A Meta-Methodology for User Evaluation of Artificial Intelligence Generated Music; Using the Analytical Hierarchy Process, Likert and Emotional State Estimations.
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| Title: | A Meta-Methodology for User Evaluation of Artificial Intelligence Generated Music; Using the Analytical Hierarchy Process, Likert and Emotional State Estimations. |
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| Authors: | Civit, Miguel1 (AUTHOR) mcivit@uloyola.es, Drai-Zerbib, Véronique2 (AUTHOR), Cuadrado, Francisco1 (AUTHOR), Escalona, Maria J.3 (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Nov2025, Vol. 41 Issue 21, p13906-13922. 17p. |
| Subjects: | Analytic hierarchy process, Likert scale, System analysis, Musical composition, Research evaluation, Evaluation methodology, Sentiment analysis |
| Abstract: | Artificial Intelligence (AI) music generation is a trending field, and many different generators are currently under development. However, no standardized evaluation method exists that can help researchers evaluate and compare AI-based music tools. To create a meta-methodology for AI music assessment based on user evaluation, that can be both standardized and deployed as a tailored implementation model adapted to the idiosyncrasies of specific generators and their intended applications, thereby helping future researchers draw comparisons between different systems. Two different decision trees/matrices are proposed to help researchers tailor their specific evaluation studies. As evaluation tools, the paper explores Likert and analytical hierarchy process (AHP) based surveys and emotional state estimations using facial action units, self-assessment, and physiological signals. A proof-of-concept study demonstrates the viability of the proposed tools for user-based AI music generation evaluation studies. A preference for audio music over symbolic music generation was observed, and this will require future research. The implementation of the proposed methodology and tools across the field will be helpful when comparing different systems in future research and to save time in the development of user-based studies. User-based evaluation studies are needed to prevent biases from passing into future iterations of AI music generators. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188923594 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Meta-Methodology for User Evaluation of Artificial Intelligence Generated Music; Using the Analytical Hierarchy Process, Likert and Emotional State Estimations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Civit%2C+Miguel%22">Civit, Miguel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mcivit@uloyola.es</i><br /><searchLink fieldCode="AR" term="%22Drai-Zerbib%2C+Véronique%22">Drai-Zerbib, Véronique</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cuadrado%2C+Francisco%22">Cuadrado, Francisco</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Escalona%2C+Maria+J%2E%22">Escalona, Maria J.</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Nov2025, Vol. 41 Issue 21, p13906-13922. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Analytic+hierarchy+process%22">Analytic hierarchy process</searchLink><br /><searchLink fieldCode="DE" term="%22Likert+scale%22">Likert scale</searchLink><br /><searchLink fieldCode="DE" term="%22System+analysis%22">System analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Musical+composition%22">Musical composition</searchLink><br /><searchLink fieldCode="DE" term="%22Research+evaluation%22">Research evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Artificial Intelligence (AI) music generation is a trending field, and many different generators are currently under development. However, no standardized evaluation method exists that can help researchers evaluate and compare AI-based music tools. To create a meta-methodology for AI music assessment based on user evaluation, that can be both standardized and deployed as a tailored implementation model adapted to the idiosyncrasies of specific generators and their intended applications, thereby helping future researchers draw comparisons between different systems. Two different decision trees/matrices are proposed to help researchers tailor their specific evaluation studies. As evaluation tools, the paper explores Likert and analytical hierarchy process (AHP) based surveys and emotional state estimations using facial action units, self-assessment, and physiological signals. A proof-of-concept study demonstrates the viability of the proposed tools for user-based AI music generation evaluation studies. A preference for audio music over symbolic music generation was observed, and this will require future research. The implementation of the proposed methodology and tools across the field will be helpful when comparing different systems in future research and to save time in the development of user-based studies. User-based evaluation studies are needed to prevent biases from passing into future iterations of AI music generators. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10447318.2025.2478265 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 13906 Subjects: – SubjectFull: Analytic hierarchy process Type: general – SubjectFull: Likert scale Type: general – SubjectFull: System analysis Type: general – SubjectFull: Musical composition Type: general – SubjectFull: Research evaluation Type: general – SubjectFull: Evaluation methodology Type: general – SubjectFull: Sentiment analysis Type: general Titles: – TitleFull: A Meta-Methodology for User Evaluation of Artificial Intelligence Generated Music; Using the Analytical Hierarchy Process, Likert and Emotional State Estimations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Civit, Miguel – PersonEntity: Name: NameFull: Drai-Zerbib, Véronique – PersonEntity: Name: NameFull: Cuadrado, Francisco – PersonEntity: Name: NameFull: Escalona, Maria J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10447318 Numbering: – Type: volume Value: 41 – Type: issue Value: 21 Titles: – TitleFull: International Journal of Human-Computer Interaction Type: main |
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