Perceptual differences between AI and human compositions: the impact of musical factors and cultural background.

The issues of what Artifcial Intelligence (AI) can and cannot do in the feld of music are among the important topics that both music researchers and AI experts are curious about. This study offers a signifcant analysis within the context of the growing role of AI technologies in music composition and their impact on creative processes. It contributes to the literature by positioning AI as a complementary tool to the composer's creativity and by enhancing the understanding of cultural adaptation processes. The study aims to identify the perceptual differences between AI and composer compositions, examine the musical and cultural foundations of these differences, and uncover the factors that infuence the listener's experience. In the research design, a mixed-method approach was adopted, combining qualitative and quantitative research methods. In the quantitative phase, a double-blind experimental design was employed to ensure that participants evaluated composer and AI works impartially. In the qualitative phase, participants' opinions were gathered. The participants were 10 individuals aged between 19 and 25, with diverse cultural and educational backgrounds; 6 had received formal music education, while 4 were casual listeners. The data collection instruments included a structured interview form and the Assessment Scale for Perceptual Factors in Musical Works. During the research process, each participant evaluated two AI and two composer works in 20-minute standardized listening sessions. All listening sessions were conducted using professional audio equipment. The analysis revealed that composer works scored signifcantly higher than AI works across all categories (p<.05). Notable differences were observed, particularly in the categories of emotional depth (X_composer = 4.6, X_AI = 3.1) and memorability (X_composer = 4.4, X_AI = 3.2). The study concluded that composer works were more effective than AI compositions in terms of emotional depth, structural coherence, and cultural resonance. Additionally, cultural background and music education emerged as signifcant factors shaping perceptual differences. Future research should broaden the participant pool and incorporate neurocognitive data to facilitate a deeper understanding of perceptual mechanisms. Furthermore, the development of AI systems for use in music should include the integration of Transformer and RNN-based advanced learning models, the implementation of traditional music theory principles, the enhancement of emotional expressiveness, the improvement of cultural adaptation capacities, and the refnement of real-time interaction mechanisms. [ABSTRACT FROM AUTHOR]