Mu Zhaoqing, Gao Ying, Guo Xinyu, Ou Shifeng
School of Physics and Electronic Information, Yantai University, Yantai 264005, China.
Key Laboratory of Geophysical Exploration Equipment, Ministry of Education, College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130000, China.
Sensors (Basel). 2025 Mar 18;25(6):1881. doi: 10.3390/s25061881.
Active Noise Control (ANC) is frequently utilized to minimize noise in industrial environments. However, the powerful pulses in industrial noise pose challenges to its application. Consequently, ANC systems necessitate a high-performance algorithm as a core component. In this process, the variable step-size strategy is the main approach for enhancing the ANC algorithm's performance but ensuring robustness while improving performance remains a challenge. To address this problem, we propose a new ANC algorithm with a variable step size. This algorithm is derived from the Affine Projection Generalized Maximum Correntropy (APGMC) method, featuring a hybrid step-size and a new step-size approach achieved by modifying the mean square deviation (MSD). To showcase the practical effectiveness of the proposed algorithm, noisy audio from a real construction site was used for noise reduction control. Results show that the proposed algorithm effectively manages noise across frequency bands, with an improvement of approximately 16% to 19.2% compared to existing similar algorithms.
有源噪声控制（ANC）经常被用于降低工业环境中的噪声。然而，工业噪声中的强脉冲对其应用提出了挑战。因此，ANC系统需要一种高性能算法作为核心组件。在此过程中，可变步长策略是提高ANC算法性能的主要方法，但在提高性能的同时确保鲁棒性仍然是一个挑战。为了解决这个问题，我们提出了一种新的可变步长ANC算法。该算法源自仿射投影广义最大相关熵（APGMC）方法，具有混合步长和通过修改均方偏差（MSD）实现的新步长方法。为了展示所提算法的实际有效性，我们使用了来自真实建筑工地的嘈杂音频进行降噪控制。结果表明，所提算法能有效管理各频段的噪声，与现有类似算法相比，降噪效果提高了约16%至19.2%。
