STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Radio Frequency Innovations Review Article

The Role of Adaptive Filters in Enhancing Acoustic Echo Cancellation Efficiency in Noisy Environments

  1. Preeti Manker Department of Digital Communications, Barkatullah University Institute of Technology, Bhopal
  2. Dr. Poonam Sinha Department of Digital Communications, Barkatullah University Institute of Technology, Bhopal
  3. Priyesh Jaiswal Department of Digital Communications, Barkatullah University Institute of Technology, Bhopal

Abstract

The novel approach that this work discusses is a DCD-based iterative learning filter approach improved with deep learning methodologies, designed to improve the efficiency of acoustic echo cancellation. The proposed system can really manage both linear and nonlinear echo scenarios, dynamically adapting to fluctuating acoustic environments. The above comparative evaluations with standard filter, the standard RLS filter, indicate that the mean square error, and the standard deviation of the correlation coefficients show that the performance of the proposed method is better. The filter delivers robust echo cancellation with reduced computational complexity and thus is best suited for real-time applications such as hands-free devices, teleconferencing, and VoIP systems. This work sets up a new standard in AEC, showing the efficacy of adaptive filtering integration with machine learning towards the support of modern communication technologies’ development. In order to guarantee clear audio transmission in modern communication systems, particularly in environments with high levels of background noise, acoustic echo cancellation (AEC) is essential. Adaptive filters, which can dynamically adjust to changing acoustic environments, have become indispensable tools for improving the efficiency of AEC systems. This article examines the processes of adaptive filters, focusing on their ability to reduce echoes in noisy settings. It evaluates the advantages and disadvantages of several adaptive algorithms, such as Kalman filters, Recursive Least Squares (RLS), and Least Mean Squares (LMS). Case studies and real-world applications are also reviewed to demonstrate how adaptive filtering techniques can enhance performance. The article concludes by emphasising the importance of adaptive filters in advancing acoustic echo cancellation technology and discussing potential future developments.

Keywords

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