Content-Based Searching in Group Communication Systems

Content-Based Searching in Group Communication Systems

Gábor Richly (Budapest University of Technology and Economics, Hungary), Gábor Hosszú (Budapest University of Technology and Economics, Hungary) and Ferenc Kovács (Budapest University of Technology and Economics, Hungary)
Copyright: © 2008 |Pages: 7
DOI: 10.4018/978-1-59904-000-4.ch017
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Abstract

The importance of real-time pattern recognition in streaming media is rapidly growing (Liu, Wang, & Chen, 1998). Extracting information from audio and video streams in an e-collaboration scenario is getting increasing relevance as the networking infrastructure develops. This development enables the use of rich media content. Shared archives of this kind of knowledge need tools for exploration, navigation and searching. As an example, to filter out redundant copies of an audio record, added by different members of an e-community, helps to keep the knowledge-base clean and compact.

Key Terms in this Chapter

Audio Signal Processing: Means coding, decoding, playing and content handling of audio data files and streams.

Content-Based Recognition: The media data are identified based on its content and not on the attributes of its file. It is also called content-sensitive searching.

Synchronization: Procedure carried out to find the appropriate points of two or more streams to form correct paralleling.

Bark-Scale: Nonlinear frequency scale modeling the resolution of the human hearing system. 1 Bark distance on the Bark-scale equals to the so called critical bandwidth that is linearly proportional to the frequency under 500Hz and logarithmically above that. The critical bandwidth can be measured by the simultaneous frequency masking effect of the ear.

Manhattan-Distance: The L1 metric for the points of the Euclidean space defined by summing the absolute coordinate differences of two points (|x2-x1| + |y2-y1| + …). Also known as “city block” or “taxi-cab” distance; a car drives this far in a lattice-like street pattern.

Client/Server Model: Communicating method, where one host has more functionality than the other. It differs from the P2P model (see below).

Application Level Network (ALN): Applications running in the hosts can create a virtual network from their logical connections. The virtual network is called overlay (see below). Such software entities are not able to communicate with each other without knowing their logical relations. The most cases this ALN software entities use the P2P model (see below), not the client/server (see below) one for the communication.

Pattern Recognition: Procedure of finding a certain series of signals in a longer data file or signal stream.

Peer-to-Peer (P2P) Model: Communication method where each node has the same authority and communication capability. They create a virtual network, overlaid on the Internet. Its members organize themselves into a topology for data transmission.

Overlay: Applications, that create an ALN (see above), work together, and usually follow the P2P communication model (see below).

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