Optimized Support Vector Machine for classifying infant cries with asphyxia using Orthogonal Least Square

• Published in: ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics
• Main Author: Sahak R.; Lee Y.K.; Mansor W.; Yassin A.I.M.; Zabidi A.
• Format: Conference paper
• Language: English
• Published: 2010
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79953897666&doi=10.1109%2fICCAIE.2010.5735023&partnerID=40&md5=2c3a3799a2d86964e0d9e1b517a1cd3b

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 classify the asphyxiated infant cry from the optimized and non-optimized input feature vector. The classification accuracy and support vector number are used to gauge the performance. Experimental result shows that for both kernels, the OLS-optimized SVM achieve equally high classification accuracy with lower support vector number than the non-optimized one. It is also found that the OLS-SVM with RBF kernel outperformed all other methods with classification accuracy of 93.16% and support vector number of 266.2. © 2010 IEEE.