Selective fixed-filter active noise control (SFANC) is a novel approach capable of mitigating noise with varying frequency characteristics. It offers faster response and greater computational efficiency compared to traditional adaptive algorithms. However, spatial factors, particularly the influence of the noise source location, are often overlooked. Some existing studies have explored the impact of the direction-of-arrival (DoA) of the noise source on ANC performance, but they are mostly limited to free-field conditions and do not consider the more complex indoor reverberant environments. To address this gap, this paper proposes a learning-based directional SFANC method that incorporates the DoA of the noise source in reverberant environments. In this framework, multiple reference signals are processed by a convolutional neural network (CNN) to estimate the azimuth and elevation angles of the noise source, as well as to identify the most appropriate control filter for effective noise cancellation. Compared to traditional adaptive algorithms, the proposed approach achieves superior noise reduction with shorter response times, even in the presence of reverberations.

翻译：选择性固定滤波器有源噪声控制（SFANC）是一种能够抑制具有变化频率特性噪声的新方法。与传统自适应算法相比，它具有更快的响应速度和更高的计算效率。然而，空间因素，特别是噪声源位置的影响，常常被忽视。现有的一些研究探讨了噪声源到达方向（DoA）对ANC性能的影响，但这些研究大多局限于自由场条件，并未考虑更为复杂的室内混响环境。为填补这一空白，本文提出了一种基于学习的方向性SFANC方法，该方法在混响环境中纳入了噪声源的DoA。在此框架中，多个参考信号通过卷积神经网络（CNN）进行处理，以估计噪声源的方位角和俯仰角，并识别出最合适的控制滤波器以实现有效的噪声消除。与传统自适应算法相比，即使在存在混响的情况下，所提出的方法也能以更短的响应时间实现更优的降噪效果。