Single-sensor active noise cancellation using recurrent neural network predictors

In this paper, we propose a recurrent neural network (RNN) predictor with an application to a single-sensor active noise cancellation (ANC) system. The proposed RNN predictor has one hidden layer whose neurons are classified into two categories, recurrent hidden neurons and non-recurrent hidden neurons. Due to the RNN´s ability of modeling time-varying signals such as acoustic noises, the proposed RNN may be more suitable than the LMS-type digital filters and multilayer perceptrons (MLP). Moreover, the number of non-recurrent hidden neurons can be arbitrarily increased according to the complexity of a given problem with a relatively little increase in computation during training. In the simulation on the noise data from a moisture-removing machine, about 22.35 dB attenuation was obtained with the proposed approach while 20.83 dB attenuation with the MLP-based approach, and 14.35 dB with a filtered-x LMS algorithm