Abstract—In this paper, an attempt is made to propose anew feature extraction method that is capable of capturing nonlinearities in signals. For this purpose, Kernel Least Mean Square KLMS (KLMS) method is used to extract features from signal and in order to evaluate it, Hidden Markov Model (HMM)is used to model extracted feature sequence and to recognize it from other models. In HMM, Gaussian Mixture Model is used. By introducing noise on signal, results showed that recognition rate in the same level of noise is good but in other SNR values it can degrade. It is also compared with Linear Predictive Coding (LPC). Results showed that in low noise level, the proposed feature extraction has better results but in high noise level LPC has better results.
Index Terms—Kernel least mean square, feature extraction, nonlinear prediction, linear predictive coding, signal recognition.
Seyed Hossein Ghafarian is an university lecturer in Mashhad (e-mail: s_h_ghafarian@ um.ac.ir).
Hadi Sadoghi Yazdi is with Department of Computer Engineering,Ferdowsi University of Mashhad, Mashhad, Iran (e-mail: email@example.com).
Hamid Reza baradaran is a MSc student in Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran (e-mail: firstname.lastname@example.org)
Cite: Seyed Hossein Ghafarian, Hadi Sadoghi Yazdi, Hamidreza Baradaran Kashani, "Kernel Least Mean Square Features for HMM-Based Signal Recognition," International Journal of Computer Theory and Engineering vol. 2, no. 2, pp. 283-289, 2010.