Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing 2001 Edition Contributor(s): Tong Zhang (Author), Kuo, C. C. Jay (Author) |
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ISBN: 0792372875 ISBN-13: 9780792372875 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: January 2001 Annotation: 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. |
Additional Information |
BISAC Categories: - Technology & Engineering | Telecommunications - Computers | Interactive & Multimedia - Computers | Computer Science |
Dewey: 025.347 |
LCCN: 00066406 |
Series: The Springer International Engineering and Computer Science |
Physical Information: 0.6" H x 6.3" W x 9.64" (0.88 lbs) 136 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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. |