Intelligent Song Recognition via a Hollow-Microstructure-Based, Ultrasensitive Artificial Eardrum.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
PMID: 39301887

Abstract

Artificial ears with intelligence, which can sensitively detect sound-a variant of pressure-and generate consciousness and logical decision-making abilities, hold great promise to transform life. However, despite the emerging flexible sensors for sound detection, most success is limited to very simple phonemes, such as a couple of letters or words, probably due to the lack of device sensitivity and capability. Herein, the construction of ultrasensitive artificial eardrums enabling intelligent song recognition is reported. This strategy employs novel geometric engineering of sensing units in the soft microstructure array (to significantly reduce effective modulus) along with complex song recognition exploration leveraging machine learning algorithms. Unprecedented pressure sensitivity (6.9 × 10 kPa) is demonstrated in a sensor with a hollow pyramid architecture with porous slants. The integrated device exhibits unparalleled (exceeding by 1-2 orders of magnitude compared with reported benchmark samples) sound detection sensitivity, and can accurately identify 100% (for training set) and 97.7% (for test set) of a database of the segments from 77 songs varying in language, style, and singer. Overall, the results highlight the outstanding performance of the hollow-microstructure-based sensor, indicating its potential applications in human-machine interaction and wearable acoustical technologies.

Authors

  • Shaopeng Li
    State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China.
  • Jiangtao Tian
    School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
  • Ke Li
    School of Ideological and Political Education, Shanghai Maritime University, Shanghai, China.
  • Kemeng Xu
    School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710060, China.
  • Jiaqi Zhang
  • Tingting Chen
    Department of Hygiene Detection Center, School of Public Health, Southern Medical University (Guangdong Provincial Key Laboratory of Tropical Disease Research), Guangzhou, Guangdong, China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Hongbo Wang
    1Department of Biological Engineering, Utah State University, 4105 Old Main Hill, Logan, UT 84322-4105 USA.
  • Qiye Wu
    State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China.
  • Jinchun Xie
    State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China.
  • Yongjun Men
    State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China.
  • Weiping Liu
  • Xiaodan Zhang
    Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agriculture University, Hefei, 230001, China.
  • Wenhan Cao
    Department of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510120, China.
  • Zhongjie Huang
    State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, China.

Keywords

External Resources

Popular Topics
Recent Journals