Deep learning-based active noise control for airplane cockpit

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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.**