Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano

Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano | 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 Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano Zhenyi Hou, Xu Zhao, Kejie Ye, Shanggerile Jiang, Xinyu Sheng, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8259090/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Vocal education in the music field is challenging to quantify due to the individual variability in singers' voices and the diverse criteria for evaluating singing techniques. Deep learning offers substantial potential in music education by efficiently handling complex data and performing quantitative analyses. However, achieving accurate evaluations with limited samples, especially for rare vocal types such as Mezzo-soprano, requires extensive well-annotated data to support deep learning models. To address this challenge, we employ transfer learning by leveraging deep learning models pre-trained on the ImageNet and Urbansound8k datasets, aiming to enhance the precision of vocal technique evaluation. Additionally, we address the issue of data scarcity by constructing a dedicated dataset, the Mezzo-soprano Vocal Set (MVS), specifically designed for vocal technique assessment. Our experimental results demonstrate that transfer learning improves the overall accuracy (OAcc) of all models by an average of 8.3%, with the highest accuracy reaching 94.2%. Beyond technical improvements, this study introduces a new quantitative assessment method for music education. By bridging deep learning methods with vocal pedagogy, our approach offers significant practical implications for enabling more objective and personalized vocal education, and advancing the scientific understanding of singing techniques. Physical sciences/Engineering Physical sciences/Mathematics and computing Transfer learning Vocal education Mezzo-soprano Vocal technique assessment Deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 15 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Submission checks completed at journal 10 Dec, 2025 First submitted to journal 02 Dec, 2025 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-8259090","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":629739414,"identity":"e5c0ba26-1a54-46bf-be7d-322563ee625f","order_by":0,"name":"Zhenyi Hou","email":"","orcid":"","institution":"University of Shanghai for Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhenyi","middleName":"","lastName":"Hou","suffix":""},{"id":629739415,"identity":"d849260f-0a80-473b-9e70-866561e4ea1d","order_by":1,"name":"Xu Zhao","email":"","orcid":"","institution":"University of Shanghai for Science and 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