Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing
Nonfiction, Computers, Database Management, Information Storage & Retrievel, Application Software, Multimedia, General Computing
| Author: | Tong Zhang, C.C. Jay Kuo | ISBN: | 9781475733396 |
| Publisher: | Springer US | Publication: | March 9, 2013 |
| Imprint: | Springer | Language: | English |
| Author: | Tong Zhang, C.C. Jay Kuo |
| ISBN: | 9781475733396 |
| Publisher: | Springer US |
| Publication: | March 9, 2013 |
| Imprint: | Springer |
| Language: | English |
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.
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.
