• Investigative Magnetic Resonance Imaging
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• v.9 no.2
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• pp.134-139
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• 2005

Purpose : To evaluate the usefulness of acoustic noise attenuator on auditory fMRI examination. Materials and methods : The acoustic noise attenuator consists of mask, earmuff and silicon earplug. The soft polyurethane sheet and polyurethane form , which has a good soundproof characteristic were used for mask and earmuff. Auditory fMRI experiments of 500 Hz pure tone stimulation were performed in three different cases; first all of mask, earmuff and earplug, secondly earmuff and earplug only and finally without attenuator in 4 normal hearing volunteers. For data acquisition, BOLD MR imaging technique was employed at a 1.5T MR scanner equipped with high performance gradient system. The raw data were analyzed using a SPM-99 analysis software and the activation maps were obtained. Results : In case of all items of acoustic attenuator used, the results revealed that activation was focused on primary auditory area. When only earmuff and earplug were used, the results showed that the activation spread over primary auditory and secondary associative areas. Last, when no device used, only weak activation was observed on the right auditory cortex. Conclusion : It is expected that the acoustic noise attenuator, which consists of earplugs, earmuffs and mask, is a very useful device in auditory fMRI study.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.614-619
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• 1997

The hydraulic pressure pulsation has on the effected on the acoustic nosie and control performance of the hydraulic-servo system. The Helmholtz attenuator introduction on the hydraulic line is an efficient device to reduce the hydraulic pulsation. The salient feature of causing hydraulic pulsation and the frequency characteristics of Helmholtz attenuator are studied. The hydraulic filter design parameters such as the locating position, connecting orifice area and accumulator volume are mathematically analyzed. The instrumental works are carried out with the remarkable reduction of the hydraulic pressure pulsation magnitude and the acoustic noise level.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.2
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• pp.44-50
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• 2012

When the fiber Bragg gratings are embedded in a fiber-optic Sagnac loop for measuring temperature or strain, it is difficult to separate the Bragg wavelengths. The transmitted light is mixed with the reflected Bragg wavelengths in the photo-detector, working as noises. To suppress the noises, we placed the FBG sensors and a fiber-optic attenuator at asymmetric positions in the loop. With the arrangement the reflected light became much bigger than the transmitted light, enabling the separation of the reflected Bragg wavelengths with almost the same signal-to-noise ratio of the FBG sensors outside the loop.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.71-76
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• 2023

This paper studied a method of attenuating nonlinear noise using a Wiener filter of a neural network structure in an acoustic noise attenuator. This system improves nonlinear noise attenuation performance with a deep learning algorithm using a neural network Wiener filter instead of using a conventional adaptive filter. A voice is estimated from a single input voice signal containing nonlinear noise using a 128-neuron, 8-neuron hidden layer and an error back propagation algorithm. In this study, a simulation program using the Keras library was written and a simulation was performed to verify the attenuation performance for nonlinear noise. As a result of the simulation, it can be seen that the noise attenuation performance of this system is significantly improved when the FNN filter is used instead of the Wiener filter even when nonlinear noise is included. This is because the complex structure of the FNN filter expresses any type of nonlinear characteristics well.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.987-994
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• 2020

In this paper, we studied the effect of kernel size of CNN layer on performance in acoustic noise attenuators. This system uses a deep learning algorithm using a neural network adaptive prediction filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using a 100-neuron, 16-filter CNN filter and an error back propagation algorithm. This is to use the quasi-periodic property in the voiced sound section of the voice signal. In this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed to verify the performance of the noise attenuator for the kernel size. As a result of the simulation, when the kernel size is about 16, the MSE and MAE values are the smallest, and when the size is smaller or larger than 16, the MSE and MAE values increase. It can be seen that in the case of an speech signal, the features can be best captured when the kernel size is about 16.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.5
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• pp.93-99
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• 2006

Flexible couplers are widely used for exhaust transmitted vibration reduction in vehicles. This paper describes an investigation into the acoustical characteristics of exhaust flexible coupler by the simulation and testing. Computational acoustic simulation is carrying out to investigate resonance frequency and transmission loss of decoupler using the boundary element method and transfer matrix approach. To confirm the acoustical simulation results of exhaust decoupler, we compare with measured experimental results by the test of transmission loss measurement system. In the comparison with simulation results and tests results, there is correctly fit the resonance frequency and the trend of transmission loss. Also, we show that the acoustical structure of decoupler is analogous to the expended tube or side branch resonator. The characteristics of exhaust decoupler have a marked increase in the acoustic attenuation at the specified frequency bend. Therefore the decoupler is applied to develop the exhaust system not only for the vibration isolator but also for the noise attenuator.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.113-118
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• 2024

In this paper, we propose an optimal wavelet in a system for canceling background noise of acoustic signals. This system performed Discrete Wavelet Transform(DWT) instead of the existing Short Time Fourier Transform(STFT) and then improved noise cancellation performance through a deep learning process. DWT functions as a multi-resolution band-pass filter and obtains transformation parameters by time-shifting the parent wavelet at each level and using several wavelets whose sizes are scaled. Here, the noise cancellation performance of several wavelets was tested to select the most suitable mother wavelet for analyzing the speech. In this study, to verify the performance of the noise cancellation system for various wavelets, a simulation program using Tensorflow and Keras libraries was created and simulation experiments were performed for the four most commonly used wavelets. As a result of the experiment, the case of using Haar or Daubechies wavelets showed the best noise cancellation performance, and the mean square error(MSE) was significantly improved compared to the case of using other wavelets.