Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks

TL;DR

This paper evaluates the effectiveness of selective fixed-filter active noise control enhanced by various convolutional neural networks, demonstrating improved noise attenuation and automatic filter selection through deep learning techniques.

Contribution

It introduces the use of different CNN architectures for SFANC and compares training strategies, notably fine-tuning, to enhance noise control performance.

Findings

CNN-based SFANC improves noise attenuation.

Fine-tuning enhances filter selection accuracy.

Deep learning simplifies ANC design.

Abstract

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.