BIOACOUSTICS AND MACHINE LEARNING FOR CONSERVATION

Abstract | Biodiversity is in precipitous decline around the globe a crisis that calls for the development of innovative, scalable methods for monitoring wildlife populations that can generate timely, actionable data. Passive acoustic monitoring which involves the remote collection of audio for later processing is emerging as a powerful tool at researchers’ disposal. Advances in machine learning are making it possible to process what used to be impractical amounts of audio data, allowing for the sustainable collection and automated analysis of audio at landscape scales With further refinement and with the development of workflows that make them accessible to those without programming experience–these novel methods and models are posed to greatly increase the capacity of researchers and conservation professionals to monitor wildlife populations around the globe. There are a number of potential ways acoustic monitoring could support banding efforts, such as facilitating better coverage for species counts during MAPS banding, identifying optimal locations for banding sites or nets within larger banding sites, as well as providing a rich resource of supplemental data about migration patterns and species distribution.

Bio | After living and working abroad for ten years and falling in love with the incredible variety of the natural world, I chose to pursue a master’s degree in Environmental Studies. My concentration is in Bioacoustics and Machine Learning for Conservation. My goal is to make use of cutting-edge technology and machine learning methods to support the conservation of biodiversity.