用深度学习让降噪系统自动识别噪音方向，提升复杂环境下的降噪速度和效果。
Directional Selective Fixed-Filter Active Noise Control Based on a Convolutional Neural Network in Reverberant Environments
- 用卷积神经网络从多路信号中估计噪音方向并选最优滤波器
- 在混响环境下比传统方法降噪更优，响应更快
- 适合需要快速精准降噪的室内场景应用
选择性固定滤波主动降噪（SFANC）是一种能应对频率变化噪声的新方法，相比传统自适应算法具有更快响应和更高计算效率。然而，空间因素尤其是噪声源位置的影响常被忽略。已有研究探讨了噪声入射方向（DoA）对降噪性能的影响，但多局限于自由场条件，未考虑更复杂的室内混响环境。为此，本文提出一种基于学习的定向SFANC方法，将噪声源的方位角与仰角信息融入混响环境中的降噪框架。该方法利用卷积神经网络（CNN）处理多路参考信号，同时估计噪声源的方位角与仰角，并选择最合适的控制滤波器以实现有效降噪。相比传统自适应算法，所提方法在存在混响的情况下仍能实现更优的降噪效果，且响应时间更短。
原文摘要 · Abstract (English)
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.
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