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Using software-based acoustic detection and supporting tools to enable large-scale environmental monitoring

Using software-based acoustic detection and supporting tools to enable large-scale environmental monitoring
Using software-based acoustic detection and supporting tools to enable large-scale environmental monitoring
Acoustic monitoring tools are often constrained to small-scale, short-term studies due to high energy consumption, limited storage, and high equipment costs. To broaden the scope of monitoring projects, affordability, energy efficiency, and space efficiency must be improved on such tools. This thesis describes efforts to empower researchers charged with monitoring ecosystems, faced with the challenges of limited budgets and cryptic targeted events. To this end AudioMoth was developed: a low-cost, open-source acoustic monitoring device which has been widely adopted by the conservation community, with over 6,600 devices sold as of August 2019.

This thesis covers the development and deployment of three acoustic detection algorithms that reduce the power and storage requirements of acoustic monitoring. The algorithms aim to detect bat echolocation, to search for evidence of a endangered cicada species, and to collect evidence of poaching in a protected nature reserve. Each algorithm addresses a detection task of increasing complexity - analysing samples multiple times to prevent missed events, implementing extra analytical steps to account for environmental conditions such as wind, and incorporating a hidden Markov model for sample classification in both the time and frequency domain. For each algorithm this thesis reports on their detection accuracy as well as real-world deployments carried out with partner organisations. The deployments demonstrate how acoustic detection algorithms extend the use of low-cost, open-source hardware and facilitate a new avenue for conservation researchers to perform large-scale monitoring.

The research also covers an analysis of the accessibility of acoustic monitoring technology, focusing on AudioMoth and its supporting software. This is done using a 75-respondent questionnaire and a thematic analysis done on a series of interviews. The results of both analyses discovered a number of potential methods for improving acoustic monitoring technology in terms of the various forms of accessibility (financial, usability, etc.). The community responses, along with the popularity of AudioMoth and the success of the deployed detection algorithms demonstrate the benefits of providing accessible acoustic monitoring solutions to conservationists.
University of Southampton
Prince, Peter
13940cd1-98ab-4dca-a9ce-2403b2e61daa
Prince, Peter
13940cd1-98ab-4dca-a9ce-2403b2e61daa
Stein, Sebastian
fb227373-7242-4982-b84b-90bc79617a50

Prince, Peter (2019) Using software-based acoustic detection and supporting tools to enable large-scale environmental monitoring. University of Southampton, Doctoral Thesis, 169pp.

Record type: Thesis (Doctoral)

Abstract

Acoustic monitoring tools are often constrained to small-scale, short-term studies due to high energy consumption, limited storage, and high equipment costs. To broaden the scope of monitoring projects, affordability, energy efficiency, and space efficiency must be improved on such tools. This thesis describes efforts to empower researchers charged with monitoring ecosystems, faced with the challenges of limited budgets and cryptic targeted events. To this end AudioMoth was developed: a low-cost, open-source acoustic monitoring device which has been widely adopted by the conservation community, with over 6,600 devices sold as of August 2019.

This thesis covers the development and deployment of three acoustic detection algorithms that reduce the power and storage requirements of acoustic monitoring. The algorithms aim to detect bat echolocation, to search for evidence of a endangered cicada species, and to collect evidence of poaching in a protected nature reserve. Each algorithm addresses a detection task of increasing complexity - analysing samples multiple times to prevent missed events, implementing extra analytical steps to account for environmental conditions such as wind, and incorporating a hidden Markov model for sample classification in both the time and frequency domain. For each algorithm this thesis reports on their detection accuracy as well as real-world deployments carried out with partner organisations. The deployments demonstrate how acoustic detection algorithms extend the use of low-cost, open-source hardware and facilitate a new avenue for conservation researchers to perform large-scale monitoring.

The research also covers an analysis of the accessibility of acoustic monitoring technology, focusing on AudioMoth and its supporting software. This is done using a 75-respondent questionnaire and a thematic analysis done on a series of interviews. The results of both analyses discovered a number of potential methods for improving acoustic monitoring technology in terms of the various forms of accessibility (financial, usability, etc.). The community responses, along with the popularity of AudioMoth and the success of the deployed detection algorithms demonstrate the benefits of providing accessible acoustic monitoring solutions to conservationists.

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Published date: October 2019

Identifiers

Local EPrints ID: 438947
URI: http://eprints.soton.ac.uk/id/eprint/438947
PURE UUID: 5bc36239-75d1-4016-9973-26d5ebec971e

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Date deposited: 27 Mar 2020 17:30
Last modified: 27 Mar 2020 17:30

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Contributors

Author: Peter Prince
Thesis advisor: Sebastian Stein

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