AI-DRIVEN MUSIC CURATION AND VISUAL CULTURE: AUDIENCE PREFERENCE ANALYSIS IN DIGITAL CREATIVE PLATFORMS - Verma, Utkarsh; Chakraborty, Apurba; Mundada, Kapil; Vasani, Jay; Rakesh, Nitin; Khadse, Manisha Vilas (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Social music curation has turned into a hallmark of AI-powered creative platforms defining the way digital platform forms listening habits and the visual culture, as well as the way to engage with the audience. This paper discusses the connection between AI and music recommendation which applies visual aesthetics to analyse the audience preferences in a modern digital creative ecosystem. The issue being discussed involves the lack of transparency related to the formation of cultural preferences by algorithms and the insufficient knowledge about the impact of music -visual associations on the perception and retention of users. This study aims to simulate the trend of audience preference through the combined analysis of an audio characteristic, visual representation, and behavioral interaction information. The suggested approach utilizes multimodal deep learning, which is audio embedding networks, visual feature extractions models and attention-based preference learning to elicit cross-modal associations among sound, images, as well as user reaction. Data of large scale interaction on digital platforms is treated to recognize clusters of preferences, and changes in taste over time, and the interaction-based visual-music fit. The results of the experiments suggest that AI-based curation greatly improves the audience satisfaction, and the curation produces some measurable results in terms of the engagement time, the variety of its discoveries, and the subjective aesthetic cohesion. Findings also demonstrate that contextualized music recommendations House better than audio only systems in predicting user preferences and maintaining creative exploration.