The Creative Performance of the AI Agents ChatGPT and Google Magenta Compared to Human-Based Solutions in a Standardized Melody Continuation Task
Die Leistungen der Künstlichen Intelligenzen ChatGPT und Google Magenta im Vergleich mit Musikstudierenden bei einer standardisierten Melodie-Fortsetzungsaufgabe
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Abstract
Many generative artificial intelligence (AI) systems have been developed over the last decade. Some systems are more of a generic character, and some are specialized in music composition. However, whether these AI systems are serious competitors for human composers remains unclear. Despite increased public interest, there is currently little empirical foundation for a conceivably equivalent performance for creative AI when compared to human experts in a controlled task. Thus, we conducted an online experiment to evaluate the subjectively perceived quality of AI compositions with human-made products (by music students) in a standardized task. Based on a melody continuation paradigm, creative products using AI were generated by the AI agents ChatGPT (Version 3.5) and Google Magenta Studio (Version 2.0). The human creative performances were realized by 57 melodic continuations, composed by music students. In the online evaluation study, listeners (N = 71, mainly musicians) rated the aesthetic quality of the outcomes of the various systems. Additionally, the raters’ musical experience level was controlled as well as the length of the given melody completion task (two probe positions). As a main result, the overall quality of the AI compositions was rated significantly lower on all four target items compared to the human-made products (large effect sizes). Musical experience and the length of the melody did not influence the ratings. We conclude that the current capabilities of AI in the domain of musical creativity determined by a standardized composition task are far below human capabilities. However, we assume rapid progress will be made in the domain of generative music-specific AI systems.
artifical intelligence; AI; composition; generative AI; empirical aesthetics; creativity