Recent progress in adaptive nonlinear active noise control

In this paper recent progress on adaptive nonlinear active noise control is presented. Particular attention is paid to a new learning algorithm for recurrent neural networks based on Adjoint Extended Kalman Filter that is developed for nonlinear active noise control. The overall control structure for active noise control is constructed using two recurrent neural networks: the first neural network is used to model secondary path of active noise control while the second one is employed to generate control signal. Recent work by authors on combined FIR and neural networks is presented for nolinear active noise control to exploit the benefit of high-order tapped delay line in FIR filter and of the nonlinearity of function expansion. Real-time experiment of the proposed algorithm using Digital Signal Processor is carried-out to show the effectiveness of the method.