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

웨이브렛 패킷 기반 캡스트럼 계수를 이용한 수중 천이신호 특징 추출 알고리즘

Kim, Juho;Paeng, Dong-Guk;Lee, Chong Hyun;Lee, Seung Woo

  • Received : 2014.06.14
  • Accepted : 2014.12.15
  • Published : 2014.12.31


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


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