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【信号去噪】基于谱减法、最小均方和维纳滤波三种算法实现信号去噪含Matla

来源：互联网收集：自由互联发布时间：2022-06-15

1 简介语音信号在实际场合中通常容易受噪声的干扰,较强的噪声会降低语音识别的效率,为了使语音信号可懂,需要对噪声进行处理.本文先分析了语音和噪声的特性,介绍了谱减法、最小

1 简介

语音信号在实际场合中通常容易受噪声的干扰,较强的噪声会降低语音识别的效率,为了使语音信号可懂,需要对噪声进行处理.本文先分析了语音和噪声的特性,介绍了谱减法、最小均方和维纳滤波三种语音去噪算法,以及这些算法实现的仿真结果对比。

2 部分代码

1 简介
语音信号在实际场合中通常容易受噪声的干扰,较强的噪声会降低语音识别的效率,为了使语音信号可懂,需要对噪声进行处理.本文先分析了语音和噪声的特性,介绍了谱减法、最小均方和维纳滤波三种语音去噪算法,以及这些算法实现的仿真结果对比。

2 部分代码
function [ss,po]=specsubm(s,fs,p)
%SPECSUBM performs speech enhancement using spectral subtraction [SS,PO]=(S,FS,P)
%
% implementation of spectral subtraction algorithm by R Martin (rather slow)
% algorithm parameters: t* in seconds, f* in Hz, k* dimensionless
% 1: tg = smoothing time constant for signal power estimate (0.04): high=reverberant, low=musical
% 2: ta = smoothing time constant for signal power estimate
% used in noise estimation (0.1)
% 3: tw = fft window length (will be rounded up to 2^nw samples)
% 4: tm = length of minimum filter (1.5): high=slow response to noise increase, low=distortion
% 5: to = time constant for oversubtraction factor (0.08)
% 6: fo = oversubtraction corner frequency (800): high=distortion, low=musical
% 7: km = number of minimisation buffers to use (4): high=waste memory, low=noise modulation
% 8: ks = oversampling constant (4)
% 9: kn = noise estimate compensation (1.5)
% 10:kf = subtraction floor (0.02): high=noisy, low=musical
% 11:ko = oversubtraction scale factor (4): high=distortion, low=musical
if nargin<3 po=[0.04 0.1 0.032 1.5 0.08 400 4 4 1.5 0.02 4].'; else po=p; end
ns=length(s);
ts=1/fs;
ss=zeros(ns,1);

ni=pow2(nextpow2(fs*po(3)/po(8)));
ti=ni/fs;
nw=ni*po(8);
nf=1+floor((ns-nw)/ni);
nm=ceil(fs*po(4)/(ni*po(7)));

win=0.5*hamming(nw+1)/1.08;win(end)=[];
zg=exp(-ti/po(1));
za=exp(-ti/po(2));
zo=exp(-ti/po(5));

px=zeros(1+nw/2,1);
pxn=px;
os=px;
mb=ones(1+nw/2,po(7))*nw/2;
im=0;
osf=po(11)*(1+(0:nw/2).'*fs/(nw*po(6))).^(-1);

imidx=[13 21]';
x2im=zeros(length(imidx),nf);
osim=x2im;
pnim=x2im;
pxnim=x2im;
qim=x2im;

for is=1:nf
idx=(1:nw)+(is-1)*ni;
x=rfft(s(idx).*win);
x2=x.*conj(x);

pxn=za*pxn+(1-za)*x2;
im=rem(im+1,nm);
if im
mb(:,1)=min(mb(:,1),pxn);
else
mb=[pxn,mb(:,1:po(7)-1)];
end
pn=po(9)*min(mb,[],2);
%os= oversubtraction factor
os=zo*os+(1-zo)*(1+osf.*pn./(pn+pxn));

px=zg*px+(1-zg)*x2;
q=max(po(10)*sqrt(pn./x2),1-sqrt(os.*pn./px));
ss(idx)=ss(idx)+irfft(x.*q);

end
if nargout==0
soundsc([s; ss],fs);
end

3 仿真结果

4 参考文献
[1]吴马军, 俞兴莉, 陈亮. 语音降噪算法的实现研究[J]. 信息通信, 2019, 000(001):72-74.

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