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ANALYSIS OF HEART SOUND AS BIOMETRIC USING MFCC & LINEAR SVM CLASSIFIER

Swati Verma, Tanuja Kashyap

Phonocardiogram (PCG) signals as a biometric is a new and novel method for user identification. This paper examines the applicability of the biometric properties of PCG signals, which can thus be included among the physiological signs used by an automatic identification system. Use of PCG signals for user recognition is a highly reliable method because heart sounds are produced by internal organs and cannot be forged easily as compared to other recognition systems. Mel frequency Cepstral Coefficients {MFCCs} has been used for feature extraction and then these feature vectors are classified to recognize a person, using Support Vector Machine (SVM) as classifier. The performance of SVM for linear kernel function was analyzed and discussed as well.