用神经网络预测移动噪声方向，提前选降噪滤波器。
Predictive Directional Selective Fixed-Filter Active Noise Control for Moving Sources via a Convolutional Recurrent Neural Network
- 用卷积循环网络捕捉噪声方向的动态变化
- 预测未来噪声方向，提前调整降噪滤波器
- 适合处理移动声源的实时主动降噪场景
定向选择固定滤波主动降噪（D-SFANC）通过根据当前噪声的到达方向（DoA）选择预训练的控制滤波器，有效抑制不同方向的噪声。然而，该方法在跟踪非平稳噪声（如移动声源）的方向变化时表现较弱。为此，本文提出一种预测性定向SFANC（PD-SFANC）方法，利用卷积循环神经网络（CRNN）捕捉移动噪声的隐含时间动态，并预测未来的控制滤波器以抵消后续噪声。实验结果表明，该方法在多种移动场景下显著提升了噪声追踪能力与动态降噪性能，优于多个代表性主动降噪基线方法。
原文摘要 · Abstract (English)
Directional Selective Fixed-Filter Active Noise Control (D-SFANC) can effectively attenuate noise from different directions by selecting the suitable pre-trained control filter based on the Direction-of-Arrival (DoA) of the current noise. However, this method is weak at tracking the direction variations of non-stationary noise, such as that from a moving source. Therefore, this work proposes a Predictive Directional SFANC (PD-SFANC) method that uses a Convolutional Recurrent Neural Network (CRNN) to capture the hidden temporal dynamics of the moving noise and predict the control filter to cancel future noise. Accordingly, the proposed method can significantly improve its noise-tracking ability and dynamic noise-reduction performance. Furthermore, numerical simulations confirm the superiority of the proposed method for handling moving sources across various movement scenarios, compared to several representative ANC baselines.
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