OtoPittan: A Music Recommendation System for Making Impressive Videos
Takahiro Yoshida, Takahiro Hayashi · 2016
The impression of a video changes depending on the audio and visual content of the video. Adding appropriate background music to a video is an important process for making the video more impressive. In this paper, we present a system called OtoPittan which recommends background music for video based on the valence and arousal model. As input for the system, first a user registers a video with no background music, and then inputs a desired impression by setting the valence and the arousal level. The system recommends music clips as candidates of background music for the video. Recommended music clips are determined by minimizing the difference between the desired impression and the predicted impression calculated from the audio features of each candidate music and the visual features of the registered video. We have confirmed that with the proposed system users can quickly create videos giving desired impressions.