A Family of Swish Diffusion Strategy Based Adaptive Algorithms for Distributed Active Noise Control

Abstract

The conventional filtered-x least mean square (F-xLMS) algorithm based distributed active noise control (DANC) system's performance suffers in the presence of outliers and impulse like disturbances. In an attempt to reduce noise in such an environment Swish function based algorithms for DANC systems have been proposed presently. The Swish function makes use of the smoothness and unboundedness properties for faster convergence and eliminating vanishing gradient issue. The intention is to employ the smooth approximation of Softplus and the non-convex property of Geman-McClure estimator to propose a Softplus Geman-McClure function. In addition, the bounded nonlinearity of Welsch function which is insensitive to the outliers is utilized with the regularization property of Softsign formulating Softsign Welsch method. Henceforth, this paper proposes a family of robust algorithms employing the Swish diffusion strategy for filtered-x sign, filtered-x LMS, filtered-x Softplus Geman-McClure and filtered-x Softsign Welsch algorithms for DANC systems. The weight update rules are derived for the proposed algorithms and convergence analysis is also carried out. The suggested methods achieve faster convergence in comparison with existing techniques and approximately 1–5 dB improvement in noise cancellation for various noise inputs and impulsive noise interferences.