[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122159-en":3,"doc-seo-122159-105":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},122159,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",6,"Technology","Whombat - An open-source audio annotation tool for machine learning assisted bioacoustics","Machine learning can scale biodiversity monitoring by analyzing bioacoustic recordings, but high-stakes use in conservation and scientific research requires careful selection and use of relevant, representative, and well-annotated datasets. Building these datasets is difficult due to large recording collections with metadata, the need for flexible tools for diverse vocalizations, and the shortage of expert annotators. Whombat addresses these challenges through an interactive, browser-based annotation workflow.","Edinburgh Research Explorer  \nWhombat  \nAn open-source audio annotation tool for machine learning assisted bioacoustics  \nCitation for published version:  \nBalvanera, SM, Mac Aodha, O, Weldy, MJ, Pringle, H, Browning, E & Jones, KE 2024, 'Whombat: An opensource audio annotation tool for machine learning assisted bioacoustics', Methods in ecology and evolution, vol. 16, no. 1, pp. 19-28. [https://doi.org/10.1111/2041-210X.14468](https://doi.org/10.1111/2041-210X.14468)  \nDigital Object Identifier (DOI):  \n10.1111/2041-210X.14468  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nMethods in ecology and evolution  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 10. Feb. 2025  \nReceived: 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 collabo","cbCaisYoHtHADfHb","https://ap.wps.com/l/cbCaisYoHtHADfHb","pdf",1329390,1,11,"English","en",105,"# Introduction\n## Data-centric machine learning for bioacoustics\n## Need for curated annotations and tools\n# Whombat: browser-based audio annotation\n## Managing recordings and annotation projects\n## Visualization, exploration, and annotation tools\n## Iterative feedback with ML predictions\n# Use cases\n## Enhancing automated UK bat call identification\n## Collaborative avian classification model extension\n# Accessibility and deployment","[{\"question\":\"What problem does Whombat target in machine learning assisted bioacoustics?\",\"answer\":\"Whombat targets the practical challenges of creating and managing well-annotated bioacoustic datasets needed for reliable ML, including handling large audio collections with metadata and supporting annotation across diverse vocalization types.\"},{\"question\":\"What capabilities does Whombat provide to users?\",\"answer\":\"Whombat offers a user-friendly, browser-based interface for managing audio recordings and annotation projects, including visualization, exploration, and annotation tools, plus options to review and share annotations and evaluate ML predictions.\"},{\"question\":\"How does Whombat support an iterative workflow with machine learning?\",\"answer\":\"Whombat enables an iterative process where user annotations and machine learning predictions feed back into improving model performance and the quality of annotations.\"}]","Whombat - 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