• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.437-442
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• 2000

An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional FX-LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.442-450
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• 2000

Active noise control is an approach to noise reduction in which a secondary noise source that destructively interferes with the unwanted noise. In general, active noise control systems rely on multiple sensors to measure the unwanted noise field and the effect of the cancellation. This paper develops an approach that utilizes a single sensor. The noise field is modeled as a stochastic process, and a time-adaptive algorithm is used to adaptively estimate the parameters of the process. Based on these parameter estimates, a canceling signal is generated. Opppenheim model assumed that transfer function characteristics from the canceling source to the error sensor is only propagation delay. But this paper proposes a modified Oppenheim model by considering transfer characteristics of acoustic device and noise path. This transfer characteristics is adaptively cancelled by adaptive model. This is proved by computer simulation with artifically generated random noise and sine wave noise. The details of the proposed architecture, and theoretical simulation and experimental results of the noise cancellation system for three dimension enclosure are presented in the paper.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.873-878
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• 1994

Application of the filtered-x LMS adaptive filter to active noise cancellation requires to estimate the transfer characteristics between the output and the error signal of the adaptive canceller. We analyze the effects of estimation accuracy on the convergence behavior of the canceller when the input noise is modeled as a sinusoid.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.276-279
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• 2015

This paper propose a method to reduce the backscatter field of the curved aluminum plate using the cancellation system. The cancellation circuit is composed of a circulator, a LNA(Low Noise Amplifier), a VGA(Variable Gain Amplifier) and two phase shifters. Prior to experiment, we performed simulations to confirm the possibility using FDTD(Finite Difference Time Domain) simulator. We confirmed that the backscatter field could be reduced by the cancellation circuit when we changed the appropriate gain and phase. Finally, we performed an experiment to verify the performance of the cancellation circuit.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2050-2058
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• 2001

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise cancellation (ANC) requires estimating of the transfer characteristic of the acoustic path between the output and error signal of the adaptive controller. The results of 7he convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• International Journal of Highway Engineering
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• v.15 no.4
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• pp.75-83
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• 2013

PURPOSES: The purpose of study is to understand the characteristic of driving noise from the front and rear tire for effective active noise cancellation application. METHODS : As literature review, noise measurement methods were reviewed. Noise measurement conducted at three kind of section by road slope using CPX(Close Proximity Method). Noise data was compared by total sound pressure level and 1/3 octave band frequency sound pressure level. Also, each section was compared by T-test using SPSS. RESULTS : In the case of the uphill section, it was shown that the sound pressure level of the front tire at Sugwang-Ri and Sinchon-RI sections was higher than that of the rear tire in low and high frequency band. In the case of high slope section of Sangsaek-Ri, the sound pressure level of the front tire was higher than that of the rear tire in high frequency. Also, in the case of the downhill section, it was shown that the sound pressure level of the front tire at Sugwang-Ri and Sinchon-RI sections was higher than that of the rear tire in low frequency band. However, the sound pressure levels of both the front and rear tires were approximately the same in the high slope section of Sangsaek-Ri. The result of T-test showed that total sound pressures of the front and rear tires were not different from each other in the case of high slope and high speed. CONCLUSIONS: Road slope was not an important variable for effective active noise cancellation.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1066-1068
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• 1996

This paper concerns about the active noise canceller via filtered-X LMS algorithm. There are various kinds of algorithms to implement a active noise canceller. Traditional LMS algorithms are not enough to implement a sharp noise cancellation characteristics. We simulates a filtered-X LMS algorithm and implements an algorithm to the TMS320C5x DSP processor and shows that result.

• Journal of IKEEE
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• v.5 no.1 s.8
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• pp.16-23
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• 2001

Passive noise reduction is a classical approach to attenuate industrial noise, and an active noise cancellation has several advantages over the passive noise cancellation. The active noise reduction system offers a better low frequency performance with a smaller and lighter system. This paper presents a simple active closed loop control system which consists of an controller for inverting and compensating the phase delay, a microphone for picking up the external noise, and a loudspeaker for radiating the acoustic out of phase signal to reduce the external noise, and external noise can be reduced after compensating the phase difference to be $180^{\circ}$ in the frequency of maximum value in the amplitude response. The noise of the phase delay covered from $50^{\circ}\;to\;310^{\circ}$ tends to be reduced in the active noise control system and it is possible to obtain a noise cancelling of up to approximately 20[dB] at the ears in the enclosurer.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.10-15
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• 1998

Application of the Filtered-X LMS adaptive filter to active noise cancellation requires to estimate the transfer characteristics between the output and the error signal of the adaptive canceler. In this paper, we derive an adaptive cancellation algorithm and analyze is convergence behavior when the acoustic noise is assumed to consist of multiple sinusoids. The results of the convergence analysis of the Filtered-X LMS algorithm indicate that the effects of parameter estimation inaccuracy on the convergence behavior of the algorithm are characterize by two distinct components : Phase estimation error and estimated magnitude. In particular, the convergence of the Filtered-X LMS algorithm is show to be strongly affected by the accuracy of the phase response estimate. Simulation results of the algorithm are presented which support the theoretical convergence analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1203-1206
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• 2021

소음은 난청, 스트레스 등의 원인이 된다. 본 연구에서는 ANC(Active Noise Cancellation)을 바탕으로, 기술적인 방법을 통해 소음을 저감 시키는 스피커를 구현하였다. ANC 란 소음 주파수의 위상을 180° 변환하여 주파수와 레벨이 동일한 역 소음을 발생시켜 주변 소음을 저감, 차단하는 기술이다. 현재 시중 제품들에 적용되는 일반적인 ANC 의 경우, 피드백(Feedback) 방식이라는 점과 시간 지연(Time gap)이 발생한다는 한계가 있다. 이를 보완하기 위해 AI 학습으로 소음을 미리 예측하여 시간 지연을 줄이는 방법을 고안했다. 순환 신경망(RNN)의 장기의존성 문제를 해결하는 시계열 예측 딥러닝 알고리즘인 LSTM(Long Short-Term Memory Network) 모델을 사용하였다. 또한, AI 학습 효율을 향상시킬 수 있는 하드웨어 장비들을 활용하였다.