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Bioacoustics data analysis - a taxonomy, survey and open challenges

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KVSN, RR, Montgomery, J ORCID: 0000-0002-5360-7514, Garg, S ORCID: 0000-0003-3510-2464 and Charleston, M ORCID: 0000-0001-8385-341X 2020 , 'Bioacoustics data analysis - a taxonomy, survey and open challenges' , IEEE Access, vol. 8 , pp. 57684-57708 , doi: 10.1109/ACCESS.2020.2978547.

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Abstract

Biodiversity monitoring has become a critical task for governments and ecological research agencies for reducing significant loss of animal species. Existing monitoring methods are time-intensive and techniques such as tagging are also invasive and may adversely affect animals. Bioacoustics based monitoring is becoming an increasingly prominent non-invasive method, involving the passive recording of animal sounds. Bioacoustics analysis can provide deep insights into key environmental integrity issues such as biodiversity, density of individuals and present or absence of species. However, analysing environmental recordings is not a trivial task. In last decade several researchers have tried to apply machine learning methods to automatically extract insights from these recordings. To help current researchers and identify research gaps, this paper aims to summarise and classify these works in the form of a taxonomy of the various bioacoustics applications and analysis approaches. We also present a comprehensive survey of bioacoustics data analysis approaches with an emphasis on bird species identification. The survey first identifies common processing steps to analyse bioacoustics data. As bioacoustics monitoring has grown, so does the volume of raw acoustic data that must be processed. Accordingly, this survey examines how bioacoustics analysis techniques can be scaled to work with big data. We conclude with a review of open challenges in the bioacoustics domain, such as multiple species recognition, call interference and automatic selection of detectors.

Item Type: Article
Authors/Creators:KVSN, RR and Montgomery, J and Garg, S and Charleston, M
Keywords: bioacoustics, biodiversity, density estimation, species identification, features, syllables, ecoacoustics, machine learning
Journal or Publication Title: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 2169-3536
DOI / ID Number: 10.1109/ACCESS.2020.2978547
Copyright Information:

Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

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