• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.543-543
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• 2010

The objective of this research is estimating the location of interested sound source by using the similarity between a beamformer output in time domain and the candidate signal. The waveform of beamformer output at the location of sound source is similar with the waveform emitted by that source. To estimate the location of sound source by using this feature, we define quantified similarity between candidate signal and beamformer output. The candidate signal describes the signal which is generated by interested source. In this paper, similarity is defined by four methods. The two methods use time vector comparison, and the other two methods use time-frequency map or linear prediction coefficients. To figure out the results and performance of localization by using similarities, we demonstrate two conditions. The one is when two pure tone sources exist and the other condition is when several bird sounds exist. As a consequence, inner product with two time-vectors and structural similarity with spectrograms can estimate the locations of interest sound source.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.193-204
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• 2019

The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2E
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• pp.72-78
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• 2009

Heart sound segmentation into its components, S1, systole, S2 and diastole is the first step of analysis and the most important part in the automatic diagnosis of heart sounds. Conventionally, the Shannon energy envelope peak detection method has been popularly used due to its superior performance in locating S1 and S2. Recently, the HMM has been shown to be quite suitable in modeling the heart sound signal and its use in segmenting the heart sound signal has been suggested with some success. In this paper, we compared the two methods for heart sound segmentation using a common database. Experimental tests carried out on the 4 different types of heart sound signals showed that the segmentation accuracy relative to the manual segmentation was 97.4% in the HMM based method which was larger than 91.5% in the peak detection method.

• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.218-226
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• 2019

In modern society, people live in an environment with artificial or natural noise. Especially, the sound that corresponds to the artificial noise makes the noise itself and affects each other because many people live and work in the city. Sounds are generated by the activities and causes of various people, such as construction sites, aircraft, production machinery, or road traffic. These sounds are essential elements in human life and are recognized and judged by human auditory organs. Noise is a sound that you do not want to hear by subjective evaluation, and it is a loud sound that gives hearing damage or a sound that causes physical and mental harm. In this study, we introduce the method of stimulating the human hearing by finger vibration and explain the advantages of the proposed method in various kinds of a noise environment. And how to improve the sound quality to improve efficiency. In this paper, we propose a method to prevent the loss of hearing loss and the transmission of sound information based on proper signal to noise ratio when using portable IT equipment in various noise environments.

• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.393-396
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• 2022

Recently, rapid climate change has had a significant impact on the ecosystem of honeybees. In addition, the problem of Vespa Hornets invasion of colonies has a fatal impact on the bee ecosystem, independent of climate change. Especially in late summer. This study relates to a method for preventing Vespa Hornets attack. In this study, we developed a Vespa Hornets sound detection device was developed by collecting and analyzing the sound of a Vespa Hornets and applying IoT technology. The developed device detects the sound of a Vespa Hornets when Vespa Hornets appears around the hive of the bees and sends a signal to automatically close the door of the beehive. The device that receives the signal drives the motor that controls the honeycomb door to close the beehive door. The Vespa Hornets sound detection device operates until no Vespa Hornets sound is detected. The system developed by us is expected to be installed in the beehives of actual beekeeping farms to dramatically reduce the damage caused by by Vespa Hornets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.962-977
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• 2019

The human voice is a convenient method of information transfer between different objects such as between men, men and machine, between machines. The development of information and communication technology, the voice has been able to transfer farther than before. The way to communicate, it is to convert the voice to another form, transmit it, and then reconvert it back to sound. In such a communication process, a vocoder is a method of converting and re-converting a voice and sound. The CELP (Code-Excited Linear Prediction) type vocoder, one of the voice codecs, is adapted as a standard codec since it provides high quality sound even though its transmission speed is relatively low. The EVRC (Enhanced Variable Rate CODEC) and QCELP (Qualcomm Code-Excited Linear Prediction), variable bit rate vocoders, are used for mobile phones in 3G environment. For the real-time implementation of a vocoder, the reduction of sound quality is a typical problem. To improve the sound quality, that is important to know the size and shape of noise. In the existing sound quality improvement method, the voice activated is detected or used, or statistical methods are used by the large mount of data. However, there is a disadvantage in that no noise can be detected, when there is a continuous signal or when a change in noise is large.This paper focused on finding a better way to decrease the reduction of sound quality in lower bit transmission environments. Based on simulation results, this study proposed a preprocessor application that estimates the SNR (Signal to Noise Ratio) using the spectral SNR estimation method. The SNR estimation method adopted the IMBE (Improved Multi-Band Excitation) instead of using the SNR, which is a continuous speech signal. Finally, this application improves the quality of the vocoder by enhancing sound quality adaptively.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.903-912
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• 2007

Faults of rotating parts of a train normally generate unexpected frequency band or impulsive sound[1] which has a period when it moves with a constant speed. The former can be detected by the moving frame acoustic holography method, which visualizes sound field that is generated by a moving and emitting pure tone or band limited noise source. We have attempted to apply the method to the latter case: the periodic impulsive sound which generate different signal compared with what can be measured by the band limited noise. The signal to noise ratio which determines the success of early fault detection must also be studied with the impulsive and moving signal. This research shows how the problems related with these issues can be resolved. The main idea is that periodic impulsive signal can be expressed by infinite set of discrete pure tones. This enables us to obtain lots of holograms that visualize periodic impulsive sound field including noise by using the moving frame acoustic holography method. Therefore holograms can be averaged to improve the signal to noise ratio until having reliable information that exhibits where the impulsive sources are. Theory and experiment by using the miniature vehicle are described [Work supported by BK21 & KRRI].

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.7-13
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• 2015

Recently, there are serviced user customized advertisement of various type using smart device. Representative services are advertisement service using light of smart TV screen or audible sound of smart TV to transmit advertisement information. However, those services have to do a specific action of smart device user for advertisement information or need audible audio information of TV contents. To overcome those weakness, therefore, we propose an advertisement method using inaudible sound of speaker based on smart device. This method supports the transfer of advertising content to the smart device user with no additional action or TV audio signal required to access that content. The proposed method used two high frequencies among 18kHz ~ 22kHz of audible frequency range which smart TV can send out. And it generates those frequencies synthesized with audio of TV contents as trigger signal which can send advertisements to smart device. Next, smart device analysis the trigger signal and request advertisement contents related to the signal to server. After then, smart device can show the downloaded contents to user. Because the proposed method uses the high frequencies of sound signals via the inner speaker of the smart device, its main advantage is that it does not affect the audio signal of TV content. To evaluate the efficacy of the proposed method, we developed an application to implement it and subsequently carried out an advertisement transmission experiment. The success rate of the transmission experiment was approximately 97%. Based on this result, we believe the proposed method will be a useful technique in introducing a customized user advertising service.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.23-29
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• 2003

In this paper, we propose an idea for the improved 3-D sound system using conventional stereo headphones to obtain a better sound diffusion from the mono-sound recorded at an anechoic chamber. We use the HRTF(Head Related Transfer Function) for the sound localization and the wavelet filter bank with time delay for the sound diffusion. And we test the modified HRTF with the various sampling rate. We investigate the effects of the 3-D sound depending on the length of time delay at lowest frequency band. Also the correlation coefficient of the signals between the left channel and the right channel is measured to identify the sound diffusion. At last we obtain the diffusion sound using Cool Edit for reverberation.