Design and Simulation of Adaptive Filters in Real-Time Environment

Abstract

The results of the study showed that adaptive filters were designed and simulated to enhance signal processing capabilities in real-time environments. The study focused on the LMS and NLMS algorithms for echo and noise cancellation in audio signals, exploring the impact of different mu values (0.01, 0.05, 0.1, and 0.5) on algorithm performance. A comprehensive literature review underscored the importance of effective adaptive filtering techniques in real-time audio processing and highlighted the trade-off between convergence speed and stability. The results indicated that a mu value between 0.05 and 0.1 optimally balances convergence speed and stability for both LMS and NLMS algorithms. The NLMS algorithm consistently outperformed LMS due to its superior adaptation to signal power variations and better stability at higher mu values. Recommendations include using the NLMS algorithm for most applications, careful tuning of the mu value, and further research into adaptive and hybrid methods. These findings aimed to enhance echo and noise cancellation, resulting in clearer and more intelligible audio signals in real-world applications. Keywords: Adaptive Filters, LMS Algorithm, NLMS Algorithm, Real-Time Signal Processing