[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120290-en":3,"doc-seo-120290-105":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":4,"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},120290,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Combining two user-friendly machine learning tools increases species detection from acoustic recordings - Machine Learning Applications","Passive acoustic monitoring often produces extensive sound datasets that require machine learning to scan recordings, while algorithm development complexity can hinder adoption. This study evaluates Kaleidoscope Pro and BirdNET, two user-friendly tools, for detecting the American toad (Anaxyrus americanus) in audio recordings using a two-step combined workflow. Individually, Kaleidoscope Pro detected the species in 85.9% of validation recordings and BirdNET in 58.4%, whereas the combined approach raised detection to 93.3%. Applied to 6194 recordings, it reduced scanning and verification time and added 37 detections in 45 minutes.","Can. . .J Zool Downloaded from cdnsciencepub.com by [193.125.236.247 on 01/03/24](193.125.236.247 on 01/03/24)  \nOPEN ACCESS | Note  \nCombining two user-friendly machine learning tools increases species detection from acoustic recordings  \nCristian Pérez-Granados a,b , Mariano J. Feldman b,c , and Marc J. Mazerolle d  \na Ecology Department, Alicante University, Alicante, Spain; b Conservation Biology Group. Landscape Dynamics and Biodiversity programme. Forest Science and Technology Center of Catalonia (CTFC), Solsona, Catalonia, Spain; c Institut de Recherche sur les Forêts (IRF), Chaire industrielle CRSNG-UQAT sur la biodiversité en contexte minier, Centre d’étude de la forêt, Université du Québec en Abitibi Témiscamingue (UQAT), Rouyn-Noranda, Québec, Canada; dCentre d’étude de la forêt, Département des sciences du bois et de la forêt, Université Laval, Québec, Canada  \nCorresponding author: Cristian Pérez-Granados (email: [cristian.perez@ua.es](cristian.perez@ua.es))  \nAbstract  \nPassive acoustic monitoring usually generates large datasets that require machine learning algorithms to scan sound files, although the complexity of developing machine learning algorithms can be a barrier. We assessed the ability and speed of two user-friendly machine learning tools, Kaleidoscope Pro and BirdNET, for detecting the American toad (Anaxyrus americanus (Holbrook, 1836)) in sound recordings. We developed a two-step approach, combining both tools to maximize species detection while minimizing the time needed for output verification. When considered separately, Kaleidoscope Pro successfully detected the American toad in 85.9% of recordings in the validation dataset, while BirdNET detected the species in 58.4% of recordings. Combining the two tools in the two-step approach increased the detection rate to 93.3% . We applied the two-step approach to a large acoustic dataset (n = 6194 recordings) . We started by scanning the dataset using Kaleidoscope Pro (species detected in 417 recordings), then we used BirdNET on the remaining recordings without confirmed presence. The two-step approach reduced the scanning time, the time needed for output verification, and added 37 additional species detections in 45 min. Our findings highlight that combining machine learning tools can improve species detectability while minimizing time and effort.  \nKey words: American toad, BirdNET, convolutional neural network, Kaleidoscope Pro, Anaxyrus americanus (Holbrook, 1836), passive acoustic monitoring  \n1. Introduction  \nThe technological advances in recent decades revolutionized the way we currently monitor habitats and species. Among the emerging techniques for biomonitoring, several automated and non-invasive methods have rapidly become standard tools in ecology, such as camera trapping, remote sensing, and passive acoustic monitoring (Lahoz-Monfort and Magrath 2021) . These automated and non-invasive techniques offer researchers the ability to expand the spatial and temporal scales of their studies and contribute to collect large amounts of data. However, datasets obtained through automated techniques often pose issues for investigators because manual processing of automated data is time-consuming, tedious, and subject to human bias. To overcome these issues, machine learning algorithms can effectively process such large datasets (e.g. , Priyadarshani et al. 2018; Stowell 2022; Xie et al. 2022) .  \nPassive acoustic monitoring is increasingly being used to detect different groups such as anurans, bats, birds, or insects (Sugai et al. 2019; Hoefer et al. 2023) . Surveys relying on passive acoustic monitoring easily generate substantial volumes of recordings, making it impossible to visually inspect or lis-  \nten to all files (e.g. , Pérez-Granados and Schuchmann 2020) . The development of machine learning algorithms has become crucial to deal with these large numbers of files (Stowell 2022; Xie et al. 2022) . Unfortunately, implementing some of the state-of-the-a","cbCaisi7BbidSrt2","https://ap.wps.com/l/cbCaisi7BbidSrt2","pdf",492134,1,7,"English","en",105,"# Introduction\n## Passive acoustic monitoring and the need for automation\n## User-friendly machine learning tools\n# Methods\n## Two-step combined approach\n## Validation dataset and detection performance\n## Large acoustic dataset application\n# Results and discussion\n## Detection rate improvements and time savings\n# Conclusion","[{\"question\":\"Why combine Kaleidoscope Pro and BirdNET for acoustic species detection?\",\"answer\":\"Manual review of large passive acoustic datasets is time-consuming, and developing complex models can be a barrier. The two-step approach combines both tools to maximize detection while reducing time spent on verification.\"},{\"question\":\"How did detection performance differ between individual tools and the combined two-step workflow?\",\"answer\":\"On the validation dataset, Kaleidoscope Pro detected American toads in 85.9% of recordings, BirdNET in 58.4%, and the combined two-step approach increased detection to 93.3%.\"},{\"question\":\"What impact did the two-step approach have when applied to a large dataset?\",\"answer\":\"For 6194 recordings, the workflow reduced scanning time and verification time, and it added 37 additional species detections within 45 minutes.\"}]","Combining two user-friendly machine learning tools increases species detection from acoustic recordings - Machine Learning Applications | PDF",1785729263,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"combining-two-user-friendly-machine-learning-tools-increases-species-detection-from-acoustic-recordings-machine-learning-applications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/combining-two-user-friendly-machine-learning-tools-increases-species-detection-from-acoustic-recordings-machine-learning-applications/120290/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why combine Kaleidoscope Pro and BirdNET for acoustic species detection?","Question",{"text":75,"@type":76},"Manual review of large passive acoustic datasets is time-consuming, and developing complex models can be a barrier. The two-step approach combines both tools to maximize detection while reducing time spent on verification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did detection performance differ between individual tools and the combined two-step workflow?",{"text":80,"@type":76},"On the validation dataset, Kaleidoscope Pro detected American toads in 85.9% of recordings, BirdNET in 58.4%, and the combined two-step approach increased detection to 93.3%.",{"name":82,"@type":73,"acceptedAnswer":83},"What impact did the two-step approach have when applied to a large dataset?",{"text":84,"@type":76},"For 6194 recordings, the workflow reduced scanning time and verification time, and it added 37 additional species detections within 45 minutes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]