Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing. The Springer International Series in Engineering and Computer Science, Band 606;

Tong, Zhang and C.C. Jay Kuo:

ISBN 10: 0792372875 / ISBN 13: 9780792372875

Published by Kluwer;, 2001

About this Item

136 Seiten; Das hier angebotene Buch stammt aus einer teilaufgelösten wissenschaftlichen Bibliothek und trägt die entsprechenden Kennzeichnungen (Rückenschild, Instituts-Stempel.); Schnitt und Einband sind etwas staubschmutzig; der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. Text in ENGLISCHER Sprache! Sprache: Englisch Gewicht in Gramm: 420.

Seller Inventory # 1574657

Bibliographic Details

• Title: Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing. The Springer International Series in Engineering and Computer Science, Band 606
• Publisher: Kluwer
• Publication Date: 2001
• Language: English
• ISBN 10: 0792372875
• ISBN 13: 9780792372875
• Binding: gebundene Ausgabe
• Condition: Gut

About this title

Synopsis

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.