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%DEMGMM3 Demonstrate density modelling with a Gaussian mixture model. % % Description % The problem consists of modelling data generated by a mixture of % three Gaussians in 2 dimensions with a m

function [mix, options, errlog] = gmmem(mix, x, options) %GMMEM EM algorithm for Gaussian mixture model. % % Description % [MIX, OPTIONS, ERRLOG] = GMMEM(MIX, X, OPTIONS) uses the Expectation % M

function [e, edata, eprior] = mlperr(net, x, t) %MLPERR Evaluate error function for 2-layer network. % % Description % E = MLPERR(NET, X, T) takes a network data structure NET together % with a m

%DEMGMM4 Demonstrate density modelling with a Gaussian mixture model. % % Description % The problem consists of modelling data generated by a mixture of % three Gaussians in 2 dimensions with a m

if 1 O = 4; T = 10; nex = 50; M = 2; Q = 3; else O = 8; %Number of coefficients in a vector T = 420; %Number of vectors in a sequence nex = 1; %Number of sequ

for i=1:10 fname = sprintf('..\\..\\ch6\\%db.wav',i-1); x = wavread(fname); [x1 x2] = vad(x); m = mfcc(x); m = m(x1-2:x2-2,:); for j=1:10 pout(j) = viterbi(hmm{j}, m); end [d,n] = m

function ccc = mfcc(x) % 归一化mel滤波器组系数 bank=melbankm(24,256,8000,0,0.5,'m'); bank=full(bank); bank=bank/max(bank(:)); % DCT系数,12*24 for k=1:12 n=0:23; dctcoef(k,:)=cos((2*n+1)*k*pi/(2*24)

function [hmm, pout] = train(samples, M) %输入: % samples -- 样本结构 % M -- 为每个状态指定pdf个数,如:[3 3 3 3] %输出: % hmm -- 训练完成后的hmm K = length(samples); % 计算语音参数 disp('正在计算语音参数'); for

function prob = mixture(mix, x) %计算输出概率 %输入: % mix -- 混合高斯结构 % x -- 输入向量, SIZE*1 %输出: % prob -- 输出概率 prob = 0; for j = 1:mix.M m = mix.mean(j,:); v = mix.var (j,:); w = mix.weig

function hmm = inithmm(samples, M) K = length(samples); %语音样本数 N = length(M); %状态数 hmm.N = N; hmm.M = M; % 初始概率矩阵 hmm.init = zeros(N,1); hmm.init(1) = 1; % 转移概率矩阵 hmm.trans=zeros(N