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Audio Segmentation

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems

  • 167 Accesses

Synonyms

Audio parsing; Auditory scene detection

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...

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Recommended Reading

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Author information

Authors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Lie Lu

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

Authors
  1. Lie Lu
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  2. Alan Hanjalic
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Corresponding author

Correspondence to Lie Lu .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Dept. of Computer Science, New Jersey Inst. of Technology, Newark, NJ, USA

    Vincent Oria

  2. Digital Content and Media Sciences ReseaMultimedia Information Research Division, National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

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Lu, L., Hanjalic, A. (2018). Audio Segmentation. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1033

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