A machine learning-driven interactive training system for extreme vocal techniques

  • The scarcity of vocal instructors proficient in extreme vocal techniques and the lack of individualized feedback present challenges for novices learning these techniques. Therefore, this work explores the use of neural networks to provide real-time feedback for extreme vocal techniques within an interactive training system. An Extreme-Vocal dataset created for this purpose served as the basis for training a model capable of classifying False-Cord screams, Fry screams, and a residual class. The neural network achieved an overall accuracy of 0.83. We integrated the model into a user application to enable real-time visualization of classification results. By conducting a first qualitative user study involving 12 participants, we investigated whether interacting with the training system could enhance self-efficacy regarding the correct application of extreme vocal techniques. Our study participants indicated that they found the training system helpful for learning and categorizing extremeThe scarcity of vocal instructors proficient in extreme vocal techniques and the lack of individualized feedback present challenges for novices learning these techniques. Therefore, this work explores the use of neural networks to provide real-time feedback for extreme vocal techniques within an interactive training system. An Extreme-Vocal dataset created for this purpose served as the basis for training a model capable of classifying False-Cord screams, Fry screams, and a residual class. The neural network achieved an overall accuracy of 0.83. We integrated the model into a user application to enable real-time visualization of classification results. By conducting a first qualitative user study involving 12 participants, we investigated whether interacting with the training system could enhance self-efficacy regarding the correct application of extreme vocal techniques. Our study participants indicated that they found the training system helpful for learning and categorizing extreme vocal techniques.show moreshow less

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Metadaten
Author:Johanna Holzinger, Alexander HeimerlGND, Ruben SchlagowskiORCiDGND, Elisabeth AndréORCiDGND, Silvan MertesORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1154516
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/115451
ISBN:979-8-4007-0968-5OPAC
Parent Title (English):AM '24: proceedings of the 19th International Audio Mostly Conference: Explorations in Sonic Cultures, September 18-20, 2024, Milan, Italy
Publisher:ACM
Place of publication:New York, NY
Editor:Luca Andrea Ludovico, Davide Andrea Mauro
Type:Conference Proceeding
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2024/09/19
First Page:348
Last Page:354
DOI:https://doi.org/10.1145/3678299.3678334
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Menschzentrierte Künstliche Intelligenz
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)