• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.3
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• pp.111-116
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• 2013

Noise problem that occurs on the road is raising a lot of problems in the economic, social and environmental aspects. The objective of this paper is to propose ANC(active noise cancellation)-based road traffic noise reduction algorithm-model which can reduce noise by generating frequency opposed to noise sources to improve and complement the problem that existing physical form of a noise barrier. In this paper, we measured the noise characteristic from collection of two difference car noise also ANC simulation has been performed by using road traffic noises input. In order to compare the control performance, we performed noise reduction simulation of ANC by filtered-X LMS algorithm and delayed control signal injection. As a result of this simulation, we confirmed that convergence performance and noise decrease effect to the filtered-X LMS algorithm by inputting the road traffic noise.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.1
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• pp.26-36
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• 2023

공군 조종사들이 비행 중에 겪는 지속적이고 강도 높은 소음은 조종사의 생리적(physiological) 및 심리적(psychological) 상태에 부정적인 영향을 미칠 수 있다. 이는 조종사의 비행 능력(performance)에 부정적인 영향을 주게 되며 임무 완수 및 비행 안전을 저해시키는 치명적인 결과로 이어질 수 있다. 대한민국 공군은 조종사들의 청력 보호를 위해 수동 소음 감쇠(Passive Noise Cancellation, PNC) 및 능동 소음 감쇠(Active Noise Cancellation, ANC) 기술이 적용된 헤드셋 및 헬멧을 사용 중이다. 그러나, 소음 저감 기술이 조종사의 청력 보호, 비행 능력, 및 비행 안전에 미치는 효용성에 대한 공군 조종사의 인식은 아직 연구된 바가 없다. 따라서 본 연구는 소음과 관련된 이론적 배경을 고찰하였고, 이후 설문조사를 통해 공군 조종사들(n=154)의 조종석 내 소음 및 소음 감쇠 기술에 대한 인식을 분석하였다. 분석 결과, 능동 소음 감쇠(ANC) 기술이 적용된 헤드셋 및 헬멧의 사용은 소음이 조종사의 생리적 상태에 미치는 영향에는 유의미한 효과가 없지만(p=0.402), 심리적 상태에 미치는 영향은 유의미하게 감소시키는 것으로 나타났다(p<0.001). 따라서, 능동 소음 감쇠(ANC) 기술이 적용된 비행 헤드셋 및 헬멧 사용의 필요성을 강조하였고, 이를 통해 조종사의 비행 능력(performance) 저하 방지 및 비행 안전 증진에 기여하고자 한다.

• Annual Conference of KIPS
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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.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.224-229
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• 2004

In this paper, we propose a new Active Noise Control (ANC) system using a teacher forced Blind Source Separation (BSS) algorithm. The Blind Source Separation based on the Independent Component Analysis (ICA) separates the desired sound signal from the unwanted noise signal. In the proposed system, the BSS algorithm is used as a preprocessor of ANC system. Also, we develop a teacher forced BSS learning algorithm to enhance the performance of BSS. The teacher signal is obtained from the output signal of the ANC system. Computer experimental results show that the proposed ANC system in conjunction with the BSS algorