Deep Learning-Based Active Trim Panels for Enhanced Aircraft Interior Noise Control

Abstract

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.