• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.654-656
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• 1998

This paper deal with the implementation of Active Noise Control (ANC) in a short duct. In case of ANC in the air duct, input microphone, control speaker, error microphone are used. But we can't use input microphone because of the characteristics of short duct. It is difficult to avoid howl. So we propose single-channel adaptive feedback ANC which is composed only error microphone and control speaker without input microphone. FXLMS algorithm is used to compensate for the time delay of the error path. Experimental results show that the controller reduce noise signal sufficiently.

• Speech Sciences
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• v.11 no.3
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• pp.157-164
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• 2004

Human has average pitch-level when speak naturally. That is 'Habitual pitch level'. However, if noise added at speech, the pitch-wave is changed irregularly. We can estimate noise level of speech by using this point. This paper calculates energy level of the input speech, pitch period from of above limited energy level by NAMDF (Normalized Average Magnitude Difference Function) method, after cut each frame by pitch period unit, and propose a method that estimate noise level through closed pitch of input speech.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.551-554
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• 2005

The method of the reduction of the automotive induction noise can be classified by the method of passive control and the method of active control. However, the passive control method has a demerit to reduce the effect of noise reduction at low frequency (below 500Hz) range and to be limited by a space of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the LMS (Least-Mean-Square) algorithm because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, When the Filtered-X LMS (FXLMS) algorithm is applied to an ANC system. However, the convergence performance of LMS algorithm goes bad when the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. Thus Normalized FXLMS algorithm was developed to improve the control performance under the rapid acceleration. The advantage of Normalized FXLMS algorithm is that the step size is no longer constant. Instead, it varies with time. But there is one additional practical difficulty that can arise when a nonstationary input is used. If the input is zero for consecutive samples, then the step size becomes unbounded. So, in order to solve this problem. the Correlation FXLMS algorithm was developed. The Correlation FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Correlation FXLMS Is presented in comparison with that of the other FXLMS algorithms based on computer simulations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.111-123
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• 2019

In this paper, we propose a method for defining the input termination for on-wafer-type device under test (DUT) measurement. Using the newly defined input termination and noise wave correlation matrix (NWCM) measurement method based on noise power ratio, the NWCM of the on-wafer-type DUT was measured. We demonstrate a noise measurement configuration that includes wafer probes and bias tees to measure the on-wafer DUT. The S-parameter of the adapter that combines the bias tee, probe, and a line terminated by open is required to define the input termination for on-wafer DUT measurement. To measure the S-parameter of the adapter, a 2-port S-parameter measurement method using 1-port measurement is introduced. Using the measured S-parameters, a method for defining the new input termination for on-wafer-type DUT measurement is applied. The proposed method involves the measurement of the NWCM of the chip with a 1.5 dB noise figure. The noise parameters of the chip were obtained using the measured NWCM. The results indicate that the obtained values of the noise parameters are similar to those mentioned on a datasheet for the chip. In addition, repeated measurements yielded similar results, thereby confirming the reliability of the measurements.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.220-225
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• 2020

This paper investigates the non-intrusive speech intelligibility estimation method in noise environments when the bottleneck feature of autoencoder is used as an input to a neural network. The bottleneck feature-based method has the problem of severe performance degradation when the noise environment is changed. In order to overcome this problem, we propose a novel non-intrusive speech intelligibility estimation method that adds the noise environment information along with bottleneck feature to the input of long short-term memory (LSTM) neural network whose output is a short-time objective intelligence (STOI) score that is a standard tool for measuring intrusive speech intelligibility with reference speech signals. From the experiments in various noise environments, the proposed method showed improved performance when the noise environment is same. In particular, the performance was significant improved compared to that of the conventional methods in different environments. Therefore, we can conclude that the method proposed in this paper can be successfully used for estimating non-intrusive speech intelligibility in various noise environments.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.3
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• pp.169-174
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• 2015

