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Vector Quantization of Reference Signals for Efficient Frame-Based Classification of Underwater Transient Signals  

Lim, Tae-Gyun (삼성전자)
Kim, Tae-Hwan (경북대학교 전자전기컴퓨터학부)
Bae, Keun-Sung (경북대학교 전자전기컴퓨터학부)
Hwang, Chan-Sik (경북대학교 전자전기컴퓨터학부)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.34, no.2C, 2009 , pp. 181-185 More about this Journal
Abstract
When we classify underwater transient signals with frame-by-frame decision, a database design method for reference feature vectors influences on the system performance such as size of database, computational burden and recognition rate. In this paper the LBG vector quantization algorithm is applied to reduction of the number of feature vectors for each reference signal for efficient classification of underwater transient signals. Experimental results have shown that drastic reduction of the database size can be achieved while maintaining the classification performance by using the LBG vector quantization.
Keywords
Underwater transient signal classification; MFCC; LBG clustering algorithm; SONAR;
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Times Cited By KSCI : 2  (Citation Analysis)
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