Deep Learning-Based Acoustic Echo Cancellation for Next- Generation Wireless Communication Systems

Abstract

Acoustic Echo Cancellation (AEC) remains a critical challenge in modern telecommunication systems and VoIP platforms. Traditional adaptive filtering algorithms, such as the Normalized Least Mean Squares (NLMS) and Recursive Least Squares (RLS), degrade significantly under non-linear loudspeaker distortions, severe double-talk scenarios, and time-varying acoustic environments. This paper presents a novel hybrid Deep Learning-assisted AEC framework integrating a recurrent neural network (LSTM/GRU architecture) with a robust adaptive filter. The proposed system suppresses non-linear acoustic echoes while preserving near-end speech signals with high perceptual quality. Extensive simulation studies conducted in MATLAB demonstrate that the proposed framework achieves superior Echo Return Loss Enhancement (ERLE) and significantly lower Mean Squared Error (MSE) compared to conventional baseline methods.