A Modified Simultaneous Perturbation Stochastic Approximation Algorithm for Active Noise Control

Secondary path modeling is an important step in active noise control. Keeping the active noise control system stable, when the secondary path changes over time, is a widely studied problem. Using the simultaneous perturbation stochastic approximation algorithm, can update the control filter without secondary path modeling, avoiding the problem of increased noise caused by secondary path modeling. In this paper a variant of the simultaneous perturbation stochastic approximation algorithm is proposed, which incorporates historical coefficients of the control filter, to correct the direction of the gradient calculation. Based on an experimental setup with a realistic transfer path, this paper demonstrates that the proposed algorithm has superior performance in terms of convergence speed and noise reduction amount, than the general simultaneous perturbation stochastic approximation algorithm.