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

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

Blog Article

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 sheepshead bay boats 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 click here 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.