Bioacoustic approaches to biodiversity monitoring and conservation in Kenya

Abstract

Kenya's rich biodiversity faces a number of threats including human encroachment, poaching and climate change. Since Kenya is a developing country, there is need to manage the sometimes competing interests of development, such as infrastructure development, and conservation. To achieve this, tools to effectively monitor the state of Kenya's various ecosystems are essential. In this paper we propose a biodiversity monitoring software tool that integrates acoustic indices of biodiversity, recognition of species of interest based on their vocalizations and acoustic census. This tool can be used by non-experts to determine the current state of their ecosystems by monitoring the state of bird species that serve as indicator taxa and whose abundance is related to the abundance of other terrestrial vertebrates including the “big five”. The tool we propose exploits state-of-the art advances in signal processing and machine learning to perform biodiversity monitoring, bird species detection and census in a joint framework. Using publicly available data we demonstrate how current acoustic indices of biodiversity can be improved by incorporating machine learning based audio segmentation algorithms. We also show how open source toolkits can be used to build bird species recognition systems. Code to reproduce the experiments in this paper is available on Github at https://github.com/ciiram/BirdPy.