PCA应用于MFCC饲养GMM分类器（sklearn库）

我面临一个（可能很简单）的问题，我必须使用PCA来减少我的特征向量的维数。所有这一切的主要点是创建一个预测由音素组成的句子的分类器。我用几小时的句子训练我的模型（句子只有10个），每个句子都有一个由一组音素组成的标签（见下文）。PCA应用于MFCC饲养GMM分类器（sklearn库）

我迄今所做如下：

import mdp 
from sklearn import mixture 
from features import mdcc 

def extract_mfcc(): 
    X_train = [] 
    directory = test_audio_folder 

    # Iterate through each .wav file and extract the mfcc 
    for audio_file in glob.glob(directory): 
     (rate, sig) = wav.read(audio_file) 
     mfcc_feat = mfcc(sig, rate) 

     X_train.append(mfcc_feat) 
    return np.array(X_train) 

def extract_labels(): 
    Y_train = [] 

    # here I have all the labels - each label is a sentence composed by a set of phonemes 
    with open(labels_files) as f: 
     for line in f: # Ex: line = AH0 P IY1 S AH0 V K EY1 K 
      Y_train.append(line) 
     return np.array(Y_train) 

def main(): 
    __X_train = extract_mfcc() 
    Y_train = extract_labels() 

    # Now, according to every paper I read, I need to reduce the dimensionality of my mfcc vector before to feed my gaussian mixture model 

    X_test = [] 
    for feat in __X_train: 
     pca = mdp.pca(feat) 
     X_test.append(pca) 

    n_classes = 10 # I'm trying to predict only 10 sentences (each sentence is composed by the phonemes described above) 
    gmm_classifier = mixture.GMM(n_components=n_classes, covariance_type='full') 
    gmm_classifier.fit(X_train) # error here!reason: each "pca" that I appended before in X_train has a different shape (same number of columns though)

我怎么能降维和，在同一时间，为每个PCA我提取相同的形状？

我还尝试了新事物：调用gmm_classifier.fit（...）中的for循环，我得到PCA矢量（见下面的代码）。函数fit（）有效，但我不确定我是否正确地训练GMM。

n_classes = 10 
gmm_classifier = mixture.GMM(n_components=n_classes, covariance_type='full') 

X_test = [] 
for feat in __X_train: 
    pca = mdp.pca(feat) 
    gmm_classifier.fit(pca) # in this way it works, but I'm not sure if it actually model is trained correctly

非常感谢

来源

2015-11-05 davideberdin

您不应该先将您的整个数据进行PCA处理，然后使用新的减少的数据来训练您的GMM分类器吗？ –

关于你的最后评论/问题： gmm_classifier.fit（PCA）＃这样它的工作原理，但我不知道它实际上模型正确的培训无论何时你调用这个，分类器都会忘记以前的信息，只能通过最后的数据进行训练。尝试追加循环内的专长，然后适合。

来源

2016-06-29 20:46:42 Ugur

PCA应用于MFCC饲养GMM分类器（sklearn库）

回答

相关问题