Vector Quantization of Speech Signals Using LBG Algorithm: Code Implementation and Analysis

This implementation applies the LBG (Linde-Buzo-Gray) algorithm for vector quantization of speech signals. The compressed package contains two core modules: training.m handles training data processing and generates four initial codebooks through iterative centroid splitting and k-means clustering, while quantizing.m performs vector quantization on target data using Euclidean distance-based codevector selection. Supporting custom functions include codebook initialization routines, distortion measurement algorithms, and quantization error analysis tools. To enhance technical clarity, we elaborate on LBG's core mechanics: it minimizes quantization distortion through binary splitting of codevectors and Lloyd's algorithm iterations. Training data typically consists of speech feature vectors (e.g., MFCCs), while target data represents unseen speech segments for compression. This implementation demonstrates practical codebook generation with configurable size control and efficient nearest-neighbor search for real-time quantization applications.