Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments

Chuanjie Yao, Suhang Liu, Zhengjie Liu, Shuang Huang, Tiancheng Sun, Mengyi He, Gemin Xiao, Han Ouyang, Yu Tao, Yancong Qiao, Mingqiang Li, Zhou Li, Peng Shi, Hui-jiuan Chen and Xi Xie ()
Additional contact information
Chuanjie Yao: Sun Yat-Sen University
Suhang Liu: Sun Yat-Sen University
Zhengjie Liu: Sun Yat-Sen University
Shuang Huang: Sun Yat-Sen University
Tiancheng Sun: Sun Yat-Sen University
Mengyi He: Sun Yat-Sen University
Gemin Xiao: Sun Yat-Sen University
Han Ouyang: University of Chinese Academy of Sciences
Yu Tao: Sun Yat-Sen University
Yancong Qiao: Sun Yat-Sen University
Mingqiang Li: Sun Yat-Sen University
Zhou Li: Chinese Academy of Sciences
Peng Shi: The City University of Hong Kong
Hui-jiuan Chen: Sun Yat-Sen University
Xi Xie: Sun Yat-Sen University

Nature Communications, 2025, vol. 16, issue 1, 1-24

Abstract: Abstract Human-machine voice interaction based on speech recognition offers an intuitive, efficient, and user-friendly interface, attracting wide attention in applications such as health monitoring, post-disaster rescue, and intelligent control. However, conventional microphone-based systems remain challenging for complex human-machine collaboration in noisy environments. Herein, an anti-noise triboelectric acoustic sensor (Anti-noise TEAS) based on flexible nanopillar structures is developed and integrated with a convolutional neural network-based deep learning model (Anti-noise TEAS-DLM). This highly synergistic system enables robust acoustic signal recognition for human-machine collaboration in complex, noisy scenarios. The Anti-noise TEAS directly captures acoustic fundamental frequency signals from laryngeal mixed-mode vibrations through contact sensing, while effectively suppressing environmental noise by optimizing device-structure buffering. The acoustic signals are subsequently processed and semantically decoded by the DLM, ensuring high-fidelity interpretation. Evaluated in both simulated virtual and real-life noisy environments, the Anti-noise TEAS-DLM demonstrates near-perfect noise immunity and reliably transmits various voice commands to guide robotic systems in executing complex post-disaster rescue tasks with high precision. The combined anti-noise robustness and execution accuracy endow this DLM-enhanced Anti-noise TEAS as a highly promising platform for next-generation human-machine collaborative systems operating in challenging noisy environments.

Date: 2025
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DOI: 10.1038/s41467-025-59523-6

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