Biodiversity loss is taking place at accelerated rates globally, and a business-as-usual trajectory will lead to missing internationally established conservation goals. Biosphere reserves are sites designed to be of global significance in terms of both the biodiversity within them and their potential for sustainable development, and are therefore ideal places for the development of local solutions to global challenges. While the protection of biodiversity is a primary goal of biosphere reserves, adequate information on the state and trends of biodiversity remains a critical gap for adaptive management in biosphere reserves. Passive acoustic monitoring (PAM) is an increasingly popular method for continued, reproducible, scalable, and cost-effective monitoring of animal wildlife. PAM adoption is on the rise, but its data management and analysis requirements pose a barrier for adoption for most agencies tasked with monitoring biodiversity. As an interdisciplinary team of machine learning scientists and ecologists experienced with PAM and working at biosphere reserves in marine and terrestrial ecosystems on three different continents, we report on the co-development of interactive machine learning tools for semi-automated assessment of animal wildlife.

Deep learning methods are well suited for data analysis in several domains, but application is often limited by technical entry barriers and the availability of large annotated datasets. We present an interactive machine learning tool for annotating passive acoustic monitoring datasets created for wildlife monitoring, which are time-consuming and costly to annotate manually. The tool, designed as a web application, consists of an interactive user interface implementing a human-in-the-loop workflow. Class label annotations provided manually as bounding boxes drawn over a spectrogram are consumed by a deep generative model (DGM) that learns a low-dimensional representation of the input data, as well as the available class labels. The learned low-dimensional representation is displayed as an interactive interface element, where new bounding boxes can be efficiently generated by the user with lasso-selection; alternatively, the DGM can propose new, automatically generated bounding boxes on demand. The user can accept, edit, or reject annotations suggested by the model, thus owning final judgement. Generated annotations can be used to fine-tune the underlying model, thus closing the loop. Investigations of the prediction accuracy and first empirical experiments show promising results on an artificial data set, laying the ground for application to a real life scenario.