Video Echoed in Harmony: Learning and Sampling Video-Integrated Chord Progression Sequences for Controllable Video Background Music Generation

Abstract

Automatically generating video background music mitigates the inefficiency and time-consuming drawbacks of current manual video editing. Two key challenges hinder the expansion of the inception of video-to-music tasks. 1) Limited availability of high-quality video–music datasets and annotations. 2) Absence of music generation methods that consider actual musicality, which are controlled by interpretable factors based on music theory. In the article, we propose video echoed in harmony (VEH), a method for learning and sampling video-integrated chord progression sequences. Our approach adopts harmony, represented by chord progressions that are aligned with various music formats [musical instrument digital interface (MIDI), audio, and score], imitating chord precedence in human music composition. Visual-language models link visual features to chord progressions through genre labels and descriptive words in generated textualized videos. The two aforementioned features collectively obviate the necessity of extensive video–music paired data. Besides, an energy-based chord progression learning and sampling algorithm quantifies abstract harmony impressions to statistical features, serving as interpretable factors for the controllable music generation based on music theory. Experimental results demonstrate that the proposed method outperforms the state-of-the-art, producing a superior music alignment for the given video.