Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets ...

• Main Authors: Cominelli, Simone, Bellin, Nicolo', Brown, Carissa D., Lawson, Jack
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Zenodo 2024
• Subjects: Passive Acoustic Monitoring; UMAP; Marine mammals; random forest; Ecoacoustics; Humpback Whale
• Online Access: https://dx.doi.org/10.5281/zenodo.10019844; https://zenodo.org/doi/10.5281/zenodo.10019844

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, and indices are best suited for characterizing marine and terrestrial acoustic environments. Here, we describe the application of multiple machine-learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models (VGGish, NOAA & 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 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 labelled sounds in the 8 kHz ... : Funding provided by: Memorial University of NewfoundlandCrossref Funder Registry ID: https://ror.org/04haebc03Award Number: Funding provided by: Fisheries and Oceans CanadaCrossref Funder Registry ID: https://ror.org/02qa1x782Award Number: Funding provided by: University of ParmaCrossref Funder Registry ID: https://ror.org/02k7wn190Award Number: ...