  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.
