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Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing

  • Book
  • © 2001

Overview

Authors:
  1. Tong Zhang

    1. Integrated Media Systems Center, University of Southern California, Los Angeles, USA

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  2. C.-C. Jay Kuo

    1. Department of Electrical Engineering — Systems, University of Southern California, Los Angeles, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 606)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xx
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Tong Zhang, C.-C. Jay Kuo
      Pages 3-19

  3. Video Content Modeling

    1. Front Matter

      Pages 21-21
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    2. Video Content Modeling

      • Tong Zhang, C.-C. Jay Kuo
      Pages 23-32

  4. Audio Content Analysis

    1. Front Matter

      Pages 33-33
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    2. Audio Feature Analysis

      • Tong Zhang, C.-C. Jay Kuo
      Pages 35-54

    3. Generic Audio Data Segmentation and Indexing

      • Tong Zhang, C.-C. Jay Kuo
      Pages 55-67

    4. Sound Effects Classification and Retrieval

      • Tong Zhang, C.-C. Jay Kuo
      Pages 69-81

  5. Image Sequence Analysis

    1. Front Matter

      Pages 83-83
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    2. Image Sequence Analysis

      • Tong Zhang, C.-C. Jay Kuo
      Pages 85-104

  6. Experimental Results

    1. Front Matter

      Pages 105-105
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    2. Experimental Results

      • Tong Zhang, C.-C. Jay Kuo
      Pages 107-120

  7. Conclusion

    1. Front Matter

      Pages 121-121
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    2. Conclusion and Extensions

      • Tong Zhang, C.-C. Jay Kuo
      Pages 123-128

  8. Back Matter

    Pages 129-136
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Keywords

About this book

Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing is an up-to-date overview of audio and video content analysis. Included is extensive treatment of audiovisual data segmentation, indexing and retrieval based on multimodal media content analysis, and content-based management of audio data. In addition to the commonly studied audio types such as speech and music, the authors have included hybrid types of sounds that contain more than one kind of audio component such as speech or environmental sound with music in the background. Emphasis is also placed on semantic-level identification and classification of environmental sounds. The authors introduce a new generic audio retrieval system on top of the audio archiving schemes. Both theoretical analysis and implementation issues are presented. The developing MPEG-7 standards are explored.
Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing will be especially useful to researchers and graduate level students designing and developing fully functional audiovisual systems for audio/video content parsing of multimedia streams.

Authors and Affiliations

  • Integrated Media Systems Center, University of Southern California, Los Angeles, USA

    Tong Zhang

  • Department of Electrical Engineering — Systems, University of Southern California, Los Angeles, USA

    C.-C. Jay Kuo

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