NEAL: An open-source tool for audio annotation

TL;DR

NEAL is an open-source, interactive audio annotation tool built with R and Shiny, designed to facilitate expert and citizen scientist contributions to large-scale acoustic datasets for ecological research.

Contribution

The paper introduces NEAL, a flexible, open-source annotation tool that simplifies audio labeling for ecological studies, leveraging R and Shiny for broad accessibility and customization.

Findings

Successfully labeled wind farm acoustic data in Ireland

Demonstrated ease of use for both experts and citizen scientists

Showcased adaptability for various bioacoustic datasets

Abstract

Passive acoustic monitoring is used widely in ecology, biodiversity, and conservation studies. Data sets collected via acoustic monitoring are often extremely large and built to be processed automatically using Artificial Intelligence and Machine learning models, which aim to replicate the work of domain experts. These models, being supervised learning algorithms, need to be trained on high quality annotations produced by experts. Since the experts are often resource-limited, a cost-effective process for annotating audio is needed to get maximal use out of the data. We present an open-source interactive audio data annotation tool, NEAL (Nature+Energy Audio Labeller). Built using R and the associated Shiny framework, the tool provides a reactive environment where users can quickly annotate audio files and adjust settings that automatically change the corresponding elements of the user interface. The app has been designed with the goal of having both expert birders and citizen scientists contribute to acoustic annotation projects. The popularity and flexibility of R programming in bioacoustics means that the Shiny app can be modified for other bird labelling data sets, or even to generic audio labelling tasks. We demonstrate the app by labelling data collected from wind farm sites across Ireland.