An LPC pole processing method for enhancing the identification of dominant spectral features

Abstract This paper proposes a new time‐resolved spectral analysis method based on a modification to the linear predictive coding (LPC) method for enhancing the identification of the dominant frequencies of a signal. The method described here is based on a z‐plane analysis of the LPC poles. These poles are used to produce a series of reduced order filter transfer functions which can accurately identify and estimate the frequency of the dominant spectral features. The standard LPC method has been shown to suffer from a sensitivity to noise and its performance is dependent on the filter order. The proposed method can accurately identify the dominant frequency components over a range of filter orders and is shown to be robust in the presence of noise. Compared with traditional time‐resolved methods, it is a parameterised method where the identification of the dominant frequency changes can be directly obtained in the form of frequency measurements. In a series of 10,000 Monte Carlo experiments on single component and multiple component signals, this LPC pole processing method outperforms the standard LPC method by accurately identifying the dominant frequency components in the signals.