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.
翻译:在生态、生物多样性和养护研究中广泛使用被动声学监测,通过声学监测收集的数据集往往非常大,并且用人工智能和机器学习模型自动处理,这些模型旨在复制域专家的工作。这些模型是受监督的学习算法,需要接受专家制作的高质量说明的培训。由于专家往往资源有限,因此需要一种具有成本效益的音频批注程序,以便从数据中得到最大程度的利用。我们提供了一个开放源互动音频数据注解工具NEAL(自然+能源音频实验室)。使用R和相关的Shiny框架,该工具提供了一个反应环境,用户可以快速注解音文档,调整环境,自动改变用户界面的相应要素。设计该应用程序的目的是让专家鸟类和公民科学家为声调注解项目作出贡献。在生物经济学中,R编程的普及性和灵活性意味着可以将新应用修改为其他鸟类标签数据集,甚至用于通用音频标签任务。我们通过对爱尔兰各地风农场收集的数据进行贴标签来展示应用。