[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122298-en":3,"doc-seo-122298-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122298,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",6,"Technology","Whombat - An open-source audio annotation tool for machine learning assisted bioacoustics","Automated analysis of bioacoustic recordings using machine learning can greatly scale biodiversity monitoring, but high-stakes use requires a data-centric approach grounded in carefully annotated and representative training and evaluation data. The creation of annotated bioacoustic datasets is challenging due to large recording collections with metadata, the need for flexible tools for diverse vocalization profiles, and the scarcity of expert annotators.","Received: 11 October 2024  \nAccepted: 8 November 2024  \nDOI: 10. 1111/2041-210X.14468  \nA PPL I C AT I O N  \nWhombat: An open-source audio annotation tool for machine learning assisted bioacoustics  \nSantiago Martínez Balvanera1  | Oisin Mac Aodha2  | Matthew J. Weldy3,4  | Holly Pringle1 | Ella Browning1,5  | Kate E. Jones1   \n1Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK  \n2School of Informatics, University of Edinburgh, Edinburgh, UK  \n3 Department of Forest Ecosystems and Society, College of Forestry, Oregon State University, Corvallis, Oregon, USA 4Pacific Northwest Research Station, USDA Forest Service, Corvallis, Oregon, USA  \n5Bat Conservation Trust, Cloisters Business Centre, London, UK  \nCorrespondence  \nSantiago Martínez Balvanera  \nEmail: [santiago.balvanera.20@ucl.ac.uk](santiago.balvanera.20@ucl.ac.uk)  \nFunding information  \nConsejo Nacional de Humanidades, Ciencias y Tecnologías, Grant/Award Number: 2020-000017-02EXTF-00334  \nHandling Editor: Camille Desjonquères  \nAbstract  \n1. 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 and scientific research, demands a data-centric approach with a focus on selecting and utilizing carefully annotated and curated evaluation and training data that are relevant and representative. Creating annotated bioacoustic datasets 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.  \n2. 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 predictionson a dataset. The tool facilitates an iterative workflow where user annotations and machine learning predictions feedback to enhance model performance and annotation quality.  \n3. We demonstrate the flexibility of Whombat by showcasing two distinct use cases: (1) a project aimed at enhancing automated UK bat call identification atthe Bat Conservation Trust (BCT), and (2) 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.  \n4. 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. The code is open-source, and we provide a user guide.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2024 The Author(s) . Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.  \n2  \n|  \nMARTÍNEZ BALVANERA et al.  \nK E Y WO R D S  \nAI, audio annotation, bioacoustics, bioinformatics, machine learning, software, sound event detection, visualisation  \n1 | INTRODUCTION  \nRecent advancements in Machine Learning (ML) are revolutionizing our ability to analyse large datasets generated by passive acoustic recorders for ecologically relevant signals (Kitzes et al., 2021; Tuia et al., 2022) . Open-source Deep Learning models, such as BirdNET (Kahl et al., 2021) and NABat ML (Khalighifar et al., 2022), can be used to monitor birds and bats at scale across la","cbCaig88vcWXfXWi","https://ap.wps.com/l/cbCaig88vcWXfXWi","pdf",1127783,1,10,"English","en",105,"# Introduction\n## Data-centric machine learning for bioacoustics\n## Challenges of audio annotation\n## Open-source tools and existing gaps\n# Abstract\n## Purpose and data requirements\n## Overview of Whombat\n## Use cases and capabilities","[{\"question\":\"Why is bioacoustic ML analysis dependent on high-quality annotated data?\",\"answer\":\"High-stakes applications require carefully selected, curated, and representative training and evaluation data to ensure reliable model performance and assessment.\"},{\"question\":\"What makes audio annotation for bioacoustics difficult?\",\"answer\":\"It is time-consuming and labor-intensive, involving locating sound events and assigning labels across large audio collections with complex metadata, while also coping with diverse vocalization types and limited expert annotators.\"},{\"question\":\"What is Whombat and what does it provide to users?\",\"answer\":\"Whombat is a user-friendly, browser-based interface for managing audio recordings and annotation projects, offering visualization, exploration, annotation tools, and the ability to review and share annotations alongside machine learning predictions.\"}]","Whombat - 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