1. Automated analysis of bioacoustic recordings using machine learning (ML) methods has the potential to greatly scale biodiversity monitoring efforts. The use of ML for high-stakes applications, such as conservation research, demands a data-centric approach with a focus on utilizing carefully annotated and curated evaluation and training data that is relevant and representative. Creating annotated datasets of sound recordings presents a number of challenges, such as managing large collections of recordings with associated metadata, developing flexible annotation tools that can accommodate the diverse range of vocalization profiles of different organisms, and addressing the scarcity of expert annotators. 2. We present Whombat a user-friendly, browser-based interface for managing audio recordings and annotation projects, with several visualization, exploration, and annotation tools. It enables users to quickly annotate, review, and share annotations, as well as visualize and evaluate a set of machine learning predictions on a dataset. The tool facilitates an iterative workflow where user annotations and machine learning predictions feedback to enhance model performance and annotation quality. 3. We demonstrate the flexibility of Whombat by showcasing two distinct use cases: an project aimed at enhancing automated UK bat call identification at the Bat Conservation Trust (BCT), and a collaborative effort among the USDA Forest Service and Oregon State University researchers exploring bioacoustic applications and extending automated avian classification models in the Pacific Northwest, USA. 4. Whombat is a flexible tool that can effectively address the challenges of annotation for bioacoustic research. It can be used for individual and collaborative work, hosted on a shared server or accessed remotely, or run on a personal computer without the need for coding skills.

翻译：1. 利用机器学习方法对生物声学记录进行自动化分析，有望显著扩展生物多样性监测工作的规模。在保护研究等高风险应用中运用机器学习，需要采用以数据为核心的方法，重点关注使用经过精心标注和整理的、兼具相关性与代表性的评估与训练数据。创建声音记录的标注数据集面临诸多挑战，例如管理带有元数据的大规模录音集合、开发能适配不同生物发声特征谱的灵活标注工具，以及应对专业标注人员稀缺的问题。2. 我们提出Whombat——一个用户友好的基于浏览器的界面，用于管理音频记录和标注项目，配备多种可视化、探索与标注工具。它使用户能够快速标注、审核和共享标注，同时可在数据集上可视化并评估一组机器学习预测结果。该工具支持迭代工作流程，用户标注与机器学习预测相互反馈，以提升模型性能与标注质量。3. 通过展示两个不同的应用实例来论证Whombat的灵活性：一项旨在增强英国蝙蝠保护信托基金会自动识别蝙蝠叫声的项目，以及美国农业部林务局与俄勒冈州立大学研究人员合作开展的探索生物声学应用、拓展美国太平洋西北地区自动鸟类分类模型的协作项目。4. Whombat是一款能够有效应对生物声学研究标注挑战的灵活工具，既可用于个人与协作工作（托管于共享服务器或远程访问），也可在个人电脑上无需编程技能即可运行。