Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks. (arXiv:2208.08440v1 [cs.LG])

Due to its rapid response time and a high degree of robustness, the selective
fixed-filter active noise control (SFANC) method appears to be a viable
candidate for widespread use in a variety of practical active noise control
(ANC) systems. In comparison to conventional fixed-filter ANC methods, SFANC
can select the pre-trained control filters for different types of noise. Deep
learning technologies, thus, can be used in SFANC methods to enable a more
flexible selection of the most appropriate control filters for attenuating
various noises. Furthermore, with the assistance of a deep neural network, the
selecting strategy can be learned automatically from noise data rather than
through trial and error, which significantly simplifies and improves the
practicability of ANC design. Therefore, this paper investigates the
performance of SFANC based on different one-dimensional and two-dimensional
convolutional neural networks. Additionally, we conducted comparative analyses
of several network training strategies and discovered that fine-tuning could
improve selection performance.