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

Abstract

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earths biodiversity is increasingly under threat by sources of anthropogenic change (e.g. resource extraction, land use change, and climate change) and surveying animal populations is critical for developing conservation strategies. However, manually monitoring tropical forests or deep oceans is intractable. For species that communicate acoustically, researchers have argued for placing audio recorders in the habitats as a costeffective and non-invasive method, a strategy known as passive acoustic monitoring (PAM). In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild. Using state-of-the-art techniques in artificial intelligence we improve upon previously proposed methods for passive acoustic monitoring for classification and segmentation. In real-time detection of elephant calls, network bandwidth quickly becomes a bottleneck and efficient ways to compress the data are needed. Most audio compression schemes are aimed at human listeners and are unsuitable for low-frequency elephant calls. To remedy this, we provide a novel end-to-end differentiable method for compression of audio signals that can be adapted to acoustic monitoring of any species and dramatically improves over nive coding strategies.

AI Ethics and Value Alignment for Nonhuman Animals

“…Moreover, AI systems may be capable of harnessing those methods of animal welfare science which are not yet advanced, such as direct communication with nonhuman animals, which would enable self-reporting of values and interests [53], or the measurement of wellbeing through correlated brain processes [19]. The measurement of brain processes would provide for optimal functional indicators, while the above ones are mostly proxy indicators.…”

Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

“…There can also be extremely imbalanced data that makes accurate prediction difficult, e.g., in developing an ML pipeline to identify areas at high risk of poaching in the protected areas of Uganda (Gholami et al, 2019). The size of raw data can also be problematic, e.g., in recent attempts to monitor audio signals of African elephants with real-time ML methods to offer protection against poachers, network bandwidth limitations required efficient audio compression (Bjorck et al, 2019).…”

“…While exploring e.g. hyperspectral remote sensing data for ML applications, researchers should focus on addressing the problem of high dimensionality of data, build models invariant under different conditions, promote the use of unsupervised classification in the absence of ground truth data, and create and use new public standardized datasets (O'Connor et al, 2017;Gewali et al, 2018;Bjorck et al, 2019;Rolnick et al, 2019). Given the requirements of data needed to capture the forest complexity, we argue that predictive ML applications in forest management must be developed at a very fine scale as used by scholars (Kelling et al, 2013;Curtis et al, 2018;Norouzzadeh et al, 2018).…”

Estimating ecoacoustic activity in the Amazon rainforest through Information Theory quantifiers

“…Currently, most ecoacoustic recognition methods rely on supervised classifiers to automatically identify and catalog species. Researchers apply these methods to a variety of species, including frogs, birds, whales, dolphins, elephants, mosquitoes, gibbons, among others [7][8][9][10][11][12][13]. Fully automatic multi-species monitoring systems have also been proposed, integrating hardware and software on a single platform [14].…”