Chalmers, C, Fergus, P, Wich, SA and Longmore, SN (2021) Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning. In: International Joint Conference on Neural Networks . (International Joint Conference on Neural Networks Virtual Event, 18-22 July 2021, Virtual Event).

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Abstract

For centuries researchers have used sound to monitor and study wildlife. Traditionally, conservationists have identified species by ear; however, it is now common to deploy audio recording technology to monitor animal and ecosystem sounds. Animals use sound for communication, mating, navigation and territorial defence. Animal sounds provide valuable information and help conservationists to quantify biodiversity. Acoustic monitoring has grown in popularity due to the availability of diverse sensor types which include camera traps, portable acoustic sensors, passive acoustic sensors, and even smartphones. Passive acoustic sensors are easy to deploy and can be left running for long durations to provide insights on habitat and the sounds made by animals and illegal activity. While this technology brings enormous benefits, the amount of data that is generated makes processing a time-consuming process for conservationists. Consequently, there is interest among conservationists to automatically process acoustic data to help speed up biodiversity assessments. Processing these large data sources and extracting relevant sounds from background noise introduces significant challenges. In this paper we outline an approach for achieving this using state of the art in machine learning to automatically extract features from time-series audio signals and modelling deep learning models to classify different bird species based on the sounds they make. The acquired bird songs are processed using mel-frequency cepstrum (MFC) to extract features which are later classified using a multilayer perceptron (MLP). Our proposed method achieved promising results with 0.74 sensitivity, 0.92 specificity and an accuracy of 0.74.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	cs.SD; cs.SD; cs.LG; eess.AS
Subjects:	Q Science > QB Astronomy Q Science > QC Physics Q Science > QH Natural history > QH301 Biology Q Science > QL Zoology
Divisions:	Astrophysics Research Institute Biological & Environmental Sciences (from Sep 19) Computer Science & Mathematics
Publisher:	IEEE
Related URLs:	http://arxiv.org/abs/2103.07276v1
Date Deposited:	14 Apr 2021 09:58
Last Modified:	12 Jan 2023 16:42
DOI or ID number:	10.1109/IJCNN52387.2021.9534195
URI:	https://researchonline.ljmu.ac.uk/id/eprint/14693

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