• The Journal of the Acoustical Society of Korea
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• v.31 no.8
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• pp.532-541
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• 2012

An underwater transient signal is distinguished from an ambient noise. Database for the underwater transient signal is required since the underwater transient signal shows various characteristics depending on acoustic features. In the paper, hence, sound mask-filter was applied to extract the transient signals which exist temporally and locally in the ocean. The standard signal was chosen and cross-correlated with the raw signal. A mask-filter for a transient signal was obtained using the threshold which was decided by the maximum likelihood method in the envelope of the cross-correlated signal. Using the sound mask-filter, the transient signal of a sea catfish {Galeichthys felis (Linnaeus)} was extracted from the underwater ambient noise. Similarly, the man-made signal was added into the noise and it was extracted by the same method. We also have demonstrated the significance of the transient signal through comparing the extracted signals depending on the standard signal. In the results, the proposed method, sound mask-filtering, could be utilized as a database construction of the transient signals in underwater noise. Particularly, this study would be useful to extract the wanted signal from arbitrary signals.

• Journal of the Society of Naval Architects of Korea
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• v.38 no.3
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• pp.117-122
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• 2001

The shock test of shipboard equipments is performed for the evaluation of the shock-resistant. capability by analyzing the maximum acceleration, the effective time duration and the shock response spectrum, etc. But some measured signals have impulsive noise and gaussian white noise because of the ambient noise, the acquisition equipment error and the transient movement of cables during the shock test. The improved transient signal analysis method which removes the noise of measured signal using the threshold policy of the median filter and the orthogonal wavelet coefficients is proposed. It was verified that the signal-to-noise ratio was improved about 30dB by the numerical simulation. And the shock response spectrum was extracted using the denoised shock response signal which was applied by this proposed method.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.