Music synchronization with video using emotion similarity

Ki Ho Shin, In Kwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we introduce a synchronization method of music and video, where the music is arranged with the video using emotion similarity. The music segments are matched with the video segments so that the music best follows the video's narrative. The temporal order of video segments are unchanged. Due to the disparity of structural musical content between segment boundaries, we design a cost function to overcome the problem. We perform a user study where the participants rate the overall quality of each synchronized video and show that our method generates a promising result.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-50
Number of pages4
ISBN (Electronic)9781509030156
DOIs
Publication statusPublished - 2017 Mar 17
Event2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 - Jeju Island, Korea, Republic of
Duration: 2017 Feb 132017 Feb 16

Publication series

Name2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017

Other

Other2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
CountryKorea, Republic of
CityJeju Island
Period17/2/1317/2/16

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Shin, K. H., & Lee, I. K. (2017). Music synchronization with video using emotion similarity. In 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 (pp. 47-50). [7881714] (2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2017.7881714