The use of BirdNET embeddings as a fast solution to find novel sound classes in audio recordings

Abstract

Passive acoustic monitoring has emerged as a useful technique for monitoring vocal species and contributing to biodiversity monitoring goals. However, finding target sounds for species without pre-existing recognisers still proves challenging. Here, we demonstrate how the embeddings from the large acoustic model BirdNET can be used to quickly and easily find new sound classes outside the original model’s training set. We outline the general workflow, and present three case studies covering a range of ecological use cases that we believe are common requirements in research and management: monitoring invasive species, generating species lists, and detecting threatened species. In all cases, a minimal amount of target class examples and validation effort was required to obtain results applicable to the desired application. The demonstrated success of this method across different datasets and different taxonomic groups suggests a wide applicability of BirdNET embeddings for finding novel sound classes. We anticipate this method will allow easy and rapid detection of sound classes for which no current recognisers exist, contributing to both monitoring and conservation goals.