Acoustic Censusing and Individual Identification of Birds in the Wild

Abstract

Avian vocal individuality carries information that can be utilized as an alternative to physical tagging of individuals. However, it is rarely used in conservation tasks despite rapidly-expanding use of passive acoustic monitoring techniques. Reliable acoustic individual recognizers and accurate quantifiers of population size remain elusive, which discourages the use of vocal individuality for monitoring, wildlife management, and ecological research. We propose a neuro-fuzzy framework that allows discrimination of individuals by their calls, the discovery of unexpected individuals in a set of recordings, and estimation of population size using solely sound. Our method, tested using data collected in the wild, allows rapid individual identification and even acoustic censusing without prior information from the recorded individuals. We achieve this by integrating a fuzzy classification and clustering methodology (LAMDA) into a Convolutional Deep Clustering Neural Network (CDCN). Our approach will benefit monitoring for conservation, and paves the way towards robust individual acoustic identification in species whose handling is time-consuming, culturally or ethically problematic, or logistically difficult.