Comparative Wavelet and MFCC Speech EmotionRecognition Experiments on the RAVDESS Dataset

Comparative Wavelet and MFCC Speech EmotionRecognition Experiments on the RAVDESS Dataset | 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 Comparative Wavelet and MFCC Speech EmotionRecognition Experiments on the RAVDESS Dataset Aayush Bajaj, Dr. K.C. Tripathi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1679598/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 Emotion Recognition (ER) from speech is one of the most interesting research domains for the scientific world. The challenge behind ER is essentially the method of speech-feature-extraction that can efficiently encapsulate speaker independent emotional information from speech signals. This paper compares the performance of Window-Fourier Transform Method, Mel-Frequency Cepstral Coefficients (MFCC’s) and Continuous/Discrete Wavelet Transforms from the perspective of constant vs variant localization of time-frequency on The Rayerson audio-visual database of emotional speech and song. Wavelet transform has proven to be a promising non-linear tool for signal analysis that has been successfully applied in image recognition, compression and other tasks. MFCC’s has been a standard in feature extraction for speech. The motive here is to compare both the methods using the Random Forest algorithm with similar hyperparameters. Computer Architecture and Engineering Artificial Intelligence and Machine Learning Continuous wavelet transform Discrete Wavelet transform Mel- Frequency Cepstral Coefficients Emotion recognition Speech processing Pat- tern Recognition Decision Tree Classifier Random Forest Classifier Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Full Text Declarations Competing interests: 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-1679598","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":110241543,"identity":"4da1e8d4-f80f-40f4-83a2-3a13d90dce2e","order_by":0,"name":"Aayush Bajaj","email":"data:image/png;base64,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","orcid":"","institution":"Maharaja Agrasen Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Aayush","middleName":"","lastName":"Bajaj","suffix":""},{"id":110241544,"identity":"8b84f411-f241-4467-b3ed-15373da12fba","order_by":1,"name":"Dr. K.C. 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