• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.470-480
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• 2010

Although e-Learning contents contain audio materials as well as visual materials, research on the design of audio materials has been focused on visual design. If it is considered that non-speech sounds which are a type of audio materials can promptly provide feedbacks of learners' responses and guide learners' learning process, the systemic design of non-speech sounds is needed. Therefore, the purpose of this study is to investigate the learners' perceptions toward non-speech sounds contained the e-Learning contents with multidimensional scaling method. For this purpose, the eleven non-speech sounds were selected among non-speech sounds designed Korea Open Courseware. The 66 juniors in A university responded the degree of similarity among 11 non-speech sounds and the learners' perceptions towards non-speech sounds were represented in the multidimensional space. The result shows that learners perceive separately non-speech sounds by the length of non-speech sounds and the atmosphere which is positive or negative.

• Journal of Biomedical Engineering Research
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• v.20 no.6
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• pp.591-599
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• 1999

연구개에 결함이 있는 사람의 발음은 부적절한 비음이 섞이게 되어 과비음성 비음이 되어 연구개를 복원해주는 시술을 하게 되는데, 과비음성 비음을 정량적으로 측정할 수있다면 시술 결과를 객관화 할 수 있게 된다. 현재 임상적으로 사용되고 있는 방법들은 관혈적이거나 고가의 장비를 필요로 한다. 본 논문에서는 비음의 특징인 스펙트럼에서 zero 의 존재와 비강에 의한 포만트의 존재 사실, 그리고 선형 예측 모델을 이용하여 마이크로폰과 사운드 카드가 장착된 PC로 구현할 수 있는 새로운 과비음성 비음 추정 알고리즘을 제안하였다. 음성 신호의 스펙트럼에 zero가 존재하는 경우, 낮은 차수(order)의 선형 예측 모델이 그 음성을 발음한 성도 시스템에 정확히 적용되지 않는다는 점을 이용하여, 같은 음성에 대한 높은 차수의 선형 예측 모델과의 차이를 이용해서 과비음성의 정량화를 시도했다. 본 논문에서는 제안된 알고리즘은 기존의 Teager Operator를 이용한 알고리즘에 비해서 Nasonmeter 의 측정결과와 더 높은 통계적 상관관계를 보여주었다.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.143-148
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• 2016

Deep neural network that effectively capture the characteristics of data has been widely used in various applications. However, the amount of sound database is often insufficient for learning the deep neural network properly, so resulting in overfitting problems. In this paper, we propose a transfer learning framework that can effectively train the deep neural network even with insufficient sound event data by employing rich speech or music data. A series of experimental results verify that proposed method performs significantly better than the baseline deep neural network that was trained only with small sound event data.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.393-407
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• 2020

In order to reduce the threat by illegal unmanned aerial vehicles, a tracking system based on sound was implemented. There are three main points to the drone acoustic tracking method. First, it scans the space through variable beam formation to find a sound source and records the sound using a microphone array. Second, it classifies it into a hidden Markov model (HMM) to find out whether the sound source exists or not, and finally, the sound source is In the case of a drone, a sound source recorded and stored as a tracking reference signal based on an adaptive beam pattern is used. The simulation was performed in both the ideal condition without background noise and interference sound and the non-ideal condition with background noise and interference sound, and evaluated the tracking performance of illegal drones. The drone tracking system designed the criteria for determining the presence or absence of a drone according to the improvement of the search distance performance according to the microphone array performance and the degree of sound pattern matching, and reflected in the design of the speech reading circuit.