维纳滤波可直接执行matlab代码

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 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); Tcision 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

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 ?t 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 êse of P 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, Dist]=vad(signal,noise,NoiseCounter,NoiseMargin,Hangover)

%[NOISEFLAG, SPEECHFLAG, NoiseCounter, NOISECOUNTER,

DIST]=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

íited 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

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