Parameter Extraction Using MATLAB's Built-in LPC Program

In speech signal processing and audio analysis, Linear Predictive Coding (LPC) serves as a classical method for extracting characteristic parameters of signals. MATLAB provides built-in LPC-related functions that facilitate efficient implementation of this process.

The fundamental concept of LPC involves estimating speech signal parameters through a linear prediction model. This model assumes that the current signal value can be represented as a linear combination of previous signal values plus an error term. Using this approach, we can extract parameters reflecting signal characteristics, such as prediction coefficients and formants.

In MATLAB, the `lpc` function calculates LPC coefficients by taking two main inputs: the signal vector and the prediction order (model order). The function returns an LPC coefficient vector. Higher-order models provide more accurate signal approximations but increase computational complexity. The syntax follows: a = lpc(x, p) where x is the input signal and p is the prediction order.

Standard implementation steps typically include: Preprocessing the signal (e.g., pre-emphasis, framing) Applying the `lpc` function to compute LPC coefficients for each frame Extracting derived features such as reflection coefficients (using the levinson function) or spectral envelopes from the LPC coefficients

LPC parameters find extensive applications in speech synthesis, speech recognition, and compression coding. MATLAB's LPC functions offer researchers convenient tools for rapid algorithm validation and feature extraction, with additional signal processing toolbox functions supporting comprehensive analysis workflows.