An Objective Metric Towards Music Mood Visualization

Music visualization is presented in different formats. The mainstream music visualization remains focused on providing animated images for visual pleasure or for enhancing the perception of sheet music with colors. Static visualization of music, on the other hand, has the potential to provide quick insights into the music’s attributes, particularly its mood. However, despite our previous work exploring several approaches to music mood visualization, a major problem still exists in how to objectively evaluate the mood of music pieces and the generated images. Thus, this paper proposes IMEMNextan improvement to the existing framework IMEMNet [1], which serves as a novel and objective metric for music mood visualization. Not only does it allow for a user study-free evaluation of music mood visualization, but the new network also enables for creation of datasets of music-image pairs with the same mood. The new IMEMNext network achieved a significant performance improvement compared to its original implementation.