Open AccessDominant frequency component tracking of noisy time‐varying signals using the linear predictive coding pole processing method
Author(s) -
Xu Jin,
Davis Mark,
de Fréin Ruairí
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12362
Subject(s) - computer science , linear prediction , filter (signal processing) , frequency modulation , signal processing , noise (video) , time–frequency analysis , instantaneous phase , linear predictive coding , mathematics , speech recognition , algorithm , control theory (sociology) , speech processing , artificial intelligence , digital signal processing , bandwidth (computing) , telecommunications , computer vision , control (management) , computer hardware , image (mathematics)
The linear predictive coding pole processing (LPCPP) method proposed in our previous work overcomes the shortcomings of the LPC method, especially its sensitivity to noise and the filter order. The LPCPP method is a parameterised method that involves processing the LPC poles to produce a series of reduced‐order filter transfer functions to estimate the dominant frequency components of a signal. This paper analyses the ability of the LPCPP method to track the frequency changes of noisy, time‐varying signals in real‐time. Linear chirped frequency modulation signals are used in a series of experiments to simulate signals with different rates of frequency change. The results show that the LPCPP method can achieve real‐time tracking of the dominant frequency in the signal and outperforms the LPC method under different frequency change rates and different noise levels. Specifically, the valid estimate percentage of LPCPP is up to 41.3% higher than that of LPC which indicates that the LPCPP method significantly improves the validity of frequency estimates.