Real-time implementation and explainable AI analysis of delayless CNN-based selective fixed-filter active noise control

• Main Authors: Luo, Zhengding, Shi, Dongyuan, Ji, Junwei, Shen, Xiaoyi, Gan, Woon-Seng
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Delayless noise control; Convolutional neural network
• Online Access: https://hdl.handle.net/10356/179403
• Institution: Nanyang Technological University

The selective fixed-filter active noise control (SFANC) approach can select suitable pre-trained control filters for different types of noise. With the learning ability of convolutional neural network (CNN), the CNN-based SFANC method can automatically learn its parameters from noise data. Combining practical experience, this paper abstracts ANC as a Markov progress and provides a detailed theoretical analysis to verify the reasonableness of the CNN-based SFANC method. To validate its effectiveness, we implement the method in a multichannel ANC window, where the CNN operating in the co-processor collaborates with the real-time controller to realize delayless noise control. Additionally, an explainable AI technique is used to analyze the underlying principle of the CNN-based SFANC method, enhancing its interpretability in acoustic applications. Numerical simulations and real-time experiments demonstrate that the CNN-based SFANC method achieves not only satisfactory noise reduction performance for broadband and real-world noises but also excellent transferability.1