• The Journal of Korea Robotics Society
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• v.5 no.1
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• pp.7-13
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• 2010

This paper presents the sound-based emotion estimation method and the growing HRI (human-robot interaction) system for a Mon-E robot. The method of emotion estimation uses the musical element based on the law of harmony and counterpoint. The emotion is estimated from sound using the information of musical elements which include chord, tempo, volume, harmonic and compass. In this paper, the estimated emotions display the standard 12 emotions including Eckman's 6 emotions (anger, disgust, fear, happiness, sadness, surprise) and the opposite 6 emotions (calmness, love, confidence, unhappiness, gladness, comfortableness) of those. The growing HRI system analyzes sensing information, estimated emotion and service log in an edutainment robot. So, it commands the behavior of the robot. The growing HRI system consists of the emotion client and the emotion server. The emotion client estimates the emotion from sound. This client not only transmits the estimated emotion and sensing information to the emotion server but also delivers response coming from the emotion server to the main program of the robot. The emotion server not only updates the rule table of HRI using information transmitted from the emotion client and but also transmits the response of the HRI to the emotion client. The proposed system was applied to a Mon-E robot and can supply friendly HRI service to users.

• Journal of Sensor Science and Technology
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• v.8 no.2
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• pp.102-107
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• 1999

In this paper, to develop the fiber optic hydrophone for the use of low frequency applications, two channels TDM(Time Division Multiplexing) fiber-optic hydrophone array was fabricated and their acoustic charateristics were investigated in the acoustic water tank. A fiber length of the order of 150m is wounded at the hollow cylinder type aluminum mandrel and the fundamental natural frequency of the mandrel maintained above 10kHz. An unbalanced interferometer (discrete Mach-Zehnder type) was used. Sound detection performance is tested in the underwater tank with 3kHz continuous sound source. Finally, it is shown that two channels TDM fiber-optic hydrophone array can detect 3kHz sound stably. This results can also applicable for the development of multi-channel fiber optic hydrophone array.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1075-1080
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• 2009

이 글은 Music Information Retrieval(MIR) 기법을 사용하여 오디오 콘텐츠의 리듬 정보를 자동으로 분석하고 이를 시각화하는 방법에 대해 다룬다. 특히 MIR을 활용한 간단한 시각화(sound visualization) 어플리케이션을 소개함으로써 음악 정보 분석이 디자인, 시각 예술에서 다양하게 활용될 수 있음을 보이고자 한다. 음악적 정보를 시각 예술로 담아내려는 시도는 20세기 초 아방가르드 화가들에 의해 본격적으로 시작되었다. 80년대 이후에는 컴퓨터 기술의 급속한 발전으로 사운드와 이미지를 디지털 영역에서 쉽게 하나로 다룰 수 있게 되었고, 이에 따라 다양한 오디오 비주얼 예술작품들이 등장하였다. MIR은 오디오 콘텐츠로부터 음악적 정보를 분석하는 DSP(Digital Signal Processing) 기술로 최근 디지털 콘텐츠 시장의 확장과 더불어 연구가 활발히 진행되고 있다. 특히 웹이나 모바일에서는 이미 다양한 상용 어플리케이션이 적용되고 있는데 query-by-humming과 같은 음악 인식 어플리케이션이 대표적인 경우이다. 이 글에서는 onset 검출(onset detection)을 중심으로 음악적 리듬을 분석하는 알고리즘을 살펴보고 기본적인 조형원리에 따라 이를 시각화하는 어플리케이션의 예를 소개한다.

• The Journal of the Acoustical Society of Korea
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• v.23 no.2E
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• pp.68-73
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• 2004

In the East Sea of Korea, the ocean environments are known to have strong variations in space and time. Their effects are very important factors in sound propagation and sonar performance. We consider the environmental factors such as eddies and thermal fronts affecting underwater sound propagation and target detection performance by sonars. Unfortunately, however, the detailed structure of eddies is usually difficult to understand by using the sea surface temperatures from infrared images alone or a few profiles from the CTD (conductivity, temperature and depth) castings. The temperature fields of eddy and thermal front are simulated with typical patterns of those obtained from several observations. This paper delivers the overviews of environments and acoustic models with their simulation results on sonar performance.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.7-10
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• 2003

In this paper, we present a new echo kernel, which is a modification of polar echo kernel. to improve the detection performance and robustness against attacks. Polar echo kernel may take advantage of large detection margin from the polarity of inserted echo signal, but its poor frequency response in low frequency band degrades sound quality. To solve this problem, we applied bipolar echo pulses to the polar echo kernel. Using the proposed echo kernel the distributions of autocepstrum peaks fur data ‘0’ and ‘1’ are located more distant and improvement of detection performance is achieved. It also makes the low frequency band flat so that the timbre difference in the polar echo kernel can be removed to reproduce the imperceptible sound qualify. Informal listening tests as well as robustness test against attacks were performed to evaluate the proposed echo kernel. Experimental results demonstrated the superiority of the proposed echo kernel to both conventional unipolar and polar echo kernels

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.621-629
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• 2016

This paper suggests a new structure of hybrid DRC to enhance the psychoacoustic sound quality of a conventional multiband DRC. The proposed hybrid DRC consists of two serially cascaded stages. The front stage DRC is multiband, and it compresses input based on RMS level detection, whereas, the back stage DRC is single band, and it regulates input according to peak level detection. The proposed hybrid DRC shows better loudness while suppressing distortion by clipping. The proposed algorithm was verified through MATLAB simulation, and it was implemented using an FPGA board for listening test. The test result showed that the proposed hybrid structure enhances overall psychoacoustic sound quality compared to conventional structures, which is based on only RMS or peak level detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.141-146
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• 2015

In order to classify and analyze variously compounded sound and voice signal from FPGA microphone, there are numerous systems to detect abnormality signal, however, they have a lot of problems to implement the abnormality signal detection efficiently and effectively. Therefore, we proposed a method that implements classifying the signal effectively and outputting the detection efficiently based on the algorithm applied FIFO structure (First-in First-out) by using microphone sensor which able to input the sound signal, and statistical variance and coefficient of variation (CV). The result showed 96.3% detection when the experiment was performed more than 100 times with the proposed algorithm applied system.

• The Journal of the Acoustical Society of Korea
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• v.20 no.1
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• pp.36-42
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• 2001

This paper propose new sound localization algorithm that detects the sound source bearing in a closed office environment using two microphone array. The proposed Subband CPSP (Cross Power Spectrum Phase) algorithm is a development of previously Down CPSP method using subband approach. It first split the received microphone signals into subbands and then calculates subband CPSP which result in possible source bearings. This type of algorithm, Subband CPSP, can provide more robust and reliable sound localization system because it limits the effects of environmental noise within each subband. To verify the performance of the proposed Subband CPSP algorithm, a real time simulation was conducted and it was compared with previous CPSP method. From the simulation results, the proposed Subband CPSP is superior to previous CPSP algorithm more than 5％ average accuracy for sound source detection.