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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...
Feature extraction techniques for input representation to diagnose infant diseases have received significant attention recently. Mel Frequency Cepstral Coefficients (MFCC) is one of the most popular feature extraction techniques due to its representation method being very similar to the human auditory system. The MFCC method for feature extraction depends on several important parameter settings, namely...
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