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feature_extraction_techniques/mfcc_pca.py

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+#!usr/bin/python
+from python_speech_features import mfcc
+import scipy.io.wavfile as wavv
+import numpy as np
+import os
+from sklearn.decomposition import IncrementalPCA, PCA
+
+'''
+Make a numpy array with length the number of mfcc features,
+for one input take the sum of all frames in a specific feature and divide them with the number of frames. Because we extract 13 features
+from every frame now we have to add them and take the mean of them in order to describe the sample. In our previous example we take
+the mean of all this features, in this case we are using PCA to conclude to a single feature vector (1,13) with dimensionality reduction.
+'''
+def mean_features(mfcc_features,wav,folder):
+	#here we are taking all the mfccs from every frame and we are not taking the average of them, instead we
+	#are taking PCA in order to reduce the dimension of our data
+
+	#make the list of lists as a numpy array in order to keep from them 15578x13 just one samples 1x13	
+	flattend_mfcc = np.array(mfcc_features)
+
+	#just to check the shape of the array before transapose
+	#print flattend_mfcc.shape
+
+	#because the shape of the array is (1199,13) se if we apply PCA we are going to keep just the number of columns we define
+	#but this is not the point we want to keep all the columns but just one row (dimensionality reduction)
+	#so we reshape our array in order to be (13,1199) so keeping all the rows, all our features, but just one column
+	#so we reduce our dimension from 1199 to 13
+	flattend_mfcc = flattend_mfcc.transpose()
+
+	#confirm that we trnaspose the arraty
+	#print flattend_mfcc.shape
+	#initialize the pca
+	pca = PCA(n_components=1)
+
+	#fit the features in the model
+	pca.fit(flattend_mfcc)
+
+	#apply PCA and keep just one column, which means one feature vector with 13 features
+	sample = pca.transform(flattend_mfcc)
+
+	#because the result is (13,1) we want to make it a feature vector se we want to reshape it like (1,13)
+	sample = sample.transpose()
+
+	#transform it to a list in order to satisfy the format for writing the feature vector in the file
+	pca_features = sample.tolist()
+
+	#and keep just the first list, because it returns you a list of lists with only one list
+	pca_features = pca_features[0]
+
+	print pca_features