本來畢設是這個類型的（後來去了這個環節。總的來說也有所收穫）
看了各種論文，都是GMM-EM，概率論朝天。
還會聚類分析預測一下，然後就是很多不懂的東西。我只知道我根據語音分析工具獲得的是許多離散點
這些離散點可以plot(x,y)直接就化成曲線，也可以化成直方圖。
不懂多幀高斯混合所謂的階數問題。可能看了那麼多高斯算法還沒有真正讀投那些的含義。
簡單說我用得是STRAIGHT工具提取出來的sp進行擬合。如果有其他離散點也是可以的。
根據博主提供的知識http://www.cnblogs.com/xingshansi/p/6539319.html#3655783。
終於知道單幀高斯的數據要怎麼弄了。
藍色的是我通過博文裏面的方法獲得的數據，data和dataY，但是扔進高斯里面的其實只是data而已。
黑色是原來的單幀sp。紅色是我用16個高斯/正態分佈弄的。
‘一sp.mat’是多幀頻譜，我取的是第三幀去擬合的效果，lsp第三幀。代碼中的gaussmix是用的voicetool裏面的
load('一sp.mat')
y=sp;
lsp=y(:,3);
%%
cnt=10000;
ilen=linspace(1,1025,cnt);
%isp=interp1(1:1025,lsp,ilen);%改變採樣點數
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');第三幀數據
我用的是save('sp3.txt','lsp','-ascii')保存的
所以要也那樣保存，在用lsp=load('sp3.txt','-ascii'); 就可以直接從代碼cnt=10000接着進行了
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