A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning

• URL: http://arxiv.org/abs/2208.08082v1
• Date: Wed, 17 Aug 2022 05:42:39 GMT
• Title: A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning
• Authors: Zhengding Luo, Dongyuan Shi, and Woon-Seng Gan
• Abstract summary: Filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. This paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence. Experiments show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness.
• Score: 17.38644275080562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness.

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