AI-GENERATED VISUALIZATIONS OF MUSICAL PATTERNS - Routray, Pravat Kumar; Nagpal, Manish; Gupta, Megha; Singh, Amanveer (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The paper will discuss the new area of AI-composed visualizations of musical patterns, which is a convergence of music theory, computational creativity, and visual arts. It explores how such audio formats as pitch, rhythm, harmony, and timbre can be produced using artificial intelligence via visual media in a dynamic and aesthetically coherent format. The research places this change in the context of interdisciplinary concepts, taking into account the aspects of perceptual, cognitive, and ethical. The system recapitulates the objective and the affective aspects of music by using neural networks that have been trained on multimodal data and relates them to the visual parameter of color, geometry and motion. The proposed system architecture combines the steps of the audio feature extraction, representation learning, and visual synthesis. As a result of experimental production and qualitative analysis, a number of recurring motifs and emergent visual structures are found, which represent associations between tonal density and color complexity, regularity in rhythm, and geometric symmetry, and harmonic consonance and spatial fluidity. The findings illustrate the interpretive depth of the AI-mediated sound-image translations to illustrate how artificial intelligence can generate work of art that can trigger aesthetic responses in the human condition. Nevertheless, the paper also notes that there are a number of limitations: the visualization in real-time is technically limited; the assessment of aesthetics is subjective by nature; and the biases of data in training corpora are potentially a problem concerning the reproducibility and the fairness.