• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.201-204
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• 1998

강당, 회의실, 강의실 등과 같은 닫혀진 공간에서 포착된 소리의 질은 반향음과 간섭 잡음 등에 의해 영향을 받는다. 마이크로폰에 수신되는 신호는 직접전달파와 벽면에 의한 반사파들이 더해지므로 해서 실제 발생음을 명확히 얻기가 어렵다. 수신측 마이크로폰에서 반사음의 영향을 제거하면 실제 발생음에 가까운 양질의 소리를 얻을 수 있을 것이다. 잡음과 반향음의 영향이 큰 음향 공간에서 고음질의 소리를 얻기 위한 방법으로 마이크로폰 배열의 병렬 신호 처리 기법이 있다. 본 연구에서 제시된 마이크로폰 배열의 병렬 신호처리 기법은 공간적 음량 선택성을 제공하기 위해 각 마이크로폰 센서들의 matched filter 처리와 병렬 처리 기법을 결합한다. 이 기법은 다중경로 왜곡(반향)과 간섭 잡음을 제거하는 수단을 제공한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1845-1851
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• 2007

Background noise and room reverberation are two causes of degradation in speech in listening situations. Many algorithms developed to enhance reverberant speech. In this paper we propose a dereverberation method for enhancement of speech using modified the linear prediction(LP) residual in reverberant room condition. The proposed dereberberation method based on the fact that the signification excitation of the vocal tract system takes place at the instant of glottal closure in voiced speech. Our method used delay information form each sensor, and we need reverberant signals from 3 sensors. We obtain a new LP residual signal using modified IP residual combination which derived form weighting of the LP residual and the Hilbert transform of LP residual. The nature of the coherently added Hilbert envelop has several large amplitude spikes because of the effects of noise and reverberation. This residual of the clean speech is used to excite the time-varying all-pole filter to obtain the enhanced speech. We achieved simulation of proposed algorithm for performance analysis in reverberation environment. The proposed algorithm improves substantially the quality of reverberant speech.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.468-479
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• 2022

To detect targets with active sonar system in the underwater environments, the targets are localized by receiving the echoes of the transmitted sounds reflected from the targets. In this case, reverberation from the scatterers is also generated, which prevents detection of the target echo. To detect the target effectively, reverberation suppression techniques such as pre-whitening based on autoregressive model and principal component inversion have been studied, and recently a Non-negative Matrix Factorization (NMF)-based technique has been also devised. The NMF-based reverberation suppression technique shows improved performance compared to the conventional methods, but the geometry of the transducer and receiver and attenuation by distance have not been considered. In this paper, the performance is improved through preprocessing such as the directionality of the receiver, Doppler related thereto, and attenuation for distance, in the case of using a continuous wave with a bistatic sonar. In order to evaluate the performance of the proposed system, simulation with a reverberation model was performed. The results show that the detection probability performance improved by 10 % to 40 % at a low false alarm probability of 1 % relative to the conventional non-negative matrix factorization.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.815-821
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• 2009

Information processing in variable and noisy environments is usually accomplished by means of adaptive filters. Among various adaptive algorithms, Least Mean Square (LMS) has become the most popular for its robustness, good tracking capabilities and simplicity, both in terms of computational load and easiness of implementation. In practical application of the LMS algorithm, the most important key parameter is the Step Size. As is well known, if the Step Size is large, the convergence rate of the algorithm will be rapid, but the steady state mean square error (MSE) will increase. On the other hand, if the Step Size is small, the steady state MSE will be small, but the convergence rate will be slow. Many researches have been proposed to alleviate this drawback by using a variable Step Size. In this paper, a new variable Step Size LMS(VSSLMS) called Categorized VSSLMS (CVSSLMS) is proposed. CVSSLMS updates the Step Size by categorizing the current status of the gradient, hence significantly improves the convergence rate. The performance of the proposed algorithm was verified from the view point of convergence rate, Excessive Mean Square Error(EMSE), and complexity through experiments.

• 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.