Abstract: In this paper, we introduce an interactive background music synthesis algorithm guided by visual content. We leverage a cascading strategy to synthesize background music in two stages: Scene Visual Analysis and Background Music Synthesis. First, seeking a deep learning-based solution, we leverage neural networks to analyze the sentiment of the input scene. Second, real-time background music is synthesized by optimizing a cost function that guides the selection and transition of music clips to maximize the emotion consistency between visual and auditory criteria, and music continuity. In our experiments, we demonstrate the proposed approach can synthesize dynamic background music for different types of scenarios. We also conducted quantitative and qualitative analysis on the synthesized results of multiple example scenes to validate the efficacy of our approach.
Authors: Yujia Wang, Wei Liang, Wanwan Li, Dingzeyu Li, Lap-Fai Yu (Beijing Institute of Technology, George Mason University, Adobe Research)