Roar Data: Redefining a Lion's Roar Using Machine Learning

TL;DR

This paper introduces a machine learning method to automatically identify and classify lion roars, improving population monitoring and reducing human bias.

Contribution

A data-driven approach using simple acoustic metrics and clustering to classify lion roars with high accuracy.

Findings

Lions produce two distinct types of roars: full-throated and intermediary.

Simple metrics like maximum frequency and vocalization length can classify lion calls with 95.4% accuracy.

Automated classification improves individual lion identification compared to manual methods.

Abstract

For territorial advertisement and intra‐pride communication African lions emit a roaring bout, of which one component, is their iconic roar. The full‐throated roar of a lion has recently been shown to be a unique and individually identifiable signature. At the same time, the frequency of large‐scale passive acoustic monitoring surveys has increased. As such, a lion's roar may soon become a useful tool to count individuals and estimate population density, to supplement traditional survey techniques. Currently, selecting full‐throated roars is heavily dependent on expert inference and is therefore subject to human‐induced bias. We propose a data‐driven approach to automatically classify lions' full‐throated roars from the other vocalisations that constitute a roaring bout. By using two‐state Gaussian Hidden‐Markov Models, we also demonstrate that two types of roars exist within a lion's roaring bout—a full‐throated roar and a newly named intermediary roar—and these can be classified at an accuracy of 84.7%. We further demonstrate that using simple metrics to describe lion vocalisations—maximum frequency (Hz) and vocalisation length (s)—and K‐means clustering is sufficient to classify lion call types, at a high accuracy (95.4%), and that using data‐driven predicted full‐throated roars results in an improved ability to identify individuals (F1‐score 0.87 vs. manual full‐throated roar classification 0.80). Here, we establish an easy‐to‐understand and implement process that will reduce the knowledge gap and make passive acoustic monitoring more accessible in a field currently dominated by other monitoring techniques (e.g., camera surveys), paving the way for novel research.

Lion's produce two distinct types of roars. Lion vocalisations can be classified using simple acoustic parameters. Automated classification of lion roars improves individual identification.

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