Computational load reduction of fast convergence algorithms for multichannel active noise control

In this paper, the computational load of fast convergence recursive least-squares algorithms for multichannel active noise control (ANC) is reduced by the use of an inverse model of the acoustic plant between the actuators and the error sensors. The complexity reduction applies to both classical recursive least-squares algorithms or their fast time series or order-recursive implementations. To develop the new algorithm, a comparison of several control structures (filtered-x, adjoint, filtered-e, inverse filtered-x, delay-compensated) available for the training of adaptive FIR filters in ANC is performed, based on three main factors that affect the convergence speed of the learning algorithms: correlation of input signals and acoustic plant, delay between the filters and the error signals, and filtering of the error signals. Stochastic gradient descent algorithms and recursive least-squares algorithms are combined with the different structures, and the resulting algorithms are compared based on the three factors. Several of the resulting algorithms have never been published, but of those new algorithms only one algorithm has the potential for optimal convergence speed, based on the three factors. Not only can this algorithm provide fast convergence, but for multichannel systems it also provides a large reduction of the computational load compared to the previously published algorithm with the fastest convergence. Therefore it is introduced in detail in the paper, and simulation results are presented to validate the convergence behavior of the new proposed algorithm.