Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems [article]

Sarab S. Sethi, Nick S. Jones, Ben D. Fulcher, Lorenzo Picinali, Dena J. Clink, Holger Klinck, C. David L. Orme, Peter H. Wrege, Robert M. Ewers
2019 biorxiv/medrxiv   pre-print
Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labour-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we developed a generalisable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed ecosystem soundscapes from a wide variety of biomes into a common acoustic space. In both supervised and unsupervised
more » ... odes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, paving the way for real-time detection of irregular environmental behaviour including illegal activity. Our highly generalisable approach, and the common set of features, will enable scientists to unlock previously hidden insights from eco-acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.
doi:10.1101/865980 fatcat:ukkix2iggbghxh2nvgi7ld4je4