LPCC Feature Extraction Implementation

This MATLAB program implements a complete workflow for extracting Linear Predictive Cepstral Coefficients (LPCC) features from audio signals. The implementation follows a systematic approach starting with audio signal acquisition and preprocessing. The program begins by reading audio files and converting them into digital signals using MATLAB's audioread function. The signal is then framed into short overlapping segments typically 20-30ms in duration, with appropriate windowing (Hamming window) applied to minimize spectral leakage. For each frame, the program calculates Linear Predictive Coding (LPC) coefficients using the autocorrelation method or covariance method, which models the vocal tract filter characteristics. The core algorithm converts LPC coefficients to LPCC coefficients through recursive computation, where each cepstral coefficient is derived from the corresponding LPC coefficient with contributions from previous cepstral terms. This transformation provides enhanced spectral resolution in lower frequencies, making LPCC particularly effective for speech recognition applications. The implementation then reconstructs the spectral envelope from LPCC coefficients and computes statistical measures including mean and standard deviation across frames. These statistical features form the final LPCC feature vector that captures both spectral characteristics and temporal variations. This program is optimized for speech signal processing, speech recognition, and speaker identification systems, providing robust feature extraction with configurable parameters for frame size, overlap, and LPCC order. The code includes error handling for various audio formats and normalization procedures for consistent feature scaling.