Advanced SearchSearch Tips
Feature Extraction Algorithm for Underwater Transient Signal Using Cepstral Coefficients Based on Wavelet Packet
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Feature Extraction Algorithm for Underwater Transient Signal Using Cepstral Coefficients Based on Wavelet Packet
Kim, Juho; Paeng, Dong-Guk; Lee, Chong Hyun; Lee, Seung Woo;
  PDF(new window)
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.
Underwater transient signal recognition;Wavelet packet filter bank;Feature extraction;Gaussian mixture model;
 Cited by
Coifman, R.R., Wickerhauser, M.V., 1992. Entropy-based Algorithm for Best Basis Selection. IEEE Transactions on Information Theory, 38(2), 713-718. crossref(new window)

Coifman, R.R., Meyer, Y., Wickerhauser, V., 1992. Wavelet Analysis and Signal Processing. Wavelets and their Applications, 153-178.

Han, H.Y., 2009. Introduction to Pattern Recognition, Hanbit Media Inc, 184-213.

Kundu, A., Chen, G.C., Persons, C.E., 1994. Transient Sonar Signal Classification Using Hidden Markov Models and Ueural Nets. IEEE Journal of Oceanic Engineering, 19(1), 87-99. crossref(new window)

Lim, T.G., Bae, K.S., Hwang, C.S., Lee, H.U., 2007. Classification of Underwater Trasient Signals Using MFCC. The Journal of Korea Information and Communications Society, 32(8), 675-680.

Mallat, S., 1999. A Wavelet Tour of Signal Processing. Academic Press.

Oliveira, P.M., Lobo, V., Barroso, V., Moura-Pires, F., 2002. Detection and Classification of Underwater Transients with Data Driven Methods Based on Time-frequency Distributions and Non-parametric Classifiers. OCEANS '02 MTS/IEEE, 1(1), 12-16.

Pavez, E., Silva, J.F., 2012. Analysis and Design of Wavelet-Packet Cepstral Coefficients for Automatic Speech Recognition. Speech Communication, 54(6), 814-835. crossref(new window)

Tucker, S., 2003. An Ecological Approach to the Classification of Transient Underwater Acoustic Events: Perceptual Experiments and Auditory Models. PhD Thesis. University of Sheffield.