• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.1009-1009
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• 2003

VIPER a new tool for the VIsual PERception of sound quality and for sound design will be presented. Requirement for the visualization of sound quality is a signal analysis modeling the information processing of the ear. The first step of the signal processing implemented in VIPER, calculates an auditory spectrogram by a filter bank adapted to the time- and frequency resolution of the human ear. The second step removes redundant information by extracting time- and frequency contours from the auditory spectrogram in analogy to contours of the visual system. In a third step contours and/or auditory spectrogram can be resynthesised confirming that only aurally relevant information were extracted. The visualization of the contours in VIPER allows intuitively to grasp the important components of a signal. Contributions of parts of a signal to the overall quality can be easily auralized by editing and resynthesising the contours or the underlying auditory spectrogram. Resynthesis of time contours alone allows e.g. to auralize impulsive components separately from the tonal components. Further processing of the contours determines tonal parts in form of tracks. Audible differences between two versions of a sound can be visually inspected in VIPER through the help of auditory distance spectrograms. Applications are shown for the sound design of several interior noises of cars.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.274-277
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• 2005

Panning is the way in which to realize a spatial sound in MIDI by moving sound images by the loudness of each channel. However, there is a limitation for the natural spatial sound. The HRTF (Head Related Transfer Function) has been widely known as one of the ways to realize spatial sound using the two channels, but it needs much processing power. It is very hard to implement a real time processing structure. In this paper, we propose an improved 3D sound model for the spatial sound location by changing the acoustic parameters. We could get a good result from the experiment with MIDI Pan and our Model.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.961-963
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• 2012

The sound source localization system is used to some area such as robotic system, object localization system, guarding system and medicine. So time delay estimation and angle estimation of sound direction are studied until now. These days time delay estimation is described in LabVIEW which is used to create innovative computer-based product and deploy measurement and control systems. In this paper, the development of signal monitoring platform is presented for sound source localization. This platform is designed in virtual instrument program and implemented in two stages. In first stage, data acquisition system is proposed and designed to analyze time delay estimation using cross correlation. In second stage, data obtaining system which is applied and designed to monitor analog signal processing is proposed.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1068-1081
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• 2019

Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.495-498
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• 2004

In the sound insulation measurements, the influence of background (extraneous) noise is often serious problem and how to reduce its effect and to improve the signal-to-noise(S/N) ratio is an important theme. As the background noise, such extraneous noises as road traffic noise and machine noise often disturb the measurement. In laboratory measurements on specimens with high sound insulation performances, even the internal noise of the measurement system can become a problem. To improve the signal-to-noise ratio and to improve the measurement accuracy, various kinds of digital signal processing techniques can be applied. In this paper, four kinds of digital signal processing techniques are applied and their effectiveness is examined by a simple sound insulation measurement.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.144-152
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• 2007

Conventional research works on the classification of the heart sound signal have been done mainly with the artificial neural networks. But the analysis results on the statistical characteristic of the heart sound signal have shown that the HMM is suitable for modeling the heart sound signal. In this paper, we model the various heart sound signals representing different heart diseases with the HMM and find that the classification rate is much affected by the clustering of the heart sound signal. Also, the heart sound signal acquired in real environments is a continuous signal without any specified starting and ending points of time. Hence, for the classification based on the HMM, the continuous cyclic heart sound signal needs to be manually segmented to obtain isolated cycles of the signal. As the manual segmentation will incur the errors in the segmentation and will not be adequate for real time processing, we propose a variant of the ergodic HMM which does not need segmentation procedures. Simulation results show that the proposed method successfully classifies continuous heart sounds with high accuracy.

• Proceedings of the IEEK Conference
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• summer
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• pp.237-243
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• 2004

A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is presented in this paper. Sound information extraction is a complex task and the main difficulty consists is the extraction of high­level information from an one-dimensional signal. The input of smart sound sensor is composed of data collected by 5 microphones and its output data is sent through a network. For a real time working purpose, the sound analysis is divided in three steps: sound event detection for each sound channel, fusion between simultaneously events and sound identification. The event detection module find impulsive signals in the noise and extracts them from the signal flow. Our smart sensor must be capable to identify impulsive signals but also speech presence too, in a noisy environment. The classification module is launched in a parallel task on the channel chosen by data fusion process. It looks to identify the event sound between seven predefined sound classes and uses a Gaussian Mixture Model (GMM) method. Mel Frequency Cepstral Coefficients are used in combination with new ones like zero crossing rate, centroid and roll-off point. This smart sound sensor is a part of a medical telemonitoring project with the aim of detecting serious accidents.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.441-444
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• 2004

In the sound insulation measurements, the influence of background (extraneous) noise is often serious problem and how to reduce its effect and to improve the signal-to-noise(S/N) ratio is an important theme. As the background noise, such extraneous noises as road traffic noise and machine noise often disturb the measurement. In laboratory measurements on specimens with high sound insulation performances, even the internal noise of the measurement system can become a problem. To improve the signal-to-noise ratio and to improve the measurement accuracy, various kinds of digital signal processing techniques can be applied. In this paper, four kinds of digital signal processing techniques are applied and their effectiveness is examined through field measurements.

• Proceedings of the Acoustical Society of Korea Conference
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• 1984.12a
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• pp.91-95
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• 1984

Because of layer and scattering in the ocean, there are some problem in algorithm currently used for the recognition of targets. Those are time delay of processing and circuit design. The simple method of detecting direct sound wave in noise caused by time delay is proposed-recognized, estimated, and then direcxt sound wave is reconstructed by the AMDF and $\mu$-processor. 2KHz, 4KHz, 8KHz, 12KHz, 16KHz sound waves are used in experiment. To obtain a reference signal, anechoic water tank is used is processing and aluminium water tank used instead of real ocean. As a result, there are a few errors which caused by anechoic water tank error, decreasing of frequency make errors. Possibility of application to Sonar Signal Processing is proved.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1114-1118
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• 2000

Impulsive sound and vibration signals in gear are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in the diagnosis of gear fault. The early detection of impulsive signal due to gear fault prevents from complete failure in gear. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the detection of impulsive signals embedded in background noise, we enhance the impulsive signals using adaptive signal processing.