@inproceedings{65bf0fcecfb14b7f861c039fa3493c5c,
title = "A Flexible Graphene Acoustic Sensor for Sound Signal Acquisition and Spiking Neural Network Recognition",
abstract = "As artificial intelligence continues to develop and mature, sound sensing and recognition technology has played a crucial role in fields such as human-computer interaction. This article fabricated a microstructure-based graphene acoustic sensor and used Spiking Neural Network (SNN) to identify the collected data. By combining a micro-pyramid structure on a flexible substrate, the as-fabricated sensor can cover the main frequency range of human sound (200-3000 Hz), display excellent mechanical sensitivity (S= 10.9 kPa-1) and fast response ability (5.8 ms), and can capture complex changes in sound. Converting sound signals into pulses can reduce losses during transmission, so a Spiking Neural Network is constructed to recognize sound datasets and an accuracy of 96.5\% is achieved. This paper provides the possibility for new applications of carbon-based acoustic sensors in intelligent sound signal recognition systems.",
keywords = "Flexible Sensor, Graphene Acoustic Sensor, Microstructure, Spiking Neural Network",
author = "Zhao, \{Lu Yu\} and Shen, \{Hao Yuan\} and Wu, \{Yi Wen\} and Zhang, \{Lu Lu\} and Li, \{Yu Tao\} and Ren, \{Tian Ling\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024 ; Conference date: 22-10-2024 Through 25-10-2024",
year = "2024",
doi = "10.1109/ICSICT62049.2024.10831368",
language = "English",
series = "2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Fan Ye and Xiaona Zhu and Tang, \{Ting Ao\}",
booktitle = "2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024",
address = "United States",
}