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matlab单帧频谱16个高斯混合拟合

时间:2022-11-25 11:31:31浏览次数:45  
标签:02 03 01 16 sig matlab lsp ind 单帧


本来毕设是这个类型的(后来去了这个环节。总的来说也有所收获)

看了各种论文,都是GMM-EM,概率论朝天。

还会聚类分析预测一下,然后就是很多不懂的东西。我只知道我根据语音分析工具获得的是许多离散点

这些离散点可以plot(x,y)直接就化成曲线,也可以化成直方图。

不懂多帧高斯混合所谓的阶数问题。可能看了那么多高斯算法还没有真正读投那些的含义。

简单说我用得是STRAIGHT工具提取出来的sp进行拟合。如果有其他离散点也是可以的。

终于知道单帧高斯的数据要怎么弄了。

蓝色的是我通过博文里面的方法获得的数据,data和dataY,但是扔进高斯里面的其实只是data而已。

黑色是原来的单帧sp。红色是我用16个高斯/正态分布弄的。

‘一sp.mat’是多帧频谱,我取的是第三帧去拟合的效果,lsp第三帧。代码中的gaussmix是用的voicetool里面的

matlab单帧频谱16个高斯混合拟合_5e


 

load('一sp.mat')
y=sp;
lsp=y(:,3);
%%
cnt=10000;
ilen=linspace(1,1025,cnt);
%isp=interp1(1:1025,lsp,ilen);%改变采样点数
Ux =1025*rand(1,cnt);
isp=interp1(1:1025,lsp,Ux);
Uy=max(isp)*rand(1,cnt);
data=Ux(Uy<=isp);
datay=Uy(Uy<=isp);
figure
plot(data,datay,'*');
hold on
plot(lsp,'k');
%[u,sig,t,iter] = fit_mix_gaussian(data',14);
[u,sig,t,g,f,pp,gg]=gaussmix(data',[],100.001,16,'kf');
u=u';
sig=sig';
t=t';
for ind=1:1025
newisp(ind)=0;
newisp(ind)=sum(t./sqrt(2*pi*sig).*exp(-(ind-u).^2./(2*sig)),2);
end

t=t*max(lsp)/max(newisp);



for ind=1:1025
newisp(ind)=0;
newisp(ind)=sum(t./sqrt(2*pi*sig).*exp(-(ind-u).^2./(2*sig)),2);

end
plot(newisp,'r');


第三帧数据  

我用的是save('sp3.txt','lsp','-ascii')保存的

所以要也那样保存,在用lsp=load('sp3.txt','-ascii');  就可以直接从代码cnt=10000接着进行了

4.5349058e-02
4.5367922e-02
4.5399739e-02
4.5409654e-02
4.5367936e-02
4.5247795e-02
4.5031328e-02
4.4707008e-02
4.4273857e-02
4.3737253e-02
4.3113425e-02
4.2420359e-02
4.1660032e-02
4.0831986e-02
3.9935151e-02
3.8968584e-02
3.7931499e-02
3.6822223e-02
3.5640654e-02
3.4387561e-02
3.3069287e-02
3.1694881e-02
3.0294978e-02
2.8916411e-02
2.7600819e-02
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2.5295814e-02
2.4352627e-02
2.3564124e-02
2.2929639e-02
2.2439603e-02
2.2074569e-02
2.1816707e-02
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标签:02,03,01,16,sig,matlab,lsp,ind,单帧
From: https://blog.51cto.com/datrilla/5886080

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