eCite Digital Repository

Bioacoustics data analysis - a taxonomy, survey and open challenges

Citation

KVSN, RR and Montgomery, J and Garg, S and Charleston, M, Bioacoustics data analysis - a taxonomy, survey and open challenges, IEEE Access, 8 pp. 57684-57708. ISSN 2169-3536 (2020) [Refereed Article]


Preview
PDF
7Mb
  

Copyright Statement

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

DOI: doi:10.1109/ACCESS.2020.2978547

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 Details

Item Type:Refereed Article
Keywords:bioacoustics, biodiversity, density estimation, species identification, features, syllables, ecoacoustics, machine learning
Research Division:Environmental Sciences
Research Group:Environmental management
Research Field:Conservation and biodiversity
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Terrestrial biodiversity
UTAS Author:KVSN, RR (Mr Rama Rao Kaluri)
UTAS Author:Montgomery, J (Dr James Montgomery)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Charleston, M (Professor Michael Charleston)
ID Code:138948
Year Published:2020
Deposited By:Information and Communication Technology
Deposited On:2020-05-13
Last Modified:2020-08-19
Downloads:6 View Download Statistics

Repository Staff Only: item control page