doi: 10.7763/IJCTE.2010.V2.153
Kernel Least Mean Square Features for HMM-Based Signal Recognition
Seyed Hossein Ghafarian1
,
Hadi Sadoghi Yazdi2 ,
Hamidreza Baradaran Kashani
- 1university lecturer in Mashhad.
- 2Department of Computer Engineering,Ferdowsi University of Mashhad, Mashhad, Iran.
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.
Keywords
- Kernel least mean square
- feature extraction
- nonlinear prediction
- linear predictive coding
- signal recognition
How to Cite
Seyed Hossein Ghafarian, Hadi Sadoghi Yazdi, and 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. https://doi.org/10.7763/IJCTE.2010.V2.153
Copyright & License
Copyright © 2010 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).