Fix Typos

Author: jsingh811

pyAudioProcessing/extract_features.py

@@ -71,7 +71,7 @@ def get_features(folder_path, feature_names):
 
 if __name__ == "__main__":
     ARGS = PARSER.parse_args()
-    # dict with stucture
+    # dict with structure
     # {
     #    <folder name inside ARGS.folder>: {
     #       <file name> :{

pyAudioProcessing/features/audioFeatureExtraction.py

@@ -252,7 +252,7 @@ def dirsWavFeatureExtraction(dirNames, mt_win, mt_step, st_win, st_step, feats,
                                        'audioData/classSegmentsRec/brush-teeth','audioData/classSegmentsRec/shower'], 1, 1, 0.02, 0.02);
 
     It can be used during the training process of a classification model ,
-    in order to get feature matrices from various audio classes (each stored in a seperate path)
+    in order to get feature matrices from various audio classes (each stored in a separate path)
     '''
 
     # feature extraction for each class:

pyAudioProcessing/trainer/audioTrainTest.py

@@ -46,7 +46,7 @@ def trainLogisticRegression(features, Cparam):
     Train a multi-class probabilitistic Logistic Regression classifier.
     Note:     This function is simply a wrapper to the sklearn functionality for logistic regression training
     ARGUMENTS:
-        - features:         a list ([numOfClasses x 1]) whose elements containt numpy matrices of features
+        - features:         a list ([numOfClasses x 1]) whose elements contain numpy matrices of features
                             each matrix features[i] of class i is [n_samples x numOfDimensions]
         - Cparam:           Logistic Regression parameter C (Inverse of regularization strength)
     RETURNS:
@@ -65,7 +65,7 @@ def trainLogisticRegression(features, Cparam):
 def evaluateclassifier(features, class_names, n_exp, classifier_name, Params, parameterMode, perTrain=0.90):
     '''
     ARGUMENTS:
-        features:     a list ([numOfClasses x 1]) whose elements containt numpy matrices of features.
+        features:     a list ([numOfClasses x 1]) whose elements contain numpy matrices of features.
                 each matrix features[i] of class i is [n_samples x numOfDimensions]
         class_names:    list of class names (strings)
         n_exp:        number of cross-validation experiments
@@ -239,7 +239,7 @@ def featureAndTrain(list_of_dirs, mt_win, mt_step, st_win, st_step,
     '''
     This function is used as a wrapper to segment-based audio feature extraction and classifier training.
     ARGUMENTS:
-        list_of_dirs:        list of paths of directories. Each directory contains a signle audio class whose samples are stored in seperate WAV files.
+        list_of_dirs:        list of paths of directories. Each directory contains a signle audio class whose samples are stored in separate WAV files.
         mt_win, mt_step:        mid-term window length and step
         st_win, st_step:        short-term window and step
         classifier_type:        "svm" or "knn" or "randomforest" or "gradientboosting" or "extratrees"