Earticle

초록

영어

Accurate audio segmentation has recently received increasing attention for its applications in automatic indexing, content analysis and information retrieval. Hence, this paper proposes a highly accurate audio segmentation methodology using a genetic algorithm-based approach to adapting and optimizing segmentation window lengths. Specifically, this paper analyzes the parameter sequence of the root-mean-square values of an input audio stream with optimal sliding window (or segmentation window) lengths found and adapted by a genetic algorithm. In addition, this paper determines whether an audio-cut occurs or not by utilizing the parameter sequences as inputs of a support vector machine. Experimental results indicate that the proposed approach achieves 100.00% and 98.69% in the average precision and recall rates of segmentation performance, respectively.

목차

Abstract
 1. Introduction
 2. Background Information
  2.1. Support Vector Machine (SVM)
  2.2. Genetic Algorithm (GA)
 3. Proposed Audio Segmentation Methodology
 4. Experimental Results
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Myeongsu Kang [ Department of Electrical, Electronic and Computer Engineering, University of Ulsan, De 93 Daehak –ro, Nam-gu, Ulsan 680749, Korea ]
• Jong-Myon Kim [ Department of Electrical, Electronic and Computer Engineering, University of Ulsan, De 93 Daehak –ro, Nam-gu, Ulsan 680749, Korea ] Corresponding author.

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605