Narrow Results

Extraction of effective features plays a key role in pattern recognition. A large number of patterns, such as speech, radar signals, earthquake signals, handwriting, etc. are of non-stationary signals or exhibit time-varying behavior. The features of these patterns are often located in both the time and frequency domains. The traditional methods fail to extract such kind of features. Fortunately, wavelet packet transform (WPT) can provide an arbitrary time-frequency decomposition for the signals, because a wavelet packet (WP) library contains many WP bases, which can handle the different components of a signal. Therefore, by selecting a suitable basis, which is called "best basis", the effective features can be extracted. In this paper, three criteria are used to select the best WPT basis, namely: (1) distance criterion, (2) divergence criterion and (3) entropy criterion. Three algorithms to implement the above criteria are also provided. Experiments are conducted and the positive results are obtained.