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
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
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