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

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 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 for establishing a link between ecologically relevant information
and PAM recordings at multiple scales. The datasets and scripts provided
in this repository allow replicating the results presented in the
publication.