• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.91-103
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• 2022

Determination of sound source characteristics such as: sound volume, direction and distance to the source is one of the important techniques for unmanned systems like autonomous vehicles, robot systems and AI speakers. There are multiple methods of determining the direction and distance to the sound source, e.g., using a radar, a rider, an ultrasonic wave and a RF signal with a sound. These methods require the transmission of signals and cannot accurately identify sound sources generated in the obstructed region due to obstacles. In this paper, we have implemented and evaluated a method of detecting and identifying the sound in the audible frequency band by a method of recognizing the volume, direction, and distance to the sound source that is generated in the periphery including the invisible region. A cross-shaped based sound source recognition algorithm, which is mainly used for identifying a sound source, can measure the volume and locate the direction of the sound source, but the method has a problem with "blind spots". In addition, a serious limitation for this type of algorithm is lack of capability to determine the distance to the sound source. In order to overcome the limitations of this existing method, we propose a QRAS-based algorithm that uses rectangular-shaped technology. This method can determine the volume, direction, and distance to the sound source, which is an improvement over the cross-shaped based algorithm. The QRAS-based algorithm for the OSSD uses 6 AITDs derived from four microphones which are deployed in a rectangular-shaped configuration. The QRAS-based algorithm can solve existing problems of the cross-shaped based algorithms like blind spots, and it can determine the distance to the sound source. Experiments have demonstrated that the proposed QRAS-based algorithm for OSSD can reliably determine sound volume along with direction and distance to the sound source, which avoiding blind spots.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.251-256
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• 2010

This paper aims for developing the intelligent robot emulating human synesthetic skills which associate a color image with sound, so that we are able to build an application system based on the principle of mutual conversion between color image and sound. As the first step, in this study, we have tried to realize a basic system using the color image to sound conversion. This study describes a new conversion method to convert color image into sound, based on the likelihood in the physical frequency information between light and sound. In addition, we present the method of converting color image into sound using color model conversion as well as histograms in the converted color model. In the basis of the method proposed in this study, we built a basic system using Microsoft Visual C++(ver. 6.0). The simulation results revealed that the hue, saturation and intensity elements of a input color image were converted into F0, harmonic and octave elements of a sound, respectively. The converted sound elements were synthesized to generate a sound source with WAV file format using Csound toolkit.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.223-231
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• 2018

Recently, due to various environmental problems, blast tracks in tunnel are replaced with concrete tracks, but they have more adverse effects on noise than blast tracks so that additional noise measures are needed. Among these measures, sound-absorbing blocks start to be used due to its easy and quick installation. However, the performance of sound absorption blocks need to be verified under real environmental and operational conditions. In this paper, interior noise levels in KTX train cruising in Dalseong tunnel are measured before and after the installation of sound-absorbing blocks and the measured data are analyzed and compared. Additionally, noise reduction are estimated by modeling the high speed train, the tunnel and absorption blocks. Measurement devices and methods are used according to ISO 3381 and the equivalent sound pressure levels during the cruising time inside the tunnel are computed. In addition to overall SPLs(Sound Pressure Levels), 1/3-octave-band levels are also analyzed to account for the frequency characteristics of sound absorption and equipment noise in a cabin. In addition, to consider the effects of train cruising speeds and environmental conditions on the measurements, the measured data are corrected by using those measured during the train-passing through the tunnels located before and behind the Dalseong tunnel. Analysis of measured results showed that the maximum noise reduction of 6.8 dB (A) can be achieved for the local region where the sound-absorbing blocks are installed. Finally, through the comparison of predicted 1/3-octave band SPLs for the KTX interior noise with the measurements, the understanding of noise reduction mechanism due to sound-absorbing blocks is enhanced.