MATLAB Source Code for LPC Spectral Estimation of Speech Signals

This implementation provides MATLAB source code for performing Linear Predictive Coding (LPC) spectral estimation on speech signals. LPC spectral estimation is a fundamental technique in speech signal processing that extracts spectral information by estimating parameters of a linear predictive model. The algorithm typically involves calculating autocorrelation coefficients, solving the Yule-Walker equations using the Levinson-Durbin recursion, and deriving the spectral envelope from the LPC coefficients. MATLAB offers comprehensive signal processing tools and functions that facilitate efficient implementation of speech processing algorithms. Key functions utilized in this implementation may include: - lpc(): Computes linear prediction coefficients using autocorrelation method - filter(): Applies the inverse LPC filter to analyze residual signals - freqz(): Visualizes the frequency response of the LPC model - pwelch(): Compares LPC spectra with traditional periodogram estimates The source code enables researchers and engineers to perform LPC analysis for extracting speech characteristics like formant frequencies and spectral envelopes. This implementation supports practical applications in speech recognition, speech synthesis, and voice analysis by providing: - Frame-based processing with overlapping windows - Pre-emphasis filtering for spectral flattening - Automatic order selection for LPC modeling - Visualization tools for spectral comparison These code examples serve as valuable references for developing speech processing systems and advancing research in digital speech processing technologies.