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http://dx.doi.org/10.5574/KSOE.2014.28.6.552

Feature Extraction Algorithm for Underwater Transient Signal Using Cepstral Coefficients Based on Wavelet Packet  

Kim, Juho (Department of Ocean System Engineering, Jeju National University)
Paeng, Dong-Guk (Department of Ocean System Engineering, Jeju National University)
Lee, Chong Hyun (Department of Ocean System Engineering, Jeju National University)
Lee, Seung Woo (Agency for Defence Development)
Publication Information
Journal of Ocean Engineering and Technology / v.28, no.6, 2014 , pp. 552-559 More about this Journal
Abstract
In general, the number of underwater transient signals is very limited for research on automatic recognition. Data-dependent feature extraction is one of the most effective methods in this case. Therefore, we suggest WPCC (Wavelet packet ceptsral coefficient) as a feature extraction method. A wavelet packet best tree for each data set is formed using an entropy-based cost function. Then, every terminal node of the best trees is counted to build a common wavelet best tree. It corresponds to flexible and non-uniform filter bank reflecting characteristics for the data set. A GMM (Gaussian mixture model) is used to classify five classes of underwater transient data sets. The error rate of the WPCC is compared using MFCC (Mel-frequency ceptsral coefficients). The error rates of WPCC-db20, db40, and MFCC are 0.4%, 0%, and 0.4%, respectively, when the training data consist of six out of the nine pieces of data in each class. However, WPCC-db20 and db40 show rates of 2.98% and 1.20%, respectively, while MFCC shows a rate of 7.14% when the training data consists of only three pieces. This shows that WPCC is less sensitive to the number of training data pieces than MFCC. Thus, it could be a more appropriate method for underwater transient recognition. These results may be helpful to develop an automatic recognition system for an underwater transient signal.
Keywords
Underwater transient signal recognition; Wavelet packet filter bank; Feature extraction; Gaussian mixture model;
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