function output=WienerScalart96(signal,fs,IS)
% output=WIENERSCALART96(signal,fs,IS)
% Wiener filter based on tracking a priori SNR usingDecision-Directed
% method, proposed by Scalart et al 96. In this method it is assumed that
% SNRpost=SNRprior +1. based on this the Wiener Filter can be adapted to a
% model like Ephraims model in which we have a gain function which is a
% function of a priori SNR and a priori SNR is being tracked using Decision
% Directed method.
% Author: Esfandiar Zavarehei
% Created: MAR-05
if (nargin<3 | 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=hamming(W);
%IGNORE FROM HERE ...............................
if (nargin>=3 & isstruct(IS))%This option is for compatibility with another programme
W=IS.windowsize
SP=IS.shiftsize/W;
%nfft=IS.nfft;
wnd=IS.window;
if isfield(IS,'IS')
IS=IS.IS;
else
IS=.25;
end
end
% ......................................UP TO HERE
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 R)
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 ctrum 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 iance
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 ori 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
function ReconstructedSignal=OverlapAdd2(XNEW,yphase,windowLen,ShiftLen);
%Y=OverlapAdd(X,A,W,S);
%Y is the signal reconstructed signal from its spectrogram. X is a matrix
%with each column being the fft of a segment of signal. A is the phase
%angle of the spectrum which should have the same dimension as X. if it is
%not given the phase angle of X is used which in the case of real values is
%zero (assuming that its the magnitude). W is the window length of time
%domain segments if not given the length is assumed to be twice as long as
%fft window length. S is the shift length of the segmentation process ( for
%example in the case of non overlapping signals it is equal to W and in the
%case of %50 overlap is equal to W/2. if not givven W/2 is used. Y is the
%reconstructed time domain signal.
%Sep-04
%Esfandiar Zavarehei
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(j*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)
% SEGMENT chops a signal to overlapping windowed segments
% A= SEGMENT(X,W,SP,WIN) returns a matrix which its columns are segmented
% and windowed frames of the input one dimentional signal, X. W is the
% number of samples per window, default value W=256. SP is the shift
% percentage, default value SP=0.4. WIN is the window that is multiplied by
% each segment and its length should be W. the default window is hamming
% window.
% 06-Sep-04
% Esfandiar Zavarehei
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)
%[NOISEFLAG, SPEECHFLAG, NOISECOUNTER, ]=vad(SIGNAL,NOISE,NOISECOUNTER,NOISEMARGIN,HANGOVER)
%Spectral Distance Voice Activity Detector
%SIGNAL is the the current frames magnitude spectrum which is to labeld as
%noise or speech, NOISE is noise magnitude spectrum template (estimation),
%NOISECOUNTER is the number of imediate previous noise frames, NOISEMARGIN
%(default 3)is the spectral distance threshold. HANGOVER ( default 8 )is
%the number of noise segments after which the SPEECHFLAG is reset (goes to
%zero). NOISEFLAG is set to one if the the segment is labeld as noise
%NOISECOUNTER returns the number of previous noise segments, this value is
%reset (to zero) whenever a speech segment is detected. DIST is the
%spectral distance.
%Saeed Vaseghi
%edited by Esfandiar Zavarehei
%Sep-04
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(find(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
