A generic AI pipeline for identifying known species and discovering new species

Abstract

To counterattack the current biodiversity crisis, we need innovative tools and technologies that can greatly improve species identification and discovery at scale. Here, we describe a data analysis pipeline to automatically identify biological sound categories in soundscape data and accelerate the discovery of new species. The pipeline consists of two components: (1) Unsupervised audio event detection and clustering that enables automatic detection and categorization of biological sounds in large audio datasets and (2) supervised deep learning-based signal detection that enables automated cluster labeling and detection of known sound categories in new audio. We evaluated the results of our unsupervised analysis tools applied to a labeled dataset of ∼18 h of recordings from Panama. Experts assigned a label to each sound event with a max frequency of 6 kHz. Our results show that clusters generally had high homogeneity and completeness and indicate that our cluster analysis tool can successfully detect and group like-sounds together, improving data processing and species identification. In addition, we were able to implement these analytical tools in a user-friendly data analysis platform that will provide scientists, wildlife managers, conservationists, and public and private environmental organizations with the information they need to make informed conservation and management decisions.