• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.40 no.3
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• pp.189-195
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• 2004

The underwater sound of California gary whale was analyzed to discuss obtained results from the previous data to compare the underwater sound between Korean gray whale and California gray whale. The frequency of low frequency rumble which occupy about 50% of the underwater sound changed to max. 654Hz and the average of its lasted time was 570msec. The range of frequency variation was coincided as compared with the previous data. The range of frequency variation for the bubble type sounds and knocks was 24${\sim}$1029Hz, respectively. The average of lasted time was 1100msec and 1364msec, respectively. The range of frequency variation and lasted time of bubble type sounds was higher than the previous result while the sound of knocks was coincided. The range of frequency variation for the sound of bong, pluses and chirps was 34${\sim}$213Hz, 75${\sim}$360Hz and 120${\sim}$200Hz, respectively and the average of lasted time was 84msec, 873msec and 80msec, respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.147-152
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• 2018

Underwater transient signals are complex, variable and nonlinear, resulting in a difficulty in accurate modeling with reference patterns. We analyze one type of underwater transient signals, in the form of whale sounds, using the MFCC(Mel-Frequency Cepstral Constant) and synthesize them from the MFCC and the weighted $L_2$-norm minimization techniques. The whales in this experiments are Humpback whales, Right whales, Blue whales, Gray whales, Minke whales. The 20th MFCC coefficients are extracted from the original signals using the MATLAB programming and reconstructed using the weighted $L_2$-norm minimization with the inverse MFCC. Finally, we could find the optimum weighted factor, 3~4 for reconstruction of whale sounds.

• Proceedings of the Korean Aquaculture Society Conference
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• 2004.05a
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• pp.49-50
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• 2004

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.43-48
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• 2017

The underwater transients signals contain the characteristics of complexity, time varying, nonlinear, and short duration. So it is very hard to model for these signals with reference patterns. In this paper we separate the whole length of signals into some short duration of constant length with overlapping frame by frame. The 20th LPC(Linear Predictive Coding) coefficients are extracted from the original signals using Durbin algorithm and applied to neural network. The 65% of whole signals were learned and 35% of the signals were tested in the neural network with two hidden layers. The types of the whales for sound classification are Blue whale, Dulsae whale, Gray whale, Humpback whale, Minke whale, and Northern Right whale. Finally, we could obtain more than 83% of classification rate from the test signals.