Make the CPUs do the hard work - Automated acoustic feature extraction
and visualization for marine ecoacoustics applications illustrated using
marine mammal Passive Acoustic Monitoring datasets
Abstract
1. Passive Acoustic Monitoring 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 acoustic environments. 2. We
present an alternative to the use of ecoacoustic indices and describe
the application of multiple machine learning techniques to the analysis
of a large PAM dataset. We combine pre-trained acoustic classification
models, dimensionality reduction, and random forest algorithms to
demonstrate how machine-learned acoustic features capture different
aspects of the marine environment. We processed two PAM databases and
conducted 13 trials showing how acoustic features can be used to: i)
discriminate between the vocalizations of marine mammals, beginning with
high-level taxonomic groups, and extending to detecting differences
between conspecifics belonging to distinct populations; ii)
discriminating amongst different marine environments; and iii) detecting
and monitoring anthropogenic and biological sound sources. 3. Acoustic
features and their UMAP projections exhibited good performance in the
classification of marine mammal vocalizations. Most of the taxonomic
levels investigated here could be classified using the UMAP projections,
apart from species that were underrepresented. Both anthropogenic (ships
and airguns) and biological (humpback whales) sound sources could also
be identified in field recordings. 4. We argue that acoustic feature
extraction, visualization, and analysis allows the retention of most of
the environmental information contained in PAM recordings, overcoming
the limitations encountered when using ecoacoustics indices. Acoustic
features are universal, permitting comparisons of results collected from
multiple environments. Our approach can be used to simultaneously
investigate the macro and micro characteristics of marine soundscapes,
with a more objective method and with far less human effort.