Skip to main content
Article thumbnail
Location of Repository

International Journal of Innovations in Engineering and Technology (IJIET) Affective Music Video Content Retrieval Features Based on Songs

By R. Hemalatha

Abstract

Abstract — Nowadays, MTV has become an important favorite pastime to modern people because of its conciseness, convenience to play and the characteristic that can bring both audio and visual experiences to audiences. An affective MTV analysis framework, which realizes MTV affective state extraction, is representation and clustering. Firstly, affective features are extracted from both audio and visual signals. Affective state of each MTV is modeled with 2D dimensional affective model and visualized in the Arousal-Valence space. Finally the MTVs having similar affective states are clustered into same categories. The validity of proposed framework is proved by subjective user study and related work proves that our features improve the performance by a significant margin. Index Terms — Categorical affective content analysis, affective visualization, dimensional affective model, Hidden Markov Models(HMMs). I

Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.359.9181
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://ijiet.com/wp-content/up... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.