Minimal-Preprocessing Paradigm for Robust COVID-19 Detection from Cough Audio Using Deep Learning

Minimal-Preprocessing Paradigm for Robust COVID-19 Detection from Cough Audio Using Deep Learning | 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 Article Minimal-Preprocessing Paradigm for Robust COVID-19 Detection from Cough Audio Using Deep Learning Chee Chin Lim, Leow Bin Toh, Megat Syahirul Amin Megat Ali, Jason Teh Lei Yik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9394179/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The COVID-19 pandemic has highlighted the need for rapid, accessible, and non-invasive screening tools to complement conventional diagnostic methods. While audio-based analysis of cough signals has emerged as a promising approach, most existing studies rely on complex preprocessing pipelines aimed at noise suppression and signal enhancement. However, the impact of such preprocessing on model generalisation remains insufficiently understood. This study challenges the prevailing assumption that extensive preprocessing is necessary for robust audio-based diagnosis. We propose a systematic evaluation of preprocessing strategies, investigating whether minimal preprocessing can preserve diagnostically relevant acoustic information and improve model performance. Cough recordings were transformed into time–frequency representations using short-time Fourier transform (STFT) and Mel-frequency cepstral coefficients (MFCCs) and classified using multiple convolutional neural network (CNN) architectures, including ResNet, Inception, and MobileNet variants. Experimental results demonstrate that minimal preprocessing using Fast Fourier Transform (FFT) without additional filtering consistently outperforms conventional noise-reduction techniques. The best-performing model, ResNet50V2, achieved a testing accuracy of 86.79%, with balanced precision, sensitivity, specificity, and F1-score. Furthermore, the combination of STFT and MFCC features provided complementary information, improving classification robustness. These findings indicate that preserving full spectral information is critical for effective deep learning–based audio diagnosis, and that aggressive preprocessing may inadvertently suppress subtle pathological signatures. This work establishes a minimal-preprocessing paradigm for cough-based COVID-19 detection and provides practical insights for developing robust, scalable, and non-invasive diagnostic systems. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviewers agreed at journal 15 May, 2026 Reviews received at journal 02 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 23 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 12 Apr, 2026 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. 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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-9394179","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633692143,"identity":"5ba8ef3f-d53f-46be-a796-3dab8f010543","order_by":0,"name":"Chee Chin Lim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYFAC5oYDDBUMDGxQHjFaGIFazpCqhYGxDWEnYQ387QcbD92cdziPT+zwswcMFdaJDWJnDPBqkTiT2HA4d9vhYjbpNHMDhjPpiQ3SOfi1GEgwgrUktkknmEkwth0mVssckJb0bxKM/4jW0gDSkmMGYhPWAvZLzrF0kJZyg4Rj6cZt0mkFeLXwtx8+/Dmnxjpx/uz0bQ8+1FjL9ksnb8CrBRmwMSSASQ78DkPVAgHsD4jWMgpGwSgYBSMCAABybUdfJ3bBtAAAAABJRU5ErkJggg==","orcid":"","institution":"Universiti Malaysia Perlis","correspondingAuthor":true,"prefix":"","firstName":"Chee","middleName":"Chin","lastName":"Lim","suffix":""},{"id":633692144,"identity":"0c358aad-d66d-4914-b11e-933850a696d6","order_by":1,"name":"Leow Bin Toh","email":"","orcid":"","institution":"Taylor’s University","correspondingAuthor":false,"prefix":"","firstName":"Leow","middleName":"Bin","lastName":"Toh","suffix":""},{"id":633692145,"identity":"eb73cdd6-8b2c-4cc9-a817-68e232ecfc68","order_by":2,"name":"Megat Syahirul Amin Megat Ali","email":"","orcid":"","institution":"Universiti Teknology MARA","correspondingAuthor":false,"prefix":"","firstName":"Megat","middleName":"Syahirul Amin Megat","lastName":"Ali","suffix":""},{"id":633692146,"identity":"72b7197a-a49d-474b-ab9b-5777eb4cff4d","order_by":3,"name":"Jason Teh Lei Yik","email":"","orcid":"","institution":"Universiti Malaysia Perlis","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"Teh Lei","lastName":"Yik","suffix":""}],"badges":[],"createdAt":"2026-04-12 12:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9394179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9394179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108803696,"identity":"28c904cc-228c-4947-b79a-129dbd7f100f","added_by":"auto","created_at":"2026-05-08 15:03:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":701983,"visible":true,"origin":"","legend":"","description":"","filename":"editscirepstage1rrmanuscripttemplate5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9394179/v1_covered_0bdc9a04-84c0-49f5-9e17-a5ce41357a15.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Minimal-Preprocessing Paradigm for Robust COVID-19 Detection from Cough Audio Using Deep Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9394179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9394179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe COVID-19 pandemic has highlighted the need for rapid, accessible, and non-invasive screening tools to complement conventional diagnostic methods. While audio-based analysis of cough signals has emerged as a promising approach, most existing studies rely on complex preprocessing pipelines aimed at noise suppression and signal enhancement. However, the impact of such preprocessing on model generalisation remains insufficiently understood. This study challenges the prevailing assumption that extensive preprocessing is necessary for robust audio-based diagnosis. We propose a systematic evaluation of preprocessing strategies, investigating whether minimal preprocessing can preserve diagnostically relevant acoustic information and improve model performance. Cough recordings were transformed into time\u0026ndash;frequency representations using short-time Fourier transform (STFT) and Mel-frequency cepstral coefficients (MFCCs) and classified using multiple convolutional neural network (CNN) architectures, including ResNet, Inception, and MobileNet variants. Experimental results demonstrate that minimal preprocessing using Fast Fourier Transform (FFT) without additional filtering consistently outperforms conventional noise-reduction techniques. The best-performing model, ResNet50V2, achieved a testing accuracy of 86.79%, with balanced precision, sensitivity, specificity, and F1-score. Furthermore, the combination of STFT and MFCC features provided complementary information, improving classification robustness. These findings indicate that preserving full spectral information is critical for effective deep learning\u0026ndash;based audio diagnosis, and that aggressive preprocessing may inadvertently suppress subtle pathological signatures. This work establishes a minimal-preprocessing paradigm for cough-based COVID-19 detection and provides practical insights for developing robust, scalable, and non-invasive diagnostic systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Minimal-Preprocessing Paradigm for Robust COVID-19 Detection from Cough Audio Using Deep Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 19:05:25","doi":"10.21203/rs.3.rs-9394179/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T20:45:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41438080165002502427162161200356112246","date":"2026-05-15T18:10:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T07:31:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92329826801857422552129333226588412969","date":"2026-04-23T22:41:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T18:00:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-23T15:32:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T09:51:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-20T09:51:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-12T12:31:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4793a1f3-0f01-4079-953c-bd2865bf5fca","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T20:45:08+00:00","index":75,"fulltext":""},{"type":"reviewerAgreed","content":"41438080165002502427162161200356112246","date":"2026-05-15T18:10:50+00:00","index":74,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T07:31:24+00:00","index":30,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67443526,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":67443527,"name":"Health sciences/Diseases"},{"id":67443528,"name":"Physical sciences/Engineering"},{"id":67443529,"name":"Health sciences/Health care"},{"id":67443530,"name":"Physical sciences/Mathematics and computing"},{"id":67443531,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-04T19:05:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 19:05:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9394179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9394179","identity":"rs-9394179","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}