Title :
Automatic Mash Up Music Video Generation System by Remixing Existing Video Content
Author :
Ohya, Hayato ; Morishima, Satoru
Author_Institution :
Dept. Adv. Sci. & Eng., Waseda Univ., Tokyo, Japan
Abstract :
Music video is a short film which presents a visual representation of recent music. In these days, there is a trend that amateur users create music video in the video sharing website. Especially, the music video which is created by cutting and pasting existing video is called mashup music video. In this paper, we proposed the system that users can easily create mushup music video by using existing music videos. In addition, we conducted assessment evaluation experiment for our system. The system firstly extracts music features and video features from existing music videos. Then, the each feature is clustered and the relationship between each feature is learned by Hidden Markov Model. At last, the system cuts learned video scene which is the closest feature among learned videos and pastes it synchronizing with input song. Experiment shows that our method can generate more synchronized video than a previous method.
Keywords :
Web sites; feature extraction; hidden Markov models; learning (artificial intelligence); music; video signal processing; Hidden Markov Model; automatic mash up music video generation system; music feature extraction; music videos; video content; video sharing Web site; visual representation; Feature extraction; Hidden Markov models; Markov processes; Synchronization; Training; Video sequences; Viterbi algorithm; machine learning; music analysis; music video; video content analysis;
Conference_Titel :
Culture and Computing (Culture Computing), 2013 International Conference on
Conference_Location :
Kyoto
DOI :
10.1109/CultureComputing.2013.44