有个用matlab编程的语音去噪的程序，现在需要用DSP实现，谁教教我实现的步骤是什么啊，怎么考虑，怎么按部就班的进行，谢谢了。
程序为：function output=SSBoll79(signal,fs,IS)
% OUTPUT=SSBOLL79(S,FS,IS)
% Spectral Subtraction based on Boll 79. Amplitude spectral subtraction
% Includes Magnitude Averaging and Residual noise Reduction
% S is the noisy signal, FS is the sampling frequency and IS is the initial
% silence (noise only) length in seconds (default value is .25 sec)
%
% April-05
% Esfandiar Zavarehei
if (nargin<3 | isstruct(IS))
IS=.25; %seconds
end
W=fix(.025*fs); %Window length is 25 ms
nfft=W;
SP=.4; %Shift percentage is 40% (10ms) %Overlap-Add method works good with this value(.4)
wnd=hamming(W);
% IGNORE THIS SECTION FOR CAMPATIBALITY WITH ANOTHER PROGRAM 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
% .......IGNORE THIS SECTION FOR CAMPATIBALITY WITH ANOTHER PROGRAM T0 HERE
NIS=fix((IS*fs-W)/(SP*W) +1);%number of initial silence segments
Gamma=1;%Magnitude Power (1 for magnitude spectral subtraction 2 for power spectrum subtraction)
y=segment(signal,W,SP,wnd);
Y=fft(y,nfft);
YPhase=angle(Y(1:fix(end/2)+1,
); %Noisy Speech Phase
Y=abs(Y(1:fix(end/2)+1,
).^Gamma;%Specrogram
numberOfFrames=size(Y,2);
FreqResol=size(Y,1);
N=mean(Y(:,1:NIS)')'; %initial Noise Power Spectrum mean
NRM=zeros(size(N));% Noise Residual Maximum (Initialization)
NoiseCounter=0;
NoiseLength=9;%This is a smoothing factor for the noise updating
Beta=.03;
YS=Y; %Y Magnitude Averaged
for i=2:(numberOfFrames-1)
YS(:,i)=(Y(:,i-1)+Y(:,i)+Y(:,i+1))/3;
end
for i=1:numberOfFrames
[NoiseFlag, SpeechFlag, NoiseCounter, Dist]=vad(Y(:,i).^(1/Gamma),N.^(1/Gamma),NoiseCounter); %Magnitude Spectrum Distance VAD
if SpeechFlag==0
N=(NoiseLength*N+Y(:,i))/(NoiseLength+1); %Update and smooth noise
NRM=max(NRM,YS(:,i)-N);%Update Maximum Noise Residue
X(:,i)=Beta*Y(:,i);
else
D=YS(:,i)-N; % Specral Subtraction
if i>1 && i
for j=1:length(D)
if D(j)
D(j)=min([D(j) YS(j,i-1)-N(j) YS(j,i+1)-N(j)]);
end
end
end
X(:,i)=max(D,0);
end
end
output=OverlapAdd2(X.^(1/Gamma),YPhase,W,SP*W);
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 [NoiseFlag, SpeechFlag, NoiseCounter, Dist]=vad(signal,noise,NoiseCounter,NoiseMargin,Hangover)
%[NOISEFLAG, SPEECHFLAG, 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
%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
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;
noisefile='F50A111.wav';
[noisesignal,fs,NBITS]=WAVREAD(noisefile);
Inss=SSBoll79(noisesignal,fs);
WAVPLAY(Inss,fs);
); %Noisy Speech Phase
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