--- a
+++ b/Wiener.m
@@ -0,0 +1,163 @@
+function [output,time] = Wiener(signal,fs,type,IS)
+
+if (nargin<4 || isstruct(IS))
+    IS=.25; %Initial Silence or Noise Only part in seconds
+end
+
+W=fix(.025*fs); %Window length is 25 ms
+SP=.4; %Shift percentage is 40% (10ms) %Overlap-Add method works good with this value(.4)
+wnd=hann(W);
+
+pre_emph=0;
+signal=filter([1 -pre_emph],1,signal);
+
+NIS=fix((IS*fs-W)/(SP*W) +1);%number of initial silence segments
+
+y=segment(signal,W,SP,wnd); % This function chops the signal into frames
+Y=fft(y);
+YPhase=angle(Y(1:fix(end/2)+1,:)); %Noisy Speech Phase
+Y=abs(Y(1:fix(end/2)+1,:));%Specrogram
+numberOfFrames=size(Y,2);
+FreqResol=size(Y,1);
+
+N=mean(Y(:,1:NIS)')'; %initial Noise Power Spectrum mean
+LambdaD=mean((Y(:,1:NIS)').^2)';%initial Noise Power Spectrum variance
+alpha=.99; %used in smoothing xi (For Deciesion Directed method for estimation of A Priori SNR)
+NoiseCounter=0;
+NoiseLength=9;%This is a smoothing factor for the noise updating
+G=ones(size(N));%Initial Gain used in calculation of the new xi
+Gamma=G;
+
+X=zeros(size(Y)); % Initialize X (memory allocation)
+
+h=waitbar(0,'Wait...');
+
+for i=1:numberOfFrames
+    %%%%%%%%%%%%%%%%VAD and Noise Estimation START
+    if i<=NIS % If initial silence ignore VAD
+        SpeechFlag=0;
+        NoiseCounter=100;
+    else % Else Do VAD
+        [NoiseFlag, SpeechFlag, NoiseCounter, Dist]=vad(Y(:,i),N,NoiseCounter); %Magnitude Spectrum Distance VAD
+    end
+    
+    if SpeechFlag==0 % If not Speech Update Noise Parameters
+        N=(NoiseLength*N+Y(:,i))/(NoiseLength+1); %Update and smooth noise mean
+        LambdaD=(NoiseLength*LambdaD+(Y(:,i).^2))./(1+NoiseLength); %Update and smooth noise variance
+    end
+    %%%%%%%%%%%%%%%%%%%VAD and Noise Estimation END
+    
+    gammaNew=(Y(:,i).^2)./LambdaD; %A postiriori SNR
+    xi=alpha*(G.^2).*Gamma+(1-alpha).*max(gammaNew-1,0); %Decision Directed Method for A Priori SNR
+    Gamma=gammaNew;
+    
+    G=(xi./(xi+1));
+    
+    X(:,i)=G.*Y(:,i); %Obtain the new Cleaned value
+    
+    waitbar(i/numberOfFrames,h,num2str(fix(100*i/numberOfFrames)));
+end
+
+close(h);
+output = OverlapAdd2(X,YPhase,W,SP*W); %Overlap-add Synthesis of speech
+output = filter(1,[1 -pre_emph],output); %Undo the effect of Pre-emphasis
+
+num = [0.00247553277809987,0.00495106555619995,0.00247553277809975];
+den = [1,-1.80098109524571,0.810883226358106];
+
+output = filter(num,den,output);
+time = (0:1/fs:(size(output,1)-1)/fs);
+
+if type == 1
+    output = 100.*output;
+end
+
+
+function ReconstructedSignal=OverlapAdd2(XNEW,yphase,windowLen,ShiftLen)
+
+if nargin<2
+    yphase=angle(XNEW);
+end
+if nargin<3
+    windowLen=size(XNEW,1)*2;
+end
+if nargin<4
+    ShiftLen=windowLen/2;
+end
+if fix(ShiftLen)~=ShiftLen
+    ShiftLen=fix(ShiftLen);
+    disp('The shift length have to be an integer as it is the number of samples.')
+    disp(['shift length is fixed to ' num2str(ShiftLen)])
+end
+
+[FreqRes, FrameNum]=size(XNEW);
+
+Spec=XNEW.*exp(1i*yphase);
+
+if mod(windowLen,2) %if FreqResol is odd
+    Spec=[Spec;flipud(conj(Spec(2:end,:)))];
+else
+    Spec=[Spec;flipud(conj(Spec(2:end-1,:)))];
+end
+sig=zeros((FrameNum-1)*ShiftLen+windowLen,1);
+weight=sig;
+for i=1:FrameNum
+    start=(i-1)*ShiftLen+1;    
+    spec=Spec(:,i);
+    sig(start:start+windowLen-1)=sig(start:start+windowLen-1)+real(ifft(spec,windowLen));    
+end
+ReconstructedSignal=sig;
+
+function Seg=segment(signal,W,SP,Window)
+
+if nargin<3
+    SP=.4;
+end
+if nargin<2
+    W=256;
+end
+if nargin<4
+    Window=hamming(W);
+end
+Window=Window(:); %make it a column vector
+
+L=length(signal);
+SP=fix(W.*SP);
+N=fix((L-W)/SP +1); %number of segments
+
+Index=(repmat(1:W,N,1)+repmat((0:(N-1))'*SP,1,W))';
+hw=repmat(Window,1,N);
+Seg=signal(Index).*hw;
+
+function [NoiseFlag, SpeechFlag, NoiseCounter, Dist]=vad(signal,noise,NoiseCounter,NoiseMargin,Hangover)
+
+if nargin<4
+    NoiseMargin=3;
+end
+if nargin<5
+    Hangover=8;
+end
+if nargin<3
+    NoiseCounter=0;
+end
+    
+FreqResol=length(signal);
+
+SpectralDist= 20*(log10(signal)-log10(noise));
+SpectralDist(SpectralDist<0)=0;
+
+Dist=mean(SpectralDist); 
+if (Dist < NoiseMargin) 
+    NoiseFlag=1; 
+    NoiseCounter=NoiseCounter+1;
+else
+    NoiseFlag=0;
+    NoiseCounter=0;
+end
+
+% Detect noise only periods and attenuate the signal     
+if (NoiseCounter > Hangover) 
+    SpeechFlag=0;    
+else 
+    SpeechFlag=1; 
+end
\ No newline at end of file