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In this work, we consider applying machine learning to the analysis andcompression of audio signals in the context of monitoring elephants insub-Saharan Africa. Earth's biodiversity is increasingly under threat bysources of anthropogenic change (e.g. resource extraction, land use change, andclimate change) and surveying animal populations is critical for developingconservation strategies. However, manually monitoring tropical forests or deepoceans is intractable. For species that communicate acoustically, researchershave argued for placing audio recorders in the habitats as a cost-effective andnon-invasive method, a strategy known as passive acoustic monitoring (PAM). Incollaboration with conservation efforts, we construct a large labeled datasetof passive acoustic recordings of the African Forest Elephant viacrowdsourcing, compromising thousands of hours of recordings in the wild. Usingstate-of-the-art techniques in artificial intelligence we improve uponpreviously proposed methods for passive acoustic monitoring for classificationand segmentation. In real-time detection of elephant calls, network bandwidthquickly becomes a bottleneck and efficient ways to compress the data areneeded. Most audio compression schemes are aimed at human listeners and areunsuitable for low-frequency elephant calls. To remedy this, we provide a novelend-to-end differentiable method for compression of audio signals that can beadapted to acoustic monitoring of any species and dramatically improves overnaive coding strategies.

Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant