GPT-4 Driven Cinematic Music Generation Through Text Processing

This paper presents Herrmann-1 1 , a multimodal framework to generate background music tailored to movie scenes, by integrating state-of-the-art vision, language, music, and speech processing models. Our pipeline begins by extracting visual and speech information from a movie scene, performing emotional analysis on it, and converting these into descriptive texts. Then, GPT-4 translates these high-level descriptions into low-level music conditions. Finally, these text-based music conditions guide a text-to-music model to generate music that resonates with input movie scenes. Comprehensive objective and subjective evaluations attest to the high synthesis quality, congruence, and superiority of our pipeline.