用神经网络让飞机内饰降噪板自动适应温度变化，提升降噪效果。
Deep Learning-Based Active Trim Panels for Enhanced Aircraft Interior Noise Control
- 用一维卷积神经网络动态选择最佳降噪滤波器。
- 在不同频率和温度下均实现有效降噪，性能稳定。
- 适合需要实时响应的飞机舱内噪声控制场景。
主动噪声控制（ANC）内饰板能有效抑制飞机内部多频段噪声。选择性固定滤波器主动降噪（SFANC）方法具有计算量小、鲁棒性强、响应快的特点，适用于因发动机转速变化而频率波动的多频段发动机噪声。然而，实际环境中衬垫温度变化会改变声学与结构路径，导致降噪性能下降。为此，本文提出一种温感型SFANC（TP-SFANC）方法，采用轻量级一维卷积神经网络（1D CNN），通过多任务学习策略，同时处理参考信号与误差信号，学习频率与温度特征，动态选择最优控制滤波器。数值仿真表明，该方法可在不同频率与衬垫温度条件下有效抑制多频段噪声。
原文摘要 · Abstract (English)
Active noise control (ANC) trim panels offer an effective solution to suppress multi-tonal noise in aircraft. The selective fixed-filter ANC (SFANC) method, characterized by low computational complexity, high robustness and rapid response, is suitable to handle multi-tonal engine noise that varies in frequency due to changes in the rotational speed of the engine shaft. However, real-world conditions introduce variations in lining temperature, altering acoustic and structural paths and degrading noise reduction performance. To address this challenge, a temperature-perceptive SFANC (TP-SFANC) approach is proposed that employs a lightweight one-dimensional convolutional neural network (1D CNN) trained using a multi-task learning strategy. By processing both reference and error signals, the 1D CNN learns frequency and temperature characteristics to dynamically select the optimal control filter. Numerical simulations demonstrate the effectiveness of the proposed method in attenuating multi-tonal noise across varying frequencies and lining temperatures.
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