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我是 Matlab 的新手，正在做一个信号处理项目（语音识别）。在进行了一些计算之后，我在矩阵中得到了一些称为 MFCC（梅尔频率倒谱系数）的值。我现在应该使用函数 gmdistribution.fit(X,k) 应用高斯混合模型 (GMM) 分布。但我不断收到错误，

X must have more rows than columns.

我不明白，我该如何解决这个问题？我尝试对矩阵进行转置，但随后出现其他错误。

??? Error using ==> gmcluster at 181
Ill-conditioned covariance created at iteration 3.

Error in ==> gmdistribution.fit at 199
    [S,NlogL,optimInfo] =...

我的 MFCC 矩阵一般有 13 行和大约 50-80 列。

有想法该怎么解决这个吗？我应该一次最多使用 12 列吗？或者在语音识别中获得最大似然 (ML) 估计的替代期望最大化 (EM) 算法是什么？

这是我从语音中提取 mfcc 特征向量后得到的示例矩阵：

 53.19162380493035  53.04536473593154   52.52404588266867   52.76558091790412   53.63907256262721   53.357790132994836  52.73205096524416   52.902995065027056  52.61096061282659   54.15474467851871   53.67444472478125   52.64177726437717   52.51697384592561   52.71137919365186   53.092851922453896  53.16427640450918   54.43019514688636   60.79640902129941   59.84919922646779   63.15389910551327   61.88723594060794   64.74826830389657   64.8349874832628    64.86278444375218   65.76126193531795   65.64589407152897   65.46920375829764   65.69178734432299   65.28831375816117   64.56074008418904   63.4966945660873    63.81859800557705   63.72800219675504   62.48994205815299   62.170438508902436  61.06563184036766   59.13583014975035   58.81335869501639   56.32130498897641   55.13711899166046   54.013505531107796  54.15759852717166   53.44176740036524   53.13219768600348   53.03407270007307   52.88271825256845   53.822163186509016  53.53892778841879   54.04538463287215   59.485371756367954  58.48009762761471   54.643413468895346  52.808848460884654  52.87392859698496   52.42111841679119   53.2365666558251    53.30622484832905   53.1799318016215    53.784807994410315  53.248067707554924  52.69122098296521   52.50131276155125   53.43030515391315   53.902384536061604  54.029570128176985  52.842675820980034  52.79731975873874   53.18695701339912
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-1.6428664752690114 3.7865726986742145  2.5318491820052564  -2.1947219298888676 -2.1237775233625986 2.598630953202959   -6.076201524281277  -5.315246911864284  -1.5747455209374586 -3.223379488606859  2.6008295264581776  1.3270506534986315  -2.5790744715346676 0.7756431623687378  3.0553271757777356  -0.20800002044634847    -1.530027153710214  -2.207970121996219  -1.8813636939941347 -2.685201388968379  -1.2497372042225408 2.5726591149003712  1.4779209530617206  0.18848939011950389 -0.8737068656038859 4.364271583896629   2.0338276700410187  4.017665258617117   2.929288856255161   10.031463178073729  7.807148474194119   8.930649791195147   9.356704480964387   4.682860624638529   3.9421955431659375  3.46979114616638    0.10907941624689588 1.013539556043216   1.380950812959332   1.077296756517698   4.643176114193134   0.276532579753215   1.3247848485761091  -1.6452351331258643 5.459080479943587   -2.623903958160855  -3.6495250981385525 0.30098983943901886 1.2192582165344557  3.9341748890807207  3.8902438441040768  2.3070835920696586  -2.692501110699399  1.6807838025217028  1.5259881694196216  0.3750392433389195  5.708674336592535   -1.1571072509634228 -1.9909829706185518 -2.911287549300028  -4.934348834333174  -2.258176779559039  0.17624511060134188 0.02295826196619305 -3.516972940169973  5.184345513656031   1.4594074325337887  -0.19794455729474633
2.362306464828889   1.8140886321872307  3.105122487428386   -2.452729932993756  -1.9482153346221507 0.23556664481369372 1.0605939999557794  9.466891504042334   4.485454438679325   2.6792667132201102  -0.7696085536288818 1.1799363148487811  -4.770207147524265  0.7773255533610134  -1.0253054017942649 5.364238239319841   3.1331011184169473  4.744685304867839   -0.052537238369118014   4.477806263589113   3.1539530991186067  6.4185233259645385  2.549990446321861   2.4829837421356564  4.089323590949597   7.9396405004582045  6.041498345508568   9.234608707932582   7.3843205505399885  10.495371462065135  15.043508733932194  8.70736248600434    13.199534350054295  9.807690741908354   9.182134815924455   12.06839623216329   7.974743468866006   12.349726591545481  5.750367027892127   -0.6482940009399485 5.4638120941442185  1.856389413910232   1.9530813300592067  -2.8701346921179733 1.558852931425583   -0.19366384484174437    -2.6386457918474457 1.4662219452543457  2.079641671534525   15.326629935694294  14.705559998054612  -0.06282946858494885    -1.827803410621235  3.114649202395378   0.3720781976421628  0.43011998686353536 -3.376799358785071  -1.5552531679484054 3.060902156478365   3.5360394473034553  -2.3908283396567356 0.6675611086499327  0.22711502816964574 -6.457828495248154  -0.6807474446526474 -0.6230980701736715 2.2692316872172476  -0.979235567032777
2.306823535295793   3.4952484194762055  5.910905884417197   -3.0627994884681873 -3.2217585242174294 -0.015187803494101149   -0.9514287527346498 3.114431724585367   0.42923281798814705 -3.189859804015462  -1.472673603923648  -3.036867739556342  -0.15973786580917693    -0.0905525722541792 2.330382174351248   2.7439958525955515  0.3730263667251821  -12.515523622378907 -13.343548342714616 -11.536760383050373 -8.307383651556634  -15.660481772806875 -14.155076207607415 -14.343032997039627 -11.791205489191787 -14.964231411185601 -13.183950294156357 -8.972526839374074  -5.366478645304655  -10.910217774510665 -1.5480767893424763 -8.888577773693916  -2.6255911360834023 -5.8588628908556695 -4.145564000313309  -2.984375697431632  0.8831077064431804  -5.243824833303439  5.196626588048474   6.352837095147023   1.2112116324076188  -2.9147691775934286 -2.6935780565318352 -2.810972986669758  4.9399646272914275  -1.1703117105056318 -2.402532372315127  -4.8461309660884675 -7.261524451953783  -2.5282219889051856 -1.0065282601086587 -2.5563997598612156 -4.351683980269447  -0.46252498899381495    -5.890633052969005  -0.3032076532083649 -0.6457938679695084 -0.455043482005029  3.359840875612215   -1.7228176367513395 -3.168976094613273  -2.5233843488620917 -6.495499983402964  -3.4972987525688515 0.7115283186290751  -2.581097605905542  0.6315410714331887  0.19502062594451325
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4

3 回答 3

1

@Shark我有同样的问题，试图从一组数据生成一个高斯MM对象。我通过指定协方差的类型来解决它：

GMM1=gmdistribution.fit(X,k,'CovType','diagonal')

GMM1 是对象名称。您可以在中找到 X 和 k 的含义

帮助 gmdistribution.fit

如果这对您不起作用，请尝试指定 gmdistribution 已用于生成 GMM 的 EM 算法的初始值。

埃利奥斯

于 2013-05-06T15:56:54.193 回答
1

我不明白，我该如何解决这个问题？

你需要转置矩阵，你做对了。向量必须是原始的

有想法该怎么解决这个吗？

GMDISTRIBUTION 实现了标准的期望最大化 (EM) 算法。在某些情况下，它可能会收敛到包含一个或多个分量的奇异或接近奇异协方差矩阵的解。这些组件通常包含一些几乎位于低维子空间中的数据点。具有奇异协方差矩阵的解通常被认为是虚假的。有时，如果您尝试另一组初始值，这个问题可能会消失；有时，由于以下任何原因，总会出现此问题：

  • 数据的维数比较多，但是观测值不够。
  • 您的数据的某些特征（变量）是高度相关的。
  • 部分或全部特征是离散的。
  • 您试图将数据拟合到太多的组件。

在您的情况下，您使用的组件数量 8 似乎太大了。您可以尝试减少组件的数量。通常，您还可以使用其他方法来避免收到“病态协方差矩阵”错误消息“

  1. 如果您不介意使用病态协方差矩阵获得解决方案，则可以使用 GMDISTRIBUTION/FIT 函数中的选项“Regularize”在每个协方差矩阵的对角线上添加一个非常小的正数。
  2. 您可以将“SharedCov”的值指定为 true，以便对每个组件使用相等的协方差矩阵。
  3. 您可以将 'CovType' 的值指定为 'diagonal' 。

也可以看看

http://www.mathworks.com/matlabcentral/newsreader/view_thread/168289

我应该一次最多使用 12 列吗？

不

于 2013-04-28T14:38:42.370 回答
0

首先你们应该使它成为线性的，因为它对于matlab来说太大了，然后最好只采用7-10个特征（我认为你得到的不仅仅是这个）。在你完成你的工作之后，然后使用 reshape 函数来使它成为你想要的

于 2016-11-14T06:15:34.737 回答