Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets
Abstract Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suit...
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crwiley:10.1002/ece3.10951 2024-06-02T08:07:56+00:00 Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets Cominelli, Simone Bellin, Nicolo' Brown, Carissa D. Rossi, Valeria Lawson, Jack Università degli Studi di Parma Fisheries and Oceans Canada 2024 http://dx.doi.org/10.1002/ece3.10951 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.10951 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Ecology and Evolution volume 14, issue 2 ISSN 2045-7758 2045-7758 journal-article 2024 crwiley https://doi.org/10.1002/ece3.10951 2024-05-03T11:20:58Z Abstract Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine‐learning techniques to the analysis of two PAM datasets. We combine pre‐trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine‐learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low‐ and high‐frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large‐scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species. Article in Journal/Newspaper Humpback Whale Wiley Online Library Ecology and Evolution 14 2 |
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Wiley Online Library |
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English |
description |
Abstract Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine‐learning techniques to the analysis of two PAM datasets. We combine pre‐trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine‐learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low‐ and high‐frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large‐scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species. |
author2 |
Università degli Studi di Parma Fisheries and Oceans Canada |
format |
Article in Journal/Newspaper |
author |
Cominelli, Simone Bellin, Nicolo' Brown, Carissa D. Rossi, Valeria Lawson, Jack |
spellingShingle |
Cominelli, Simone Bellin, Nicolo' Brown, Carissa D. Rossi, Valeria Lawson, Jack Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets |
author_facet |
Cominelli, Simone Bellin, Nicolo' Brown, Carissa D. Rossi, Valeria Lawson, Jack |
author_sort |
Cominelli, Simone |
title |
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets |
title_short |
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets |
title_full |
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets |
title_fullStr |
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets |
title_full_unstemmed |
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets |
title_sort |
acoustic features as a tool to visualize and explore marine soundscapes: applications illustrated using marine mammal passive acoustic monitoring datasets |
publisher |
Wiley |
publishDate |
2024 |
url |
http://dx.doi.org/10.1002/ece3.10951 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.10951 |
genre |
Humpback Whale |
genre_facet |
Humpback Whale |
op_source |
Ecology and Evolution volume 14, issue 2 ISSN 2045-7758 2045-7758 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/ece3.10951 |
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Ecology and Evolution |
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14 |
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2 |
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1800753085904060416 |