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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Bibliographic Details
Published in:Ecology and Evolution
Main Authors: Cominelli, Simone, Bellin, Nicolo', Brown, Carissa D., Rossi, Valeria, Lawson, Jack
Other Authors: Università degli Studi di Parma, Fisheries and Oceans Canada
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2024
Subjects:
Online Access:http://dx.doi.org/10.1002/ece3.10951
https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.10951
Description
Summary: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.