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More than a whistle: automated detection of marine sound sources with a convolutional neural network

More than a whistle: automated detection of marine sound sources with a convolutional neural network
More than a whistle: automated detection of marine sound sources with a convolutional neural network
The effective analysis of Passive Acoustic Monitoring (PAM) data has the potential to determine spatial and temporal variations in ecosystem health and species presence if automated detection and classification algorithms are capable of discrimination between marine species and the presence of anthropogenic and environmental noise. Extracting more than a single sound source or call type will enrich our understanding of the interaction between biological, anthropogenic and geophonic soundscape components in the marine environment. Advances in extracting ecologically valuable cues from the marine environment, embedded within the soundscape, are limited by the time required for manual analyses and the accuracy of existing algorithms when applied to large PAM datasets. In this work, a deep learning model is trained for multi-class marine sound source detection using cloud computing to explore its utility for extracting sound sources for use in marine mammal conservation and ecosystem monitoring. A training set is developed comprising existing datasets amalgamated across geographic, temporal and spatial scales, collected across a range of acoustic platforms. Transfer learning is used to fine-tune an open-source state-of-the-art ‘small-scale’ convolutional neural network (CNN) to detect odontocete tonal and broadband call types and vessel noise (from 0 to 48 kHz). The developed CNN architecture uses a custom image input to exploit the differences in temporal and frequency characteristics between each sound source. Each sound source is identified with high accuracy across various test conditions, including variable signal-to-noise-ratio. We evaluate the effect of ambient noise on detector performance, outlining the importance of understanding the variability of the regional soundscape for which it will be deployed. Our work provides a computationally low-cost, efficient framework for mining big marine acoustic data, for information on temporal scales relevant to the management of marine protected areas and the conservation of vulnerable species.
CNN - convolutional neural network, Delphinids, efficientNet-B0, marine mammal acoustics, marine soundscapes, passive acoustic monitoring, sound source detection
2296-7745
White, Ellen Louise
1f019923-787e-4d89-9069-2f0b1ecf3506
White, Paul
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Bull, Jonathan
974037fd-544b-458f-98cc-ce8eca89e3c8
Risch, Denise
ac397a82-74f9-4305-956f-edd2ea5b2e3c
Beck, Suzanne
149c3da0-9319-4959-86f0-76d245cd4b6b
Edwards, Ewan
4a2832bf-deed-4b5b-a581-1bf4a5d50fdf
White, Ellen Louise
1f019923-787e-4d89-9069-2f0b1ecf3506
White, Paul
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Bull, Jonathan
974037fd-544b-458f-98cc-ce8eca89e3c8
Risch, Denise
ac397a82-74f9-4305-956f-edd2ea5b2e3c
Beck, Suzanne
149c3da0-9319-4959-86f0-76d245cd4b6b
Edwards, Ewan
4a2832bf-deed-4b5b-a581-1bf4a5d50fdf

White, Ellen Louise, White, Paul, Bull, Jonathan, Risch, Denise, Beck, Suzanne and Edwards, Ewan (2022) More than a whistle: automated detection of marine sound sources with a convolutional neural network. Frontiers in Marine Science, 9, [879145]. (doi:10.3389/fmars.2022.879145).

Record type: Article

Abstract

The effective analysis of Passive Acoustic Monitoring (PAM) data has the potential to determine spatial and temporal variations in ecosystem health and species presence if automated detection and classification algorithms are capable of discrimination between marine species and the presence of anthropogenic and environmental noise. Extracting more than a single sound source or call type will enrich our understanding of the interaction between biological, anthropogenic and geophonic soundscape components in the marine environment. Advances in extracting ecologically valuable cues from the marine environment, embedded within the soundscape, are limited by the time required for manual analyses and the accuracy of existing algorithms when applied to large PAM datasets. In this work, a deep learning model is trained for multi-class marine sound source detection using cloud computing to explore its utility for extracting sound sources for use in marine mammal conservation and ecosystem monitoring. A training set is developed comprising existing datasets amalgamated across geographic, temporal and spatial scales, collected across a range of acoustic platforms. Transfer learning is used to fine-tune an open-source state-of-the-art ‘small-scale’ convolutional neural network (CNN) to detect odontocete tonal and broadband call types and vessel noise (from 0 to 48 kHz). The developed CNN architecture uses a custom image input to exploit the differences in temporal and frequency characteristics between each sound source. Each sound source is identified with high accuracy across various test conditions, including variable signal-to-noise-ratio. We evaluate the effect of ambient noise on detector performance, outlining the importance of understanding the variability of the regional soundscape for which it will be deployed. Our work provides a computationally low-cost, efficient framework for mining big marine acoustic data, for information on temporal scales relevant to the management of marine protected areas and the conservation of vulnerable species.

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Accepted/In Press date: 14 September 2022
Published date: 4 October 2022
Additional Information: Funding Information: This work was supported by the Natural Environmental Research Council [grant number NE/S007210/1]. The COMPASS project has been supported by the EU’s INTERREG VA Programme, managed by the Special EU Programmes Body. The views and opinions expressed in this document do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB). Publisher Copyright: Copyright © 2022 White, White, Bull, Risch, Beck and Edwards.
Keywords: CNN - convolutional neural network, Delphinids, efficientNet-B0, marine mammal acoustics, marine soundscapes, passive acoustic monitoring, sound source detection

Identifiers

Local EPrints ID: 471320
URI: http://eprints.soton.ac.uk/id/eprint/471320
ISSN: 2296-7745
PURE UUID: 28167dbc-38b9-4a1b-983e-5436cc88d218
ORCID for Paul White: ORCID iD orcid.org/0000-0002-4787-8713
ORCID for Jonathan Bull: ORCID iD orcid.org/0000-0003-3373-5807

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Date deposited: 03 Nov 2022 17:32
Last modified: 12 Jul 2024 01:34

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Contributors

Author: Paul White ORCID iD
Author: Jonathan Bull ORCID iD
Author: Denise Risch
Author: Suzanne Beck
Author: Ewan Edwards

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