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A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques

Author

Listed:
  • Sandhya Sharma

    (The Department of Engineering, Muroran Institute of Technology, Muroran 050-0071, Japan)

  • Kazuhiko Sato

    (The Department of Engineering, Muroran Institute of Technology, Muroran 050-0071, Japan)

  • Bishnu Prasad Gautam

    (The Department of Economic Informatics, Kanazawa Gakuin University, Kanazawa 920-1392, Japan)

Abstract

Artificial intelligence (AI) has become a significantly growing field in the environmental sector due to its ability to solve problems, make decisions, and recognize patterns. The significance of AI in wildlife acoustic monitoring is particularly important because of the vast amounts of data that are available in this field, which can be leveraged for computer vision and interpretation. Despite the increasing use of AI in wildlife ecology, its future in acoustic wildlife monitoring remains uncertain. To assess its potential and identify future needs, a scientific literature review was conducted on 54 works published between 2015 and March 2022. The results of the review showed a significant rise in the utilization of AI techniques in wildlife acoustic monitoring over this period, with birds (N = 26) gaining the most popularity, followed by mammals (N = 12). The most commonly used AI algorithm in this field was Convolutional Neural Network, which was found to be more accurate and beneficial than previous categorization methods in acoustic wildlife monitoring. This highlights the potential for AI to play a crucial role in advancing our understanding of wildlife populations and ecosystems. However, the results also show that there are still gaps in our understanding of the use of AI in wildlife acoustic monitoring. Further examination of previously used AI algorithms in bioacoustics research can help researchers better understand patterns and identify areas for improvement in autonomous wildlife monitoring. In conclusion, the use of AI in wildlife acoustic monitoring is a rapidly growing field with a lot of potential. While significant progress has been made in recent years, there is still much to be done to fully realize the potential of AI in this field. Further research is needed to better understand the limitations and opportunities of AI in wildlife acoustic monitoring, and to develop new algorithms that can improve the accuracy and usefulness of this technology.

Suggested Citation

  • Sandhya Sharma & Kazuhiko Sato & Bishnu Prasad Gautam, 2023. "A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7128-:d:1131670
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    References listed on IDEAS

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    1. Hjalmar K Turesson & Sidarta Ribeiro & Danillo R Pereira & João P Papa & Victor Hugo C de Albuquerque, 2016. "Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-14, September.
    2. Ivan Braga Campos & Todd J Landers & Kate D Lee & William George Lee & Megan R Friesen & Anne C Gaskett & Louis Ranjard, 2019. "Assemblage of Focal Species Recognizers—AFSR: A technique for decreasing false indications of presence from acoustic automatic identification in a multiple species context," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-14, December.
    3. Oisin Mac Aodha & Rory Gibb & Kate E Barlow & Ella Browning & Michael Firman & Robin Freeman & Briana Harder & Libby Kinsey & Gary R Mead & Stuart E Newson & Ivan Pandourski & Stuart Parsons & Jon Rus, 2018. "Bat detective—Deep learning tools for bat acoustic signal detection," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-19, March.
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