A Real-Time Platform for Portable and Scalable Active Noise Mitigation for Construction Machinery
<|reference_start|>A Real-Time Platform for Portable and Scalable Active Noise Mitigation for Construction Machinery: This paper introduces a novel portable and scalable Active Noise Mitigation (PSANM) system designed to reduce low-frequency noise from construction machinery. The PSANM system consists of portable units with autonomous capabilities, optimized for stable performance within a specific power range. An adaptive control algorithm with a variable penalty factor prevents the adaptive filter from over-driving the anti-noise actuators, avoiding non-linear operation and instability. This feature ensures the PSANM system can autonomously control noise at its source, allowing for continuous operation without human intervention. Additionally, the system includes a web server for remote management and is equipped with weather-resistant sensors and actuators, enhancing its usability in outdoor conditions. Laboratory and in-situ experiments demonstrate the PSANM system's effectiveness in reducing construction-related low-frequency noise on a global scale. To further expand the noise reduction zone, additional PSANM units can be strategically positioned in front of noise sources, enhancing the system's scalability.The PSANM system also provides a valuable prototyping platform for developing adaptive algorithms prior to deployment. Unlike many studies that rely solely on simulation results under ideal conditions, this paper offers a holistic evaluation of the effectiveness of applying active noise control techniques directly at the noise source, demonstrating realistic and perceptible noise reduction. This work supports sustainable urban development by offering innovative noise management solutions for the construction industry, contributing to a quieter and more livable urban environment.<|reference_end|>
@article{gan2024a,
    title={A Real-Time Platform for Portable and Scalable Active Noise Mitigation
  for Construction Machinery},
    author={Woon-Seng Gan, Santi Peksi, Chung Kwan Lai, Yen Theng Lee, Dongyuan
  Shi, Bhan Lam},
    journal={2024 Asia Pacific Signal and Information Processing Association
  Annual Summit and Conference (APSIPA ASC)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10534},
    primaryClass={eess.AS cs.SD}
}