• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.43-49
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• 1997

Generally, echo cancelers of a TV conference system or a audio conference system are to carry out a real time processing in the case of the closed room having long reverberation time because the system requires much time to modify filter coefficients to environmental changes. Therefore this paper proposes a new subband adaptive filtering method using polyphase filter banks of MPEG(Moving Picture Experts Group) audio system to solve the problems. This method divides signal spectra of input and output into several frequency bands, and each band is adaptively filtered by using ES-NLMS (Exponential Step-Normalized Least Mean Square) algorithm. The optimal number of subband is determined by computational simulations. According to the results of simulation, ERLE of the subband model is 2dB smaller than general full band, calculation rate's of the subband model is decreased about 88%.

• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.411-421
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• 2010

A time-reversal mirror (TRM) is useful in diverse areas, such as reverberation ing, target echo enhancement and underwater communication. In underwater communication, the bit error rate has been improved significantly due to the increased signal-to-noise ratio by spatio-temporal focusing. This paper deals with two issues. First, the optimal number of array elements for a given environment was investigated based on the exploitation of spatial diversity. Second, an algorithm was developed to determine the optimal location of the given number of array elements. The formulation is based on a genetic algorithm maximizing the contrast between the foci and area of interest as an objective function. In addition, the developed algorithm was applied to the matched field processing with ocean experimental data for verification. The sea-going data and simulation showed almost 3 dB improvement in the output power at the foci when the array elements were optimally distributed.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.