Noise problem that occurs on the road is raising a lot of problems in the economic, social and environmental aspects. The active noise control (ANC) systems based on the filtered-X least mean square(FxLMS) algorithm have a problem with compensating the acoustic feedback of secondary route. However, newly proposed correlation-LMS(CLMS) and expanded CLMS algorithms have slightly much calculation and are minutely behind performance, these have a advantage not in measuring transfer function onerously so that we can easily adapt these in real time. The CLMS and expanded CLMS algorithm was developed to improve the real-time implementation performance under the variable input noise such as road noise environment. In this paper, we compared and analyzed their performance. From the results of the Matlab simulation for an ANC system, it is shown that expanded CLMS algorithms are more convergence speed and keep the desirable performance even in the input of road noise situation.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.12
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• pp.117-124
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• 2008

In early design stage, the simulation of interior noise is useful for the enhancement of the noise, vibration and harshness (NVH) performance in a vehicle. The traditional transfer path analysis (TPA) technology cannot simulate the interior noise since it uses the experimental method. In order to solve this problem, in this paper, the hybrid TPA is developed as the novel approach. The hybrid TPA uses the simulated excitation force as the input force, which excites the flexible body of a car at the mount point, while the traditional TPA uses the measured force. This simulated force is obtained by numerical analysis for the FE (finite element) model of a powertrain. The interior noise is predicted by multiplying the simulated force by the vibro-acoustic transfer function (VATF) of the vehicle. The VATF is the acoustic response in the compartment of a car to the input force at the mount point of the powertrain in the flexible car body. The trend of the predicted interior noise based on the hybrid TPA very well corresponds to the measured interior noise, although there is some difference due to not only the experimental error and the simulation error but also the effect of the air-borne path.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.251-260
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• 2009

In this paper, we studied on an attenuation effect of automobile exhaust noise according to the direction of canceling speaker in ANC system. Automobile exhaust noise was recorded at 800 rpm, 3500 rpm and 5000 rpm of a diesel engine. Directions of canceling speaker can be set to $30^{\circ}$, $90^{\circ}$ and $150^{\circ}$ against the primary noise flow by acrylic ducts to be made for the experimentation. DSP board with TMS320C6416 chip of Texas Instrument Co. used to control the ANC system. The algorithm of this ANC system applied the Filtered-x-LMS algorithm that is modified to compensate for a property of DSP input signal and the secondary-path effect. As an experiment result, the direction of canceling speaker was proved to influence the reduction effect of noise. The $150^{\circ}$ duct in the attenuation effect of noise showed a better result than the $90^{\circ}$ or $30^{\circ}$ duct.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1964-1966
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• 2001

Active Noise control(ANC) is rapidly becoming the most effective way to reduce noises that can otherwise be very difficult and expensive to control. This research presents ANFIS (Adaptive Network Fuzzy Inference System) controller for adaptively noise cancelling in a duct. ANC system generates secondary control sound pressure with same amplitude and with opposite phase as noise to be eliminated. ANFIS controller is trained to optimize its parameters for adaptively cancelling noise. That is ANFIS train its parameters by gradient descent and LSE method so called hybrid method. This paper present ANFIS in active noise control which provides an improvement convergence speed and limitation of linearity condition. It can model nonlinear functions of arbitrary complexity and ANFIS can construct an input-ouput mapping based on both human knowledge in the form of Takagi and Sugeno's fuzzy if-then rules and stipulated input-output data pairs. This paper also shows that the proposed ANFIS active noise control system successfully cancelled noise.

• Journal of the Korean Society of Safety
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• v.12 no.4
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• pp.3-8
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• 1997

This paper shows a performance estimation of windowing a single tone with added Gaussian noise and uniform noise. Signal-to-noise ratio can be determined by the ratio of the output noisy signal variance to the input noisy signal variance of a window. Standard deviation of noise is reduced by windowing Signal-to-noise ratio of the noisy signal is reduced by the windowing operation. Thus, performance of window function can be determined by this filtering operation that improved the signal-to-noise ratio.