• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.617-621
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• 2003

Existing loudness models are specified only to diotic sounds in spite of the fact that normal human beings hear dichotic sounds. Approximately, the arithmetic mean of loudness values of both ear signals has been suggested for the resultant perceived loudness. In this study, the dependence of overall loudness perception on the interaural level differences was investigated by the subjective tests. It was found that the larger the interaural level difference, the louder the perception than the mean of calculated loudness values at both ears and the lower the critical band rate or the reference level, the louder the perception than the mean value. A modified loudness model was proposed to he applicable to dichotic sounds by using the equivalent diotic levels.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.210-216
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• 2013

It is hard to listen to the music clearly in the presence of background noise. In this paper, a method that modifies the audio signal automatically to enhance the audio listening experience in adverse environment is proposed. Specifically, the method that amplifies the audio signal so that the perceived loudness of audio signal in each band becomes similar to that of the noiseless signal. The loudness perception model proposed by Moore et. al is utilized. Extending the previous work that is applied to speech reinforcement, the full band signal sampled at 48kHz is manipulated based on the loudness restoration principle. Moreover, based on the observation that the audio clarity is compromised even with loudness restored signal, a modification that intentionally boosts high frequency loudness more than lower band is also proposed. Experimental results showed that the proposed algorithm can enhance the audio listening experience in adverse environment.

• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.187-194
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• 2013

The final aim of the present study is to build a system of converting a color image into musical elements based on a synesthetic perception, emulating human synesthetic skills, which make it possible to associate a color image with a specific sound. This can be done on the basis of the similarities between physical frequency information of both light and sound. As a first step, an input true color image is converted into hue, saturation, and intensity domains based on a color model conversion theory. In the next step, musical elements including note, octave, loudness, and duration are extracted from each domain of the HSI color model. A fundamental frequency (F0) is then extracted from both hue and intensity histograms. The loudness and duration are extracted from both intensity and saturation histograms, respectively. In experiments, the proposed system on the conversion of a color image into musical elements was implemented using standard C and Microsoft Visual C++(ver. 6.0). Through the proposed system, the extracted musical elements were synthesized to finally generate a sound source in a WAV file format. The simulation results revealed that the musical elements, which were extracted from an input RGB color image, reflected in its output sound signals.

• ETRI Journal
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• v.35 no.5
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• pp.859-868
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• 2013

The active noise control (ANC) technique attenuates acoustic noise in a flexible and effective way. Traditional ANC design aims to minimize the residual noise energy, which is indiscriminative in the frequency domain. However, human hearing perception exhibits selective sensitivity for different frequency ranges. In this paper, we aim to improve the noise attenuation performance in perceptual perspective by incorporating noise weighting into ANC design. We also introduce psychoacoustic analysis to evaluate the sound quality of the residual noise by using a predictive pleasantness model, which combines four psychoacoustic parameters: loudness, sharpness, roughness, and tonality. Simulations on synthetic random noise and realistic noise show that our method improves the sound quality and that ITU-R 468 noise weighting even performs better than A-weighting.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.511-516
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• 2002

Pitch is a perception related to the subjective frequency that is one of the psychological aspects or attributes of tones. It is also an important factor to determine the sound quality together with loudness and timber. Although the study on pitch has been active in the field of speech communication, but its application to the product sound quality is not yet enough. In this study, the empirical data by Zwicker is made use in the modification of the currently available pitch extraction model based on the place theory. By applying this modified model to various sound samples composed of tonal or banded components, the applicability of the model is suggested. As a demonstration example, the algorithm is used for the sound quality analysis of a product noise having fundamental frequency and harmonics. The result shows that the pitch should be regarded as an important subjective cue in the sound quality analysis.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.9 s.114
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• pp.897-903
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• 2006

The reduction of the vehicle interior noise has been the main interest of noise and vibration harshness (NVH) engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the heating, ventilation and air conditioning (HVAC) system sound among the vehicle interior noise has been reflected sensitively in psychoacoustics view point. Even though the HVAC noise is not louder than overall noise level, it clearly affects subjective perception to drivers in the way of making to be nervous or annoyed. Therefore, these days a vehicle engineer takes aim at developing sound quality as well as reduction of noise. In this paper, we acquired noises in the HVAC from many vehicles. Through the objective and subjective sound quality (SQ) evaluation with acquiring noises recorded by the vehicle HVAC system, the simple and multiple regression models were obtained for the subjective evaluation 'Pleasant' using the semantic differential method (SDM). The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Furthermore, the neural network (NN) model were obtained using three inputs(loudness, sharpness and roughness) of the SQ metrics and one output(subjective 'Pleasant'). Because human's perception is very complex and hard to estimate their pattern, we used NN model. The estimated models were compared with correlations between output indexes of SQ and hearing test results for verification data 'Pleasant'. As a result of application of the SQ indexes, the NN model was shown with the largest correlation of SQ indexes and we found possibilities to predict the SQ metrics.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.457-466
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• 1997

We developed a new hearing aid telephone which helps the hearing impaired person to improve the listening ability of telephone speech. Recently, the hearing impaired person and the elderly who has hearing loss have been continuously increased and their desire for participating society as a producer has been increased also. So they strong1y want the hearing aid devices which make compensation fortheir handicap. The hearing aid telephone is one of the basic aid devices that helps the hearing impaired to communicate well with other poeple and to acquire easily useful information through the phone. We analyze the hearing ability of the hearing impaired, design the new model of the hearing aid telephone and test the telephone in three fields-electrical, word perception, user test. Our new tolephone has lour band pass filter channels and the center frequencies of these filters are 500, 1000, 2000, 3000Hz which are considered psychoacoustic factors and telephone line characteristics. The hearing impaired can adjust the total gain characteristics of receiving sound to his hearing ability by setting four volumes in the telelphone. This procedure is called fitting which is a very important factor for the hearing impaired to take meaning of speech. The total gain of this telephone is over 20dB from 250Hz to 3200Hz range. From the results of the tests we certify that our new model is better for the hearing impaired to understand the meaning or telephone speech than the old general models. The next step of developing the hearing aid telephone is to study about compressing sidetone and noise, dividing frequency bands, selecting hearing aid pattern and compensating psychoacoustic loudness. we expect that the advanced hearing aid telephone can be developed by the research about speech perception characteristics of the hearing impaired in engineering and clinical side.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1443-1448
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• 2006

The reduction of the vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the HVAC sound among the vehicle interior noise has been reflected sensitively in the side of psychology. Even though the HVAC noise is not louder than overall noise level, it clearly affects subjective perception in the way of making a diver become nervous or annoyed. Therefore, these days a vehicle engineer takes aim at developing sound quality as well as reduction of noise. In this paper, we acquired noises in the HVAC from many vehicles. Through the objective and subjective sound quality evaluation with acquiring noises caused by the vehicle HVAC system, the simple and multiple regression models were obtained for the subjective evaluation 'Pleasant' using the sound quality metrics. The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Furthermore, the neural network model were obtained using three inputs(loudness, sharpness and roughness) of the sound quality metrics and one output(subjective 'Pleasant'). And then the models were compared with correlations between sound quality index outputs and hearing test results for 'Pleasant'. As a result of application of the sound quality index, the neural network was verified with the largest correlation of the sound quality index.