Performance of Principal Component Analysis and Orthogonal Least Square on Optimized Feature Set in Classifying Asphyxiated Infant Cry Using Support Vector Machine

<p>An investigation into optimized support vector machine (SVM) integrated with principal component analysis (PCA) and orthogonal least square (OLS) in classifying asphyxiated infant cry was performed in this study. Three approaches were used in the classification; SVM, PCA-SVM, and OLS-SVM. Various numbers of features extracted from Mel-frequency Cepstral coefficient (MFCC) were tested to obtain the optimal parameters of SVM kernels. Once...