Encyclopedia of Database Systems

Living Edition
| Editors: Ling Liu, M. Tamer Özsu

Audio Segmentation

  • Lie Lu
  • Alan Hanjalic
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7993-3_1033-2

Synonyms

Definition

Audio segmentation refers to the class of theories and algorithms designed to automatically reveal semantically meaningful temporal segments in an audio signal, also referred to as auditory scenes [7]. These scenes can be seen as equivalents of paragraphs in text, and can serve as input into audio categorization processes, either supervised (audio classification) or unsupervised (audio clustering). Through these processes, semantically similar auditory scenes can be grouped together and/or labeled using semantic indexes to provide multi-level, non-linear content-based access to large audio documents and collections.

Historical Background

Automatic detection of auditory scenesis an important step in enabling high-level semantic inference from general audio signals, and can benefit various content-based applications involving both audio and multimodal (multimedia) data sets. Traditional approaches to audio segmentation usually...

Keywords

Spectral Cluster Importance Weight Inverse Document Frequency Audio Feature Audio Segment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Cai R, Lu L, Hanjalic A Unsupervised content discovery in composite audio. In: Proceedings of the 13th ACM International Conference on Multimedia; 2005. p. 628–37.Google Scholar
  2. 2.
    Foote J. Automatic audio segmentation using a measure of audio novelty. In: Proceedings of the IEEE International Conference on Multimedia and Expo; 2000. p. 452–5.Google Scholar
  3. 3.
    Hanjalic A, Lagendijk RL, Biemond J. Automated high-level movie segmentation for advanced video-retrieval systems. IEEE Trans Circuits Syst Video Technol. 1999;9(4):580–8.CrossRefGoogle Scholar
  4. 4.
    Kozima H. Text segmentation based on similarity between words. In: Proceedings of the 31st Annual Meeting on Association for Computational Linguistics; 1993. p. 286–8.Google Scholar
  5. 5.
    Kender JR, Yeo B-L. Video scene segmentation via continuous video coherence. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition; 1998. p. 367–73.Google Scholar
  6. 6.
    Lu L, Cai R, Hanjalic A. Towards a unified framework for content-based audio analysis. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing; 2005. p. 1069–72.Google Scholar
  7. 7.
    Lu L, Cai R, Hanjalic A. Audio elements based auditory scene segmentation. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing; 2006. p. 17–20.Google Scholar
  8. 8.
    Lu L., Hanjalic A. Towards optimal audio keywords detection for audio content analysis and discovery. In: Proceedings of the 14th ACM International Conference on Multimedia; 2006. p. 825–34.Google Scholar
  9. 9.
    Ng AY, Jordan MI, Weis Y. On spectral clustering: analysis and an algorithm. In: Proceedings of the Advances in Neural Information Processing Systems; 2001. p. 849–56.Google Scholar
  10. 10.
    Sundaram H, Chang S-F. Audio scene segmentation using multiple features, models and timescales. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing; 2000. p. 2441–4.Google Scholar
  11. 11.
    Tzanetakis G, Cook P. Multifeature audio segmentation for browsing and annotation. In: Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics; 1999. p. 103–6.Google Scholar
  12. 12.
    Wang D, Lu L, Zhang H-J. Speech segmentation without speech recognition. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing; 2003. p. 468–71.Google Scholar
  13. 13.
    Xu M, Maddage N, Xu C-S, Kankanhalli M, Tian Q. Creating audio keywords for event detection in soccer video. In: Proceedings of the IEEE International Conference on Multimedia and Expo; 2003. p. 281–4.Google Scholar
  14. 14.
    Zelnik-Manor L, Perona P. Self-tuning spectral clustering. In: Proceedings of the Advances in Neural Information Processing Systems; 2004. p. 1601–8.Google Scholar

Copyright information

© Springer Science+Business Media LLC 2016

Authors and Affiliations

  1. 1.Microsoft Research AsiaBeijingChina
  2. 2.Delft University of TechnologyDelftThe Netherlands

Section editors and affiliations

  • Vincent Oria
    • 1
  • Shin'ichi Satoh
    • 2
  1. 1.Dept. of Computer ScienceNew Jersey Inst. of TechnologyNewarkUSA
  2. 2.Digital Content and Media Sciences ReseaMultimedia Information Research DivisionNational Institute of InformaticsTokyoJapan