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Deep learning-assisted active noise control in a time-varying environment
Seonghun Im,Si Won Kim,Sunghwa Woo,Inman Jang,한태우,Uiwon Hwang,엄원석,이명한 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.3
The success of active noise control (ANC) is largely determined by the fidelity of the estimated secondary path, which encapsulates the "room acoustics" between the secondary sound source and the error sensor. In a time-invariant system the secondary path is usually measured and hard-coded in the controller prior to the ANC operation. When ANC is to be performed in a time-varying environment, however, the estimated secondary path should be updated accordingly, a task that poses many challenges in terms of efficacy, cost, and user comfort. In this paper we present a deep learning-assisted secondary path update technique, in which deep neural networks are trained to estimate the secondary path in real time according to changing boundary conditions. The feasibility of the technique is tested in an airborne duct, where the error sensor is allowed to move along the duct to simulate changes in boundary conditions. Results have shown that even in the face of a dramatic change in boundary conditions, the ANC system equipped with the present update scheme is capable of reducing broadband noise by up to 10 dB.
Sound visualization of smartphone-radiated noise using nearfield acoustic holography
Taeyoung Park,Seonghun Im,Dong-Hyun Kim,Sangbeom Woo,Inman Jang,Won-Suk Ohm,Heungkil Park,Sehun Park,Guwon Ji 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.8
A smartphone contains electronic components that can inadvertently act as sources of unwanted vibration and noise. Also known as the "buzz," the noise is produced primarily by multilayer ceramic capacitors, the piezoelectric pulsation of which drives the circuit board into vibration, hence sound radiation. Given the close proximity of the smartphone to the ear, the audible noise, albeit low in amplitude, can be a nuisance and degrade the call quality. Thus accurate measurement and analysis of the noise are needed as the first step toward resolving the problem. The popular farfield-based techniques, however, are not applicable to smartphone-radiated noise because of its low sound pressure level (~20 dB). This paper presents an alternative method based on nearfield acoustic holography (NAH), here tailored for a small mobile device radiating low-intensity noise. The NAH method is shown to be capable of visualizing sound pressure and intensity anywhere near the smartphone as well as the vibration of the circuit board, which could lead to an effective design strategy for a quieter smartphone.