Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59523-6 |
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| author | 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 Xi Xie |
| author_facet | 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 Xi Xie |
| author_sort | Chuanjie Yao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7bab417113ff40609e037db534d0a686 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-7bab417113ff40609e037db534d0a6862025-08-20T03:52:53ZengNature PortfolioNature Communications2041-17232025-05-0116112410.1038/s41467-025-59523-6Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environmentsChuanjie Yao0Suhang Liu1Zhengjie Liu2Shuang Huang3Tiancheng Sun4Mengyi He5Gemin Xiao6Han Ouyang7Yu Tao8Yancong Qiao9Mingqiang Li10Zhou Li11Peng Shi12Hui-jiuan Chen13Xi Xie14State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen UniversityState Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen UniversityState Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen UniversityState Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen UniversityState Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen UniversityState Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen UniversityThe Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen UniversitySchool of Nanoscience and Engineering, University of Chinese Academy of SciencesThe Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen UniversitySchool of Biomedical Engineering, Sun Yat-Sen UniversityThe Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen UniversityBeijing Institute of Nanoenergy and Nanosystems, Chinese Academy of SciencesDepartment of Biomedical Engineering, The City University of Hong KongState Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen UniversityState Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen UniversityAbstract 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.https://doi.org/10.1038/s41467-025-59523-6 |
| spellingShingle | 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 Xi Xie Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments Nature Communications |
| title | Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments |
| title_full | Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments |
| title_fullStr | Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments |
| title_full_unstemmed | Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments |
| title_short | Deep learning-enhanced anti-noise triboelectric acoustic sensor for human-machine collaboration in noisy environments |
| title_sort | deep learning enhanced anti noise triboelectric acoustic sensor for human machine collaboration in noisy environments |
| url | https://doi.org/10.1038/s41467-025-59523-6 |
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