1 简介 为提高低信噪比环境下语音端点检测算法性能不高的问题,提出将MFCC倒谱距离与对数能量结合进行端点检测.首先,对语音计算对数能量,然后计算改进的倒谱距离,将MFCC倒谱距离与对
1 简介
为提高低信噪比环境下语音端点检测算法性能不高的问题,提出将MFCC倒谱距离与对数能量结合进行端点检测.首先,对语音计算对数能量,然后计算改进的倒谱距离,将MFCC倒谱距离与对数能量融合,获得了一种新的语音参数,该参数能有效地提高低信噪比情况下语音与噪声的区别,对参数进行顺利滤波用于语音端点检测,采用自动更新的双阈值进行语音端判别.仿真实验表明,该算法具有较好的适用不同噪声,在低信噪比下依然能获得比较理想的端点检测效果.
2 部分代码
function [x,mc,mn,mx]=melbankm(p,n,fs,fl,fh,w)if nargin < 6
w='tz';
if nargin < 5
fh=0.5;
if nargin < 4
fl=0; % min freq is DC
end
end
end
sfact=2-any(w=='s');
wr=' '; % default warping is mel
for i=1:length(w)
if any(w(i)=='lebf');
wr=w(i);
end
end
if any(w=='h') || any(w=='H')
mflh=[fl fh];
else
mflh=[fl fh]*fs;
end
if ~any(w=='H')
switch wr
case 'f'
case 'l'
if fl<=0
error('Low frequency limit must be >0 for l option');
end
mflh=log10(mflh);
case 'e'
mflh=frq2erb(mflh);
case 'b'
mflh=frq2bark(mflh);
otherwise
mflh=frq2mel(mflh);
end
end
melrng=mflh*(-1:2:1)';
fn2=floor(n/2);
if isempty(p)
p=ceil(4.6*log10(fs));
end
if any(w=='c')
if p<1
p=round(melrng/(p*1000))+1;
end
melinc=melrng/(p-1);
mflh=mflh+(-1:2:1)*melinc;
else
if p<1
p=round(melrng/(p*1000))-1;
end
melinc=melrng/(p+1);
end
switch wr
case 'f'
blim=(mflh(1)+[0 1 p p+1]*melinc)*n/fs;
case 'l'
blim=10.^(mflh(1)+[0 1 p p+1]*melinc)*n/fs;
case 'e'
blim=erb2frq(mflh(1)+[0 1 p p+1]*melinc)*n/fs;
case 'b'
blim=bark2frq(mflh(1)+[0 1 p p+1]*melinc)*n/fs;
otherwise
blim=mel2frq(mflh(1)+[0 1 p p+1]*melinc)*n/fs;
end
mc=mflh(1)+(1:p)*melinc;
b1=floor(blim(1))+1;
b4=min(fn2,ceil(blim(4))-1);
switch wr
case 'f'
pf=((b1:b4)*fs/n-mflh(1))/melinc;
case 'l'
pf=(log10((b1:b4)*fs/n)-mflh(1))/melinc;
case 'e'
pf=(frq2erb((b1:b4)*fs/n)-mflh(1))/melinc;
case 'b'
pf=(frq2bark((b1:b4)*fs/n)-mflh(1))/melinc;
otherwise
pf=(frq2mel((b1:b4)*fs/n)-mflh(1))/melinc;
end
if pf(1)<0
pf(1)=[];
b1=b1+1;
end
if pf(end)>=p+1
pf(end)=[];
b4=b4-1;
end
fp=floor(pf);
pm=pf-fp;
k2=find(fp>0,1);
k3=find(fp<p,1,'last');
k4=numel(fp);
if isempty(k2)
k2=k4+1;
end
if isempty(k3)
k3=0;
end
if any(w=='y')
mn=1;
mx=fn2+1;
r=[ones(1,k2+b1-1) 1+fp(k2:k3) fp(k2:k3) repmat(p,1,fn2-k3-b1+1)];
c=[1:k2+b1-1 k2+b1:k3+b1 k2+b1:k3+b1 k3+b1+1:fn2+1];
v=[ones(1,k2+b1-1) pm(k2:k3) 1-pm(k2:k3) ones(1,fn2-k3-b1+1)];
else
r=[1+fp(1:k3) fp(k2:k4)];
c=[1:k3 k2:k4];
v=[pm(1:k3) 1-pm(k2:k4)];
mn=b1+1;
mx=b4+1;
end
if b1<0
c=abs(c+b1-1)-b1+1;
end
% end
if any(w=='n')
v=0.5-0.5*cos(v*pi);
elseif any(w=='m')
v=0.5-0.46/1.08*cos(v*pi);
end
if sfact==2
msk=(c+mn>2) & (c+mn<n-fn2+2);
v(msk)=2*v(msk);
end
if nargout > 2
x=sparse(r,c,v);
if nargout == 3
mc=mn;
mn=mx;
end
else
x=sparse(r,c+mn-1,v,p,1+fn2);
end
if any(w=='u')
sx=sum(x,2);
x=x./repmat(sx+(sx==0),1,size(x,2));
end
if ~nargout || any(w=='g')
ng=201;
me=mflh(1)+(0:p+1)'*melinc;
switch wr
case 'f'
fe=me;
xg=repmat(linspace(0,1,ng),p,1).*repmat(me(3:end)-me(1:end-2),1,ng)+repmat(me(1:end-2),1,ng);
case 'l'
fe=10.^me;
xg=10.^(repmat(linspace(0,1,ng),p,1).*repmat(me(3:end)-me(1:end-2),1,ng)+repmat(me(1:end-2),1,ng));
case 'e'
fe=erb2frq(me);
xg=erb2frq(repmat(linspace(0,1,ng),p,1).*repmat(me(3:end)-me(1:end-2),1,ng)+repmat(me(1:end-2),1,ng));
case 'b'
fe=bark2frq(me);
xg=bark2frq(repmat(linspace(0,1,ng),p,1).*repmat(me(3:end)-me(1:end-2),1,ng)+repmat(me(1:end-2),1,ng));
otherwise
fe=mel2frq(me);
xg=mel2frq(repmat(linspace(0,1,ng),p,1).*repmat(me(3:end)-me(1:end-2),1,ng)+repmat(me(1:end-2),1,ng));
end
v=1-abs(linspace(-1,1,ng));
if any(w=='n')
v=0.5-0.5*cos(v*pi);
elseif any(w=='m')
v=0.5-0.46/1.08*cos(v*pi);
end
v=v*sfact;
v=repmat(v,p,1);
if any(w=='y')
v(1,xg(1,:)<fe(2))=sfact;
v(end,xg(end,:)>fe(p+1))=sfact;
end
if any(w=='u')
dx=(xg(:,3:end)-xg(:,1:end-2))/2;
dx=dx(:,[1 1:ng-2 ng-2]);
vs=sum(v.*dx,2);
v=v./repmat(vs+(vs==0),1,ng)*fs/n;
end
plot(xg',v','b');
set(gca,'xlim',[fe(1) fe(end)]);
xlabel(['Frequency (' xticksi 'Hz)']);
end
3 仿真结果
编辑
4 参考文献
[1]周代勇, 曾妍萍, 蔡燕. 基于Matlab的语音识别端点检测算法研究与实现[J]. 内江科技, 2013, 34(9):2.
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