Document Type

Journal Article

Publication Title

IEEE Access

Volume

11

First Page

60078

Last Page

60108

Publisher

IEEE

School

School of Engineering

RAS ID

60092

Funders

Department of Jobs, Tourism, Science and Innovation, Defence Science Center, Australia

Comments

Zaheer, R., Ahmad, I., Habibi, D., Islam, K. Y., & Phung, Q, V. (2023). A survey on artificial intelligence-based acoustic source identification. IEEE Access, 11, 60078-60108. https://doi.org/10.1109/ACCESS.2023.3283982

Abstract

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

DOI

10.1109/ACCESS.2023.3283982

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Download

Share

 
COinS