Aesthetic Matters in Music Perception for Image Stylization: A Emotion-driven Music-to-Visual Manipulation
This work addresses the problem of enhancing emotional expression in images for creative industries and interactive technologies, representing an incremental advancement in multimodal emotional integration.
The paper tackles the challenge of translating musical emotions into visual images by introducing EmoMV, a method that manipulates images based on music elements like pitch and rhythm, resulting in effective translation of emotional content into visually compelling images as evaluated through image quality metrics, aesthetic assessments, and EEG measurements.
Emotional information is essential for enhancing human-computer interaction and deepening image understanding. However, while deep learning has advanced image recognition, the intuitive understanding and precise control of emotional expression in images remain challenging. Similarly, music research largely focuses on theoretical aspects, with limited exploration of its emotional dimensions and their integration with visual arts. To address these gaps, we introduce EmoMV, an emotion-driven music-to-visual manipulation method that manipulates images based on musical emotions. EmoMV combines bottom-up processing of music elements-such as pitch and rhythm-with top-down application of these emotions to visual aspects like color and lighting. We evaluate EmoMV using a multi-scale framework that includes image quality metrics, aesthetic assessments, and EEG measurements to capture real-time emotional responses. Our results demonstrate that EmoMV effectively translates music's emotional content into visually compelling images, advancing multimodal emotional integration and opening new avenues for creative industries and interactive technologies.