Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks
This work addresses noise attenuation in practical ANC systems, but it is incremental as it builds on existing SFANC methods by comparing CNNs.
This paper tackled the problem of selecting pre-trained control filters for active noise control (ANC) systems by evaluating different convolutional neural networks (CNNs) in selective fixed-filter ANC (SFANC), finding that fine-tuning improved selection performance.
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.