Classify of Underwater Target Utilizing Audio Fingerprint

As well known, because of the complicated watery environment and the limitation of the detecting method, recognizing the underwater target precisely is always a Gordian knot to all countries. In this paper, we designed a genetic-based classifier system (CS) which recognizes a target utilizes audio fingerprints. Audio fingerprint technology extracts some unique feature from a given sound clip, and the classifier system does some classifying and recognizing works according to those features. In order to improve the performance of the classifier system, we designed some techniques: the comparing and matching algorithm would give the fitness value more explicitly statistical meaning; the hyperplasia operator gives the system persistent learning abilities, the refining classifier merges redundant rules and shrinks the rule set, and an alterable mutation probability increases the speed and the accuracy of the classifying operation. Experiments show that it's competent for the recognition