• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.10
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• pp.985-993
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• 2012

In this paper, a new method is proposed to estimate the sound pressure generated from gasoline direct injection (GDI) engine. There are many noise sources as much as components in GDI engine. Among these components, fuel pump, fuel injector, fuel rail, pressure pump and intake/exhaust manifolds are major components generated from top of the engine. In order to estimate the contribution of these components to engine noise, the total sound pressure at the front of the engine is estimated by using airborne source quantification (ASQ) method. Airborne source quantification method requires the acoustic source volume velocity of each component. The volume velocity has been calculated by using the inverse method. The inverse method requires many tests and has ill-condition problem. This paper suggested a method to obtain volume velocity directly based on the direct measurement of sound intensity and particle velocity. The method is validated by using two known monopole sources installed at the anechoic chamber. Finally the proposed method is applied to the identification and contribution of noise sources caused by the GDI components of the test engine.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.345-352
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• 2004

This paper deals a method to deliver more realistic sound by cancelling the cross-talk which is inherent to the 5.1 channel speaker system. The acoustical model for cross-talk cancellation is the free field model. This model minimizes distortion of sound. 1 used the bark scale sound quality compensation which based on psycho-acoustic. For the surround channels, band-limited sound quality compensation is performed in the frequency domain. I also performed the sound qualify assessment test on the traditional 2 channel stereo and 5.1 channel system. This test is performed in the tort chamber which satisfies the ITU-R specifications. 1 uses the IACC(Inter-Aural Cross-Correlation) to determine the preferences of the amateur and the golden ear experts to asses the trans-aural filter. According to the result from the proposed method, I got more the 38dB separation rates with the Dolby standard speaker array. The results on the diffusion by the subjective test with the experts shows 0.4∼0.5 point Increased then before.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.178-183
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• 2024

This study introduces an enhanced sound source localization technique, bolstered by a data-driven deep learning approach, to improve the precision and accuracy of direction of arrival estimation. Focused on refining Time Difference Of Arrival (TDOA) based sound source localization, the research hinges on accurately estimating TDOA from cross-correlation functions. Accurately estimating the TDOA still remains a limitation in this research field because the measured value from actual microphones are mixed with a lot of noise. Additionally, the digitization process of acoustic signals introduces quantization errors, associated with the sampling frequency of the measurement system, that limit the precision of TDOA estimation. A deep learning-based approach is designed to overcome these limitations in TDOA accuracy and precision. To validate the method, we conduct comprehensive evaluations using both two and three-microphone array configurations. Moreover, the feasibility and real-world applicability of the suggested method are further substantiated through experiments conducted in an anechoic chamber.