• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.3
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• pp.142-146
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• 2007

Ambient noise was measured for 3 hours on May, 2001 at a site of 20 m water depth in the Korean coast of the Yellow Sea. During the measurement, the strong underwater sound assuming by marine life was continually observed. The spectrum level of this sound was very high compared to that of underwater ambient noise over the frequency range from 1 to 20 kHz. Therefore, this underwater sound can continually affect the ambient noise level. In this study, the source of the underwater sound was investigated. The snapping shrimp was estimated as reliable source. It was confirmed through comparison with experimental results described in previously literatures. It was also confirmed through analysis of snapping shrimp sound measured under laboratory conditions.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.262-269
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• 2023

In this paper, we propose a Linear Prediction (LP) analysis-based feature for detecting Snapping Shrimp (SS) Noise (SSN) in underwater acoustic data. SS is a species that creates high amplitude signals in shallow, warm waters, and its frequent and loud sound is a major source of noise. The proposed feature takes advantage of the characteristic of SSN, which is sudden and rapidly disappearing, by using LP analysis to detect the exact noise interval and reduce the effects of SSN. The error between the predicted and measured value is large and results in effective SSN detection. To further improve performance, a constant false alarm rate detector is incorporated into the proposed feature. Our evaluation shows that the proposed methods outperform the state-of-the-art MultiLayer-Wavelet Packet Decomposition (ML-WPD) in terms of receiver operating characteristic curve and Area Under the Curve (AUC), with the LP analysis-based feature achieving a higher AUC by 0.12 on average and lower computational complexity.