• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.409-416
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• 2022

This paper relates to the blind noise separation method of time-delayed convolutive mixed signals. Since the mixed model of acoustic signals in a closed space is multi-channel, a convolutive blind signal separation method is applied and time-delayed data samples of the two microphone input signals is used. For signal separation, the mixing coefficient is calculated using an inverse model rather than directly calculating the separation coefficient, and the coefficient update is performed by repeated calculations based on secondary statistical properties to estimate the speech signal. Many simulations were performed to verify the performance of the proposed blind signal separation. As a result of the simulation, noise separation using this method operates safely regardless of convolutive mixing, and PESQ is improved by 0.3 points compared to the general adaptive FIR filter structure.

• 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.

• Journal of Aerospace System Engineering
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• v.17 no.2
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• pp.26-37
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• 2023

Urban air mobility (UAM) is being developed as part of the next-generation aircraft, which could be a viable solution to entrenched problems of urban traffic congestion and environmental pollution. A new airport platform called vertiport as a space where UAM can take off and land vertically is also being introduced. Noise regulations for UAM will be strict due to its operation in a highly populated urban area. Ground effects caused by vertiport can directly affect aerodynamic forces and noise characteristics of UAM. In this study, ground effects of vertiport on aerodynamic loads, vorticity field, and far-field noise were analyzed using Lattice-Boltzmann Method (LBM) simulation and Ffowcs Williams and Hawkings (FW-H) acoustic analogy with a permeable surface method.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.9
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• pp.778-786
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• 2012

Sensors that are used on wireless sensor networks can be divided into two types: directional sensors, such as PIR, image, and electromagnetic sensors; and non-directional sensors, such as seismic, acoustic and magnetic sensors. In order to guarantee the line-of-sight of a directional sensor, the installation location of the sensor must be higher than ground level. Among non-directional sensors, seismic sensors should be installed on the ground in order to ensure the maximal performance. As a result, seismic sensors may have network connectivity problems due to communication failure. In this paper, we propose a 3D node deployment method to maximize the coverage and the network connectivity considering the sensor-specific properties. The proposed method is for non-directional sensors to be placed on the ground, while the directional sensor is installed above the ground, using trees or poles, to maximize the coverage. As a result, through the topology that the detection data from non-directional sensors are transmitted to the directional sensor, we can maximize the network connectivity. Simulation results show that our strategy improves sensor coverage and network connectivity.

• Journal of Korea Game Society
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• v.12 no.2
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• pp.3-14
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• 2012

As one of elements to be able to endow more exciting and higher degree of completion for game, the feeling of hit is realized by image, sound and body-sensing (vibration) effects. When the feeling of hit is realized by game developer, most proper effects will be chosen with regard to genre, system and standpoint of world for the game. In general, most of choices for the effects are performed by the experience of game developer or referring the other games. Nevertheless the related studies are not significant in comparison with the importance for the feeling of hit, and the fundamental studies are mostly not accomplished. This paper introduces a study on efficiency and important factors for the feeling of hit by analyzing the properties and degrees of feeling for all effects to represent the feeling of hit through experiments. For this, a software simulator was implemented to test all effects and therewith the final results are presented through questionnaires for the feeling of hit sent to gamers. Our results are expected to be used to accomplish higher degree of completion for mobile games or web games with limited resources.

• Fire Science and Engineering
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• v.32 no.3
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• pp.67-75
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• 2018

To reduce the number of casualties in the case of fire, an alarm sound needs to be delivered to the people who remain in the apartment unit. On the other hand, it was reported that the fire alarm sound generated in the elevator hall was not delivered sufficiently to the people staying in the apartment units. In this study, the background noise level and noise level generated in an apartment unit were measured during the day and night time. In addition, the transmission of the fire alarm sound into the each room of apartment units was simulated and compared with the background noise level. The fire alarm sound generated in the elevator halls was reduced by the fire door and doors, and was not transmitted sufficiently into the internal spaces of the apartment units. Starting evacuation action was difficult after hearing the fire alarm sound generated outside the apartment units. To improve the transmission of an alarm sound to the inner spaces of apartment units, an acoustic simulation was carried out for cases where the alarm sound generator was installed on a wall-pad in the living room and the alarm sound generator was installed on the ceiling of each rooms in apartment units. Background noise of + 15 dB and 75 dB (A) were satisfied when alarm sound generator was installed on the ceiling of each room.