• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.138-146
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• 2013

Passive sonars employ DEMON (Detection of Envelope Modulation on Noise) processing to extract propeller information from the radiated noise of underwater targets. Conventional DEMON processing improves SNR(Signal to Noise Ratio) characteristic by Welch method. The conventional Welch method overlaps several different time domain DEMON outputs to reduce the variance. However, the conventional methods have high computational complexity to get high SNR with correlated acoustic signals. In this paper, we propose new DEMON processing method that divides acoustic signal into several frequency bands before DEMON processing and averages each DEMON outputs. Therefore, the proposed method gathers independent acoustic signal faster than conventional method with low computational complexity. We prove the performance of the proposed method with mathematical analysis and computer simulations.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.78-88
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• 2024

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.214-221
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• 2019

In this paper, a temperature dependent acoustic receiving characteristics of underwater acoustic sensor is studied by theoretical and experimental investigations. Two different types (low mid frequency sensor and high frequency sensor) of underwater acoustic sensors are designed with different configuration of baffle and conditioning plate. The temperature dependent characteristics of the acoustic sensors are investigated within the temperature range from $-2^{\circ}C$ to $35^{\circ}C$. The material properties of the piezoelectric ceramics, molding and baffle, which are the primary materials of the acoustic sensors, are measured with temperature change. The temperature dependent RVS (Receiving Voltage Sensitivity) characteristics of the acoustic sensors are simulated by using the measured material properties. The RVS changes of the acoustic sensors are measured by changing temperature in the watertank where the acoustic sensors are installed. The measured and the simulated data show that the temperature dependent characteristics of the acoustic sensors are mainly dependent for the sound speed changes of the molding material.