MATLAB Source Code for Noise Cancellation using LMS Algorithm

This article discusses the implementation of noise cancellation using the LMS (Least Mean Squares) algorithm and provides complete MATLAB source code. The LMS algorithm is an adaptive filtering technique widely used for noise reduction in signal processing applications. It works by continuously adjusting filter coefficients based on the error signal to minimize noise interference. Our MATLAB implementation demonstrates how to apply the LMS algorithm for effective noise cancellation, enabling clearer signal reception. The code includes key components such as: adaptive filter initialization, step size parameter configuration, error calculation, and coefficient update operations using the Widrow-Hoff update rule. Through MATLAB simulation, users can easily execute and test the algorithm's performance under various noise conditions. The provided source code contains comprehensive comments and modular structure, allowing readers to modify parameters, test different filter lengths, and analyze convergence behavior. This implementation serves as both an educational tool for understanding adaptive filtering concepts and a practical solution for real-time noise cancellation applications. We hope this resource helps developers and researchers better understand and implement LMS-based noise cancellation systems.