期刊名称：Canadian Journal on Artificail Intelligence, Machin Learning and Pattern Recognition
出版年度：2011
卷号：2
期号：2
页码：28-33
出版社：AM Publishers Corporation Canada
摘要：Active noise control (ANC) is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise signal with equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, the performance of two kinds of feedforward neural networks in active noise cancellation is evaluated. For this reason, multilayer perceptron (MLP) and generalized regression neural networks (GRNN) are designed and trained with acoustic noise signals. After training, performance of these networks in noise attenuation is investigated and compared. In order to compare the two networks, training and test samples are similar. Sound noise signals are selected from SPIB database. The results of simulation show the ability of MLP network and GRNN in active cancellation of sound noise. As it is seen, multilayer perceptron network has better performance in noise attenuation than the generalized regression neural network.
