COMPARATIVE ANALYSIS OF ADAPTIVE DIGITAL FILTERING METHODS FOR SPEECH ENHANCEMENT IN NOISY ENVIRONMENTS

Abstract

Speech enhancement in noisy environments remains one of the major challenges in digital signal processing and multimedia technologies. Environmental noise significantly degrades speech quality, reduces intelligibility, and negatively affects communication systems, automatic speech recognition, and intelligent audio applications. This paper presents a comprehensive comparative analysis of widely used adaptive digital filtering algorithms, including Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Wiener filtering, Recursive Least Squares (RLS), and Kalman filtering. Their mathematical models, convergence characteristics, computational complexity, stability, and noise suppression capabilities are analyzed. Based on the identified limitations of existing approaches, a conceptual adaptive parameter optimization framework is proposed to improve speech enhancement performance under dynamically changing acoustic environments. The presented analysis provides a theoretical foundation for future research aimed at developing efficient adaptive filtering techniques suitable for real-time multimedia and communication systems.