Background music recommendation for video based on multimodal latent semantic analysis
Fang-Fei Kuo, Man-Kwan Shan, Suh-Yin Lee · 2013
Automatic video editing is receiving increasingly attention as the digital camera technology develops further and social media sites such as YouTube and Flickr become popular. Background music selection is one of the key elements to make the generated video attractive. In this work, we propose a framework for background music recommendation based on multi-modal latent semantic analysis between video and music. The videos and accompanied background music are collected from YouTube, and the videos with low musicality are filtered out by musicality detection algorithm. The co-occurrence relationships between audiovisual features are derived for multi-modal latent semantic analysis. Then, given a video, a ranked list of recommended music can be derived from the correlation model. In addition, we propose an algorithm for music beat and video shot alignment to calculate the alignability of recommended music and video. The final recommendation list is the combined result of both content correlation and alignability. Experiments show that the proposed method achieves a promising result.