Evaluation of the Performance of Feedforward and Recurrent Neural Networks in Active Cancellation of Sound Noise

Article English OPEN
Mehrshad Salmasi ; Homayoun Mahdavi-Nasab (2012)
  • Publisher: Najafabad Branch, Islamic Azad University
  • Journal: Journal of Intelligent Procedures in Electrical Technology (issn: 2322-3871, eissn: 2345-5594)
  • Subject: Feedforward Neural Network | Recurrent Neural Network | Electrical engineering. Electronics. Nuclear engineering | TK1-9971 | Feedback ANC System | Active Noise Control (ANC)
    arxiv: Quantitative Biology::Neurons and Cognition | Computer Science::Neural and Evolutionary Computation

Active noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, performance of the neural networks is evaluated in active cancellation of sound noise. For this reason, feedforward and recurrent neural networks are designed and trained. After training, performance of the feedforwrad and recurrent networks in noise attenuation are compared. We use Elman network as a recurrent neural network. For simulations, noise signals from a SPIB database are used. In order to compare the networks appropriately, equal number of layers and neurons are considered for the networks. Moreover, training and test samples are similar. Simulation results show that feedforward and recurrent neural networks present good performance in noise cancellation. As it is seen, the ability of recurrent neural network in noise attenuation is better than feedforward network.
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