Bird Sound Feature Extraction and Recognition Model Design Based on Neural Network Architecture Search

Bird Sound Feature Extraction and Recognition Model Design Based on Neural Network Architecture Search | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bird Sound Feature Extraction and Recognition Model Design Based on Neural Network Architecture Search ying jiang, liu-lei zhang, fan yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3602232/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Birds are of great significance to biodiversity. Bird sounds have obvious characteristics per species, and they are an important way forbids to communicate and transmit information. Accurate identification of bird species plays a critical role in biodiversity surveys. The development of deep learning has made it possible to identify bird species through acoustic characteristics. However, most of the deep learning-based research on bird sound recognition relies too much on traditional experience and subjective human-designed feature extraction and model design, regardless of whether they are optimal for the bird sound data. Therefore, this study proposes a deep learning model based on neural architecture search, which uses a simple and equivalent node to design the model structure for feature extraction and recognition and takes the inferring time as part of the loss function to balance classification accuracy and running time, reducing model complexity. During the feature extraction phase, the final model selects 36 as the optimal number of Mel filter banks. On a dataset of 264 bird sounds, the neural network architecture search-based model achieves an average classification accuracy of 92.44% and a maximum classification accuracy of 98.14%. This study provides a meaningful exploration for achieving bird sound recognition without relying on traditional experience and subjective human design. deep Learning Mel filter bank Mel spectrum karchitecture search Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3602232","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323576164,"identity":"78c4235c-d939-476a-9a6e-1efbb5c21c5d","order_by":0,"name":"ying jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACxmYwJcHAwMz/4EBCBQMPCVrYexgPfDhDhBYE4DnDfHBmGxEKmduZnz382maRJx+Re+Aw77w6GXP2A4wfPubgcxibubFsm0Sx4Y28hMO82w7zWPYkMEvO3IbXL2bSkm0SiRtnJBgAtRzgMTiQwMbMi1cL+zckLXPqeAzOPyCkhcdM8iNQy3yeMwYHZzYw8xjcIGgLT5k0wzmJxA3sbQkHPhw7DNTysBmvXwz7j2+T/FFWlzi/mfnwh4SaOnuD88kHP3zEp6UBGNC8bAwMBgcQNjfgVg8E8iAlP/4AGfjVjYJRMApGwUgGAKkfVb10TdPwAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-4293-634X","institution":"Nanjing Forestry University","correspondingAuthor":true,"prefix":"","firstName":"ying","middleName":"","lastName":"jiang","suffix":""},{"id":323576165,"identity":"7c150751-f080-4ba6-8683-5d559e609a49","order_by":1,"name":"liu-lei zhang","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"liu-lei","middleName":"","lastName":"zhang","suffix":""},{"id":323576166,"identity":"9e79aa2d-ccab-4306-80f9-f03e6e199d97","order_by":2,"name":"fan yang","email":"","orcid":"","institution":"Nanjing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"fan","middleName":"","lastName":"yang","suffix":""}],"badges":[],"createdAt":"2023-11-13 00:41:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3602232/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3602232/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63197191,"identity":"247599e0-18eb-46dc-bb20-a3dbff52e2e2","added_by":"auto","created_at":"2024-08-24 20:53:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":515414,"visible":true,"origin":"","legend":"","description":"","filename":"output3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3602232/v1_covered_4106391b-45a6-4a1c-8b38-835e5e9e4dd0.pdf"}],"financialInterests":"","formattedTitle":"Bird Sound Feature Extraction and Recognition Model Design Based on Neural Network Architecture Search","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"deep Learning, Mel filter bank, Mel spectrum, karchitecture search","lastPublishedDoi":"10.21203/rs.3.rs-3602232/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3602232/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Birds are of great significance to biodiversity. 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