focus on generating MIDI files, our proposed model generates chord sequences

that match the video content. Our decision to work with chords is largely due

to the lack of symbolic music-video datasets. Hence we first transcribed the

chords from the audio. Since it is much more accurate to transcribe chords

compared to polyphonic MIDI (Cheuk et al., 2023), we opted to train on chords

as this would propagate the least amount of error. In a post-processing step,

these chords are appropriately arpeggiated and rendered expressively to further

match the mood of the video and create a richer, more intricate sound.

2.2. Music Generation from Videos

The number of papers purely on music generation for video can be counted

on one hand. However, there has been related work leading up to this. Below we

expand on several papers related to generating music that serves as a narrative,

be it for games, video, reconstructing instrument sounds, or other purposes.
Video2Music