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

Abstract

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.11The code will be accessible at https://github.com/Luo-Zhengding/SFANC-Window.