Deep learning-based active noise control for airplane cockpit

VINTEC声学专注于为与声学相关的各种工程问题提供解决方案，包括高性能音响设备设计以及噪音和振动的控制。建筑工地、汽车或飞机等工业领域会产生持续的噪声，需要有效地降低这种噪声，以提高工人的安全和工作效率。有两种方法可以抑制噪声。第一种是被动噪声控制(PNC)，它使用物理上阻挡或吸收声音的结构。然而，PNC提供的隔音性能有限，而且可能非常昂贵。第二种选择是主动噪声控制(ANC)，它通过产生与噪声具有相反相位的抗噪声信号来消除源声音中不需要的噪声。众所周知，ANC在去除低频段噪声方面很有效，但其传统算法往往是不可靠的滤波器，不能准确捕获非线性和具有复杂模式的噪声源。为了解决这个问题，基于深度神经网络的滤波器正在作为一种替代方法出现，因为它们能够对非线性系统进行有效的建模。卷积神经网络(CNN)作为一种声学模型的噪声抑制主要应用于自动语音识别和语音质量增强。然而，基于深度学习的工业应用噪声控制的发展却鲜有人关注。在这个项目中，申请人和VINTEC声学公司的目标是开发一种ANC方法和一种新的算法，该方法使用先进的人工智能技术(即深度学习)，专门用于消除飞机驾驶舱的噪音。深层CNN是从输入声音中检测和估计噪声频谱特征的核心模块。还有必要构建一种新的轻量级CNN结构，以降低计算成本，因为飞机降噪需要实时处理。在飞机上，任何相移(时间延迟)都可能产生灾难性的后果。预计本项目开发的系统将作为需要实时降噪技术的各种工业领域的模板。**

Vintec Acoustics focuses on providing solutions to various engineering problems related to acoustics, including high-performance sound equipment design and control of noise and vibration. Industrial fields such as construction sites, automobiles or aircraft generate continuous noise, and this noise needs to be effectively reduced in order to improve workers' safety and work efficiency. There are two approaches to noise suppression. The first is passive noise control (PNC), which uses structures that physically block or absorb sound. However, PNC offers limited soundproofing performance, and can be very expensive. The second option is active noise control (ANC), which cancels the unwanted noise from the source sound by generating anti-noise signals which have the opposite phase to the noise. ANC is well-known to be effective at removing noise in the low-frequency band, but its traditional algorithms tend to be unreliable filters that do not accurately capture noise sources that are nonlinear and have complex patterns. To address this issue, filters based on deep neural networks are emerging as an alternative approach, because they are known to enable effective modeling for nonlinear systems. Noise suppression using convolutional neural networks (CNNs) as an acoustic model has been applied mainly in automatic speech recognition and voice quality enhancement. However, the development of deep learning-based noise control for industrial applications has received little attention. In this project, the applicant and Vintec Acoustics aim to develop an ANC method with a new algorithm that uses advanced artificial intelligence techniques (i.e., deep learning) and is specialized in eliminating noise from aircraft cockpits. Deep CNNs are core modules to detect and estimate the spectral features of the noise from an input sound. It will also be necessary to build a new lightweight CNN structure that can reduce computational costs, because aircraft noise reduction requires real-time processing. In an aircraft, any phase shift (time delay) could have catastrophic consequences. It is expected that the system developed in this project will serve as a template for various industrial fields requiring real-time noise reduction technology.**