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我努力学习HMM GMM实现并创建了一个简单的模型来检测某些特定的声音(召唤兽等)MATLAB墨菲的HMM工具箱
我努力训练HMM(隐马尔可夫模型)网络GMM (高斯混合)在MATLAB中。
我有几个问题,我无法找到任何有关的信息。
1)应该mhmm_em()
函数在每个HMM状态的环被称为或它是自动完成?
如:
for each state
Initialize GMM’s and get parameters (use mixgauss_init.m)
end
Train HMM with EM (use mhmm_em.m)
2)
[LL, prior1, transmat1, mu1, Sigma1, mixmat1] = ...
mhmm_em(MFCCs, prior0, transmat0, mu0, Sigma0, mixmat0, 'max_iter', M);
最后一个参数,它应该是高斯或number_of_states-1的数量?
3)如果我们正在寻找最大可能性,那么维特比在哪里进场?
说如果我想用我提取的声学特征向量训练我的模型后检测某种类型的动物/人类呼叫,我是否仍然需要测试模式下的维特比算法?
这有点让我困惑,我非常感谢这部分的解释。
对HMM GMM逻辑方面的代码的任何评论也将不胜感激。
谢谢
这是我的MATLAB例程;
O = 21; % Number of coefficients in a vector(coefficient)
M = 10; % Number of Gaussian mixtures
Q = 3; % Number of states (left to right)
% MFCC Parameters
Tw = 128; % analysis frame duration (ms)
Ts = 64; % analysis frame shift (ms)
alpha = 0.95; % preemphasis coefficient
R = [ 1 1000 ]; % frequency range to consider
f_bank = 20; % number of filterbank channels
C = 21; % number of cepstral coefficients
L = 22; % cepstral sine lifter parameter(?)
%Training
[speech, fs, nbits ] = wavread('Train.wav');
[MFCCs, FBEs, frames ] = mfcc(speech, fs, Tw, Ts, alpha, hamming, R, f_bank, C, L);
cov_type = 'full'; %the covariance type that is chosen as ҦullҠfor gaussians.
prior0 = normalise(rand(Q,1));
transmat0 = mk_stochastic(rand(Q,Q));
[mu0, Sigma0] = mixgauss_init(Q*M, dat, cov_type, 'kmeans');
mu0 = reshape(mu0, [O Q M]);
Sigma0 = reshape(Sigma0, [O O Q M]);
mixmat0 = mk_stochastic(rand(Q,M));
[LL, prior1, transmat1, mu1, Sigma1, mixmat1] = ...
mhmm_em(MFCCs, prior0, transmat0, mu0, Sigma0, mixmat0, 'max_iter', M);
%Testing
for i = 1:length(filelist)
fprintf('Processing %s\n', filelist(i).name);
[speech_tst, fs, nbits ] = wavread(filelist(i).name);
[MFCCs, FBEs, frames ] = ...
mfcc(speech_tst, fs, Tw, Ts, alpha, hamming, R, f_bank, C, L);
loglik(i) = mhmm_logprob(MFCCs,prior1, transmat1, mu1, Sigma1, mixmat1);
end;
[Winner, Winner_idx] = max(loglik);
非常感谢您的回复尼古拉。 – bluemustang 2014-11-07 19:24:30