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A platform for research: civil engineering, architecture and urbanism
Deep learning-based active noise control on construction sites
Abstract Although construction noise pollution has always been a severe issue for governments in metropolitan cities, there is no effective and practical solution to control it. Due to the high nonlinear and transient nature of machinery noises on construction sites, traditional active noise control (ANC) algorithms show a marginal ability to mitigate them. In this paper, a novel high-performance deep-learning-based feedforward ANC controller is proposed to attenuate construction-related noise by considering the delay and nonlinear behavior of acoustic devices. The developed network, with around 128,500 parameters, can be expanded to a multi-channel ANC method without increasing computational costs, and it is suitable for ANC in open space environments like construction sites. Broadband noise attenuation of around 8.3 dB was achieved of a wide variety of construction noises with minor degradation at very high-frequency ranges (7.5–8 kHz). The presented network outperformed traditional and state-of-the-art ANC algorithms.
Highlights Deep learning-based active noise cancelation (CsNNet) was developed to mitigate various construction site noises. By incorporating LSTM and attention module, CsNNet extracts effective and robust features at a minimal computational cost. CsNNet mitigates impulsive construction noises and predict future canceling signals, satisfying causality constraints. CsNNet showed state-of-the-art performance in canceling all 17 construction noises.
Deep learning-based active noise control on construction sites
Abstract Although construction noise pollution has always been a severe issue for governments in metropolitan cities, there is no effective and practical solution to control it. Due to the high nonlinear and transient nature of machinery noises on construction sites, traditional active noise control (ANC) algorithms show a marginal ability to mitigate them. In this paper, a novel high-performance deep-learning-based feedforward ANC controller is proposed to attenuate construction-related noise by considering the delay and nonlinear behavior of acoustic devices. The developed network, with around 128,500 parameters, can be expanded to a multi-channel ANC method without increasing computational costs, and it is suitable for ANC in open space environments like construction sites. Broadband noise attenuation of around 8.3 dB was achieved of a wide variety of construction noises with minor degradation at very high-frequency ranges (7.5–8 kHz). The presented network outperformed traditional and state-of-the-art ANC algorithms.
Highlights Deep learning-based active noise cancelation (CsNNet) was developed to mitigate various construction site noises. By incorporating LSTM and attention module, CsNNet extracts effective and robust features at a minimal computational cost. CsNNet mitigates impulsive construction noises and predict future canceling signals, satisfying causality constraints. CsNNet showed state-of-the-art performance in canceling all 17 construction noises.
Deep learning-based active noise control on construction sites
Mostafavi, Alireza (author) / Cha, Young-Jin (author)
2023-04-15
Article (Journal)
Electronic Resource
English
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