{"paper_id":"207fce43-6e42-47a1-8dcc-e9614d444bc8","body_text":"SunEcho: An Optimized Deep Learning Model for Real-Time Urban Environmental Sound Classification | 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 SunEcho: An Optimized Deep Learning Model for Real-Time Urban Environmental Sound Classification Muhanguzi Joel Tibabwetiza, Trevor Saaka, Solomon Nsumba, John Quinn, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8739067/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Noise pollution represents a growing public health crisis, with 466 million people worldwide experiencing disabling hearing loss in 2019, projected to reach 700 million by 2050. Approximately 80% of affected individuals reside in low- and middle-income countries, where limited capacity to identify and monitor noise sources exacerbates the problem. This paper presents an optimized urban noise classification system designed for deployment on resource-constrained edge devices to enable continuous environmental monitoring. We investigated four convolutional neural network architectures—SunEcho, Spec-CNN, AlexNet, and LeNet-5—using the Sunbird/urban-noise-uganda-61k dataset with two input representations: log-mel spectrograms and YAMNet embeddings. Models were evaluated under fine-grained (19-class) and categorical (6-class) taxonomies. Spectrogram-based inputs consistently outperformed embeddings across all architectures, with the custom SunEcho model achieving optimal performance: 89% categorical accuracy and 81% fine-grained validation accuracy. The system provides city authorities in developing regions with an accessible, deployable tool for evidence-based noise source identification and mitigation strategies to improve public health outcomes. Urban Noise Classification Edge Computing Convolutional Neural Network Log-Mel Spectrogram Environmental Acoustic Monitoring TinyML Low and Middle-Income Countries Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers invited by journal 19 Feb, 2026 Editor assigned by journal 15 Feb, 2026 Submission checks completed at journal 15 Feb, 2026 First submitted to journal 30 Jan, 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. 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. 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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-8739067\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":595320090,\"identity\":\"9d23ea6d-143a-419d-8e08-978b50a9c6b9\",\"order_by\":0,\"name\":\"Muhanguzi Joel 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