Support Vector Machine Performance with Optimal Parameters Identification in Recognising Asphyxiated Infant Cry

Detection of asphyxia in infant at an early stage is crucial to reduce the rate of infant morbidity. The information regarding asphyxia can be extracted from infant cry signals using support vector machine (SVM) combined with effective feature selection methods such as principal component analysis (PCA) or orthogonal least square (OLS). The performance of SVM in recognizing infant cry with asphyxia after undergone comprehensive identification of optimal parameters at the feature extraction and classification stages has not been     reported. This paper describes the two stages of optimal parameter identification; at Mel-frequency Cepstral coefficient (MFCC) analysis stage, SVM with and without employing the PCA and OLS stages, and the performance of the SVM in recognizing infant cry with asphyxia resulted from all levels of optimal parameters identification. The SVM was first optimized after performing MFCC analysis to find the optimum parameters. Two types of kernels were used, the polynomial and RBF kernels. The subsequent SVM optimizations were conducted after PCA and OLS were employed. In the PCA, the significant features were selected using eigenvalue-one-criterion (EOC), cumulative percentage variance (CPV) and the Scree test (SCREE). The SVM performance was evaluated based on classification accuracy and computation time. The experimental results have shown that the optimized SVM was able to recognize the asphyxiated infant cry with an accuracy of 94.84% and computation time of 1.98s using PCA with EOC and RBF kernel.