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（ML）方法对生物声学录音进行自动化分析，有望大幅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部署ML，需采取以数据为中心的方法，重点使用经过精细标注和严格筛选、具备相关性与代表性的评估与训练数据。创建录音标注数据集面临诸多挑战，例如管理包含元数据的大规模录音库、开发能适应不同生物发声特征的灵活标注工具，以及应对专业标注人员匮乏的问题。2. 我们提出Whombat——一个用户友好型浏览器界面，用于管理录音与标注项目，集成了多种可视化、探索和标注工具。该工具支持用户快速完成标注、审查与共享，并可在数据集上可视化与评估一组机器学习预测结果。其核心机制支持迭代式工作流：用户标注与ML预测结果可相互反馈，从而同步提升模型性能与标注质量。3. 我们通过两个不同的应用案例展示Whombat的灵活性：一是英国蝙蝠保护信托基金会（BCT）旨在提升自动识别英国蝙蝠叫声能力的项目；二是美国农业部林务局与俄勒冈州立大学研究人员在太平洋西北地区合作探索生物声学应用并扩展鸟类自动分类模型的协作项目。4. Whombat作为灵活的工具，能有效应对生物声学研究的标注挑战。它既支持个人与协作研究，可部署于共享服务器或远程访问，亦可在个人计算机上运行，且无需编程技能。