Hybrid metaheuristic method of ABC kernel filtering for nonlinear acoustic echo cancellation

Abstract

This research work uses nonlinear kernel adaptive filtering to eliminate echo, a type of voice that is undesirable and communicated over the microphone. Adaptive filters enable the detection of time-varying potentials and the tracking of the signal’s dynamic changes. In this work, a hybrid metaheuristic technique that combines the Artificial Bee Colony (ABC) with the Kernel Adaptive Improved Proportionate and Normalized Least Mean Square (KIPNLMS) filter is developed to significantly boost echo cancellation efficiency. The unique adaptive filter based on sub-filter and proportionate adaptation is combined in the advanced technique, which presents an improved Non-linear Acoustic Echo Cancellation (NAEC) framework. An essential part of kernel techniques is that they translate the information on to a high-dimensional feature space, where linear filtering is performed. In the initial space, the arithmetic operations are carried out by evaluating inner products among pairs of input patterns, also known as kernels. Along with the ABC step size regulation technique, kernel-based adaptive filtering improves Signal to Noise Ratio (SNR) and accelerates convergence. The algorithm's effectiveness is assessed through simulation using signal Echo Return Loss Enhancement (ERLE). The performance of Kernel LMS, Kernal Normalized LMS (KNLMS), Kernal Proportionate NLMS, and KIPNLMS algorithms are compared. In contrast to typical kernel adaptive filters, the suggested ABC-KIPNLMS increases the ERLE more than 10 dB compared to other algorithms.