AI & ML
Art2Song: Enhancing Visual Art Appreciation with Contextual Music Generation
arXiv cs.HC0 views
arXiv:2609.06044v2 Announce Type: replace Abstract: Art2Song is a conceptual framework that expresses artworks as sound by separating Non-Visual Context, which is difficult to perceive from the image alone, from Visual Evidence. Visual Evidence, such as objects, colors, and spatial composition, is transformed into Lyrics, while the Contextual Mood derived from the historical and art-historical context in the museum's artwork description is reflected in the background soundtrack. Rather than describing artworks textually, Art2Song aims to explore the possibility of a new mode of art appreciation in which viewers experience hidden stories and emotional context through music. As future interaction directions, we plan an Emotional Layer Blending Slider interface and a structured, traceable song-generation scenario, presenting the possibility that users can explore the relationship between Visual Evidence and Contextual Mood.
Source：https://arxiv.org/abs/2609.06044PreviousCharacterising divergence between heuristic and language-model ratings of mobile user interface qualityNextWhose Guardrail? User Identity Shapes the Effectiveness of Model Disclaimers in Mitigating LLM Political Persuasion
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