absEf = max(abs(Ek2), threshMod);
%absEf = threshMod./absEf;
%Ek2 = Ek2.*absEf;
%if sum(Se) <= sum(Sd)
% mEk = mufb.*Ek2;
% PP = conj(XFm).*(ones(M,1) * mEk')';
% tmp = [PP ; flipud(conj(PP(2:N,:)))];
% IFPP = real(ifft(tmp));
% PH = IFPP(1:N,:);
% tmp = fft([PH;zeros(N,M)]);
% FPH = tmp(1:N+1,:);
% %WFbOld = WFb;
% WFb = WFb + FPH;
%else
% WF = WFbOld;
%end
% Shift old FFTs
%for m=M:-1:2
% XFm(:,m) = XFm(:,m-1);
% YFm(:,m) = YFm(:,m-1);
%end
XFm(:,2:end) = XFm(:,1:end-1);
YFm(:,2:end) = YFm(:,1:end-1);
xfwm(:,2:end) = xfwm(:,1:end-1);
dfm(:,2:end) = dfm(:,1:end-1);
%if mod(kk, floor(Nb/50)) == 0
% fprintf(1, '.');
%end
if mod(kk, floor(Nb/100)) == 0
%if mod(kk, floor(Nb/500)) == 0
progressbar(kk/Nb);
%figure(5)
%plot(abs(WFb));
%legend('1','2','3','4','5','6','7','8','9','10','11','12');
%title(kk*N/fs);
%figure(6)
%plot(WFbD);
%figure(6)
%plot(threshMod)
%if length(divergeIdx) > 0
% plot(abs(DD))
% hold on
% plot(abs(Ek), 'r')
% hold off
%plot(min(sqrt(Sd./(Se+1e-10)),1))
%axis([0 N 0 1]);
%end
%figure(6)
%plot(cohedFast);
%axis([1 N+1 0 1]);
%plot(WFbEn);
%figure(7)
%plot(weight);
%plot([cohxd 1-cohed]);
%plot([cohxd 1-cohed 1-cohedFast hnled]);
%plot([cohxd cohxdFast/max(cohxdFast)]);
%legend('cohxd', '1-cohed', '1-cohedFast');
%axis([1 65 0 1]);
%pause(0.5);
%overdrive
end
end
progressbar(1);
%figure(2);
%plot([feat(:,1) feat(:,2)+1 feat(:,3)+2 mfeat+3]);
%plot([feat(:,1) mfeat+1]);
%figure(3);
%plot(10*log10([dri erifb erifb3 ericn]));
%legend('Near-end','Error','Post NLP','Final',4);
% Compensate for delay
%ercn=[ercn(N+1:end);zeros(N,1)];
%ercn_=[ercn_(N+1:end);zeros(N,1)];
%figure(11);
%plot(cohxdSlow);
%figure(12);
%surf(cohxdSlow);
%shading interp;
%figure(13);
%plot(overdriveM);
%figure(14);
%surf(overdriveM);
%shading interp;
figure(10);
t = (0:Nb)*N/fs;
rrinSubSamp = rrin(N*(1:(Nb+1)));
plot(t, rrinSubSamp/max(abs(rrinSubSamp)),'b');
hold on
plot(t, hnledAvg, 'r');
plot(t, hnlxdAvg, 'g');
plot(t, hnlSortQV, 'y');
plot(t, hnlLocalMinV, 'k');
plot(t, cohxdLocalMinV, 'c');
plot(t, hnlPrefAvgV, 'm');
%plot(t, cohxdAvg, 'r');
%plot(cohxdFastAvg, 'r');
%plot(cohxdAvgBad, 'k');
%plot(t, cohedAvg, 'k');
%plot(t, 1-cohedFastAvg, 'k');
%plot(ssin(N*(1:floor(length(ssin)/N)))/max(abs(ssin)));
%plot(echoBands,'r');
%plot(overdrive, 'g');
%plot(erfb(N*(1:floor(length(erfb)/N)))/max(abs(erfb)));
hold off
tightx;
figure(11)
plot(t, ovrdV);
tightx;
%plot(mfeat,'r');
%plot(1-cohxyp_,'r');
%plot(Hnlxydp,'y');
%plot(hnledp,'k');
%plot(Hnlxydp, 'c');
%plot(ccohpd_,'k');
%plot(supplot_, 'g');
%plot(ones(length(mfeat),1)*rr1_, 'k');
%plot(ones(length(mfeat),1)*rr2_, 'k');
%plot(N*(1:length(feat)), feat);
%plot(Sep_,'r');
%axis([1 floor(length(erfb)/N) -1 1])
%hold off
%plot(10*log10([Se_, Sx_, Seu_, real(sf_.*conj(sf_))]));
%legend('Se','Sx','Seu','S');
%figure(5)
%plot([ercn ercn_]);
figure(12)
plot(t, dIdxV);
%plot(t, SLxV);
tightx;
%figure(13)
%plot(t, [ekEnV dkEnV]);
%plot(t, dkEnV./(ekEnV+1e-10));
%tightx;
%close(hh);
%spclab(fs,ssin,erfb,ercn,'outxd.pcm');
%spclab(fs,rrin,ssin,erfb,1.78*ercn,'vqeOut-1.pcm');
%spclab(fs,erfb,'aecOutLp.pcm');
%spclab(fs,rrin,ssin,erfb,1.78*ercn,'aecOut25.pcm','vqeOut-1.pcm');
%spclab(fs,rrin,ssin,erfb,ercn,'aecOut-mba.pcm');
%spclab(fs,rrin,ssin,erfb,ercn,'aecOut.pcm');
%spclab(fs, ssin, erfb, ercn, 'out0.pcm');