Background Music Search System to an Input Video Using Factor Analysis for Impression Words

Abstract

The use of video clip sharing sites and applications has become popular in the recent years. However, when video contributors were looking for sound sources that could be used as background music, it took a lot of time to actually listen to a number of songs to confirm if they fit the video. The author of this study believes that video contributors would improve their production efficiency if there is a system that automatically lists up suitable background music for videos. In the proposed method, the features for the videos are formulated using color and optical flow histograms. Mel frequency cepstrum coefficients, which have often been used in the recent deep learning research, are used for the music features. A function that converts these features to linguistic evaluation values is obtained by multiple regression analysis. The top five music files with the highest similarity to the input video are calculated to evaluate the proposed system. Whether they are compatible with the respondents’ ranking is then checked. The result shows that the proposed method via impression words can perform a more accurate retrieval than the learning network that directly connects video and music features.