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This paper investigates the effect of optimizing Support Vector Machine, with linear and RBF kernels, on its performance in classifying asphyxiated infant cries, with Orthogonal Least Square. Mel Frequency Cepstrum analysis first extracts feature from the infant cry signals. The extracted features are then ranked in accordance to its error reduction ratio with OLS. SVM with linear and RBF kernel then...
This paper describes the classification of asphyxiated infant cry using orthogonal least square (OLS) based Support vector machine (SVM). The features of the cry signal were extracted using mel frequency cepstral coefficient analysis and significant features were selected using OLS. SVM with linear and RBF kernels were used to classify the asphyxiated infant cry signals. Classification accuracy and...
Combined Support Vector Machine (SVM) and Principal Component Analysis (PCA) was used to recognize the infant cries with asphyxia. SVM classifier based on features selected by the PCA was trained to differentiate between pathological and healthy cries. The PCA was applied to reduce dimensionality of the vectors that serve as inputs to the SVM. The performance of the SVM utilizing linear and RBF kernel...
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