CrySenseNet: A Deep Learning-Based Acoustic Intelligence System for Decoding Infant Cries

CrySenseNet: A Deep Learning-Based Acoustic Intelligence System for Decoding Infant Cries | 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 CrySenseNet: A Deep Learning-Based Acoustic Intelligence System for Decoding Infant Cries Krishna S, Anushka B R, Swetha Saju, Amrutha K V, Devika S Babu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7670543/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 Infant crying is the primary communication medium for infants,for expressing their fundamental needs and medical issues. Proper infant cry classification can help parents and clinicians identify issues early and apply correct interventions. Here, we examine infant cry signal classification with various machine learning and deep learning techniques. Hand-crafted features were taken out from audio signals and labelled with traditional machine learning models, i.e., Support Vector Machine,Hidden Markov Model,Probabilistic Neural Network,Multi-Layer Perceptron, and Recurrent Neural Network. Additionally, deep learning models like 1D Convolutional Neural Network, Convolutional Neural Network-By Long Short Term Memory hybrid models, and transfer models like GoogLeNet, ShuffleNet, and ResNet-18 were used over spectrogram-based representations.The experiments were performed on the Infant Cry Audio Corpus dataset, which contains five different classes. Out of all the models, SVM produced the maximum classification accuracy of 96.07% then GoogLeNet (84.98%), ShuffleNet (84.78%),CNN-BiLSTM(84%) PNN (83.70%), MLP (82.61%),ResNet-18(80.43%), RNN (80%), CNN (82%), and HMM (66%). The outcomes show that transfer learning models and conventional machine learning classifiers, especially when they employ hand-crafted features, perform better than single deep learning models in this task. In general, the findings confirm the effectiveness of combining signal processing techniques with high-end classification methods for stable and accurate infant cry analysis. Artificial Intelligence and Machine Learning Infant Cry Analysis Hand-Crafted Features MFCC Deep Learning Machine Learning Cry Classification Full Text Additional Declarations The authors declare no competing interests. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7670543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518602043,"identity":"e1dfe105-48d4-4abf-a3bd-15fdc682ca0a","order_by":0,"name":"Krishna S","email":"","orcid":"","institution":"Digital University Kerala","correspondingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"","lastName":"S","suffix":""},{"id":518602044,"identity":"2f2501b7-cd40-4c5a-b8c8-932c2ea5860a","order_by":1,"name":"Anushka B R","email":"","orcid":"","institution":"Digital University Kerala","correspondingAuthor":false,"prefix":"","firstName":"Anushka","middleName":"B","lastName":"R","suffix":""},{"id":518602045,"identity":"6d55b17e-49ac-4eb5-86d7-ea9aed7fce6c","order_by":2,"name":"Swetha Saju","email":"","orcid":"","institution":"Digital University Kerala","correspondingAuthor":false,"prefix":"","firstName":"Swetha","middleName":"","lastName":"Saju","suffix":""},{"id":518602046,"identity":"9c9b5b51-e6a9-4185-aaf7-49169fe5fd91","order_by":3,"name":"Amrutha K V","email":"","orcid":"","institution":"Digital University Kerala","correspondingAuthor":false,"prefix":"","firstName":"Amrutha","middleName":"K","lastName":"V","suffix":""},{"id":518602047,"identity":"37ff8082-33b7-4465-a904-63d97fbae62c","order_by":4,"name":"Devika S Babu","email":"","orcid":"","institution":"Digital University Kerala","correspondingAuthor":false,"prefix":"","firstName":"Devika","middleName":"S","lastName":"Babu","suffix":""},{"id":518602048,"identity":"01b1a166-b644-47e0-979d-0590cb8b1abe","order_by":5,"name":"Sishu Shankar Muni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYDACZgY2IHmAgYEHwufhB5EJBaRokWwAaTHAaw+qFgaDA2ASt3r5dvZnj3n+3JE35zn87NPNtjsyxudXJ354YMAgzy92AKsWg8M85sa8bc8Md/a2Gc/ObXvGY3bj7WYJoMMMZ85OwK6FmYdNmrfhMOOG8wzGzLlth4Fazm4AaUkwuI1di3wz+zNpnj+H7TecZ/8M1mI84+zmH/i0MBxmMJPmYTucuOFsD8QWA/7ebXhtAfrFTHJu2+HkDWfOFDPnnDvMI3GDd5tFgoEETr/I9x9/JvHmz2HbDWfSNzPnlB225+8/u/nmjwobeX5pHA7DBBJglRLEKgcB/gOkqB4Fo2AUjIIRAAAVqmBZX2lwOgAAAABJRU5ErkJggg==","orcid":"","institution":"Digital University Kerala","correspondingAuthor":true,"prefix":"","firstName":"Sishu","middleName":"Shankar","lastName":"Muni","suffix":""},{"id":518602049,"identity":"b40fc79e-6df1-4416-b677-1a85c33f31f5","order_by":6,"name":"Swetha P","email":"","orcid":"","institution":"Digital University Kerala","correspondingAuthor":false,"prefix":"","firstName":"Swetha","middleName":"","lastName":"P","suffix":""}],"badges":[],"createdAt":"2025-09-22 09:11:40","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7670543/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7670543/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92391892,"identity":"7996a26f-06b6-49e6-8c34-6a76f5eed98d","added_by":"auto","created_at":"2025-09-29 08:41:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1307065,"visible":true,"origin":"","legend":"","description":"","filename":"InfantcrysignalKrishnaAnushkaSwethaAmrutha3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7670543/v1_covered_e56f63e5-d43d-4a80-8b68-562007a609c4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eCrySenseNet: A Deep Learning-Based Acoustic Intelligence System for Decoding Infant Cries\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Digital University Kerala","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":"Infant Cry Analysis, Hand-Crafted Features, MFCC, Deep Learning, Machine Learning, Cry Classification","lastPublishedDoi":"10.21203/rs.3.rs-7670543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7670543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInfant crying is the primary communication medium for infants,for expressing their fundamental needs and medical issues. Proper infant cry classification can help parents and clinicians identify issues early and apply correct interventions. Here, we examine infant cry signal classification with various machine learning and deep learning techniques. Hand-crafted features were taken out from audio signals and labelled with traditional machine learning models, i.e., Support Vector Machine,Hidden Markov Model,Probabilistic Neural Network,Multi-Layer Perceptron, and \u0026nbsp;Recurrent Neural Network. Additionally, deep learning models like 1D Convolutional Neural Network, Convolutional Neural Network-By Long Short Term Memory hybrid models, and transfer models like GoogLeNet, ShuffleNet, and ResNet-18 were used over spectrogram-based representations.The experiments were performed on the Infant Cry Audio Corpus dataset, which contains five different classes. Out of all the models, SVM produced the maximum classification accuracy of 96.07% then GoogLeNet (84.98%), ShuffleNet (84.78%),CNN-BiLSTM(84%) PNN (83.70%), MLP (82.61%),ResNet-18(80.43%), RNN (80%), CNN (82%), and HMM (66%). The outcomes show that transfer learning models and conventional machine learning classifiers, especially when they employ hand-crafted features, perform better than single deep learning models in this task. In general, the findings confirm the effectiveness of combining signal processing techniques with high-end classification methods for stable and accurate infant cry analysis.\u003c/p\u003e","manuscriptTitle":"CrySenseNet: A Deep Learning-Based Acoustic Intelligence System for Decoding Infant Cries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 08:40:10","doi":"10.21203/rs.3.rs-7670543/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9645af96-21e6-4a94-9a93-4e77a4d18f43","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55104065,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-09-29T08:40:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 08:40:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7670543","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7670543","identity":"rs-7670543","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}