Evaluation of Neural Networks Performance in Active Cancellation of Acoustic Noise

Active Noise Control (ANC) works on the principle of destructive interference between the primary disturbance field heard as undesired noise and the secondary field which is generated from control actuators. In the simplest system, the disturbance field can be a simple sine wave, and the secondary field is the same sine wave but 180 degrees out of phase. This research presents an investigation on the use of different types of neural networks in active noise control. Performance of the multilayer perceptron (MLP), Elman and generalized regression neural networks (GRNN) in active cancellation of acoustic noise signals is investigated and compared in this paper. Acoustic noise signals are selected from a Signal Processing Information Base (SPIB ) database. In order to compare the networks appropriately, similar structures and similar training and test samples are deduced for neural networks. The simulation results show that MLP, GRNN, and Elman neural networks present proper performance in active cancellation of acoustic noise. It is concluded that Elman and MLP neural networks have better performance than GRNN in noise attenuation. It is demonstrated that designed ANC system achieve good noise reduction in low frequencies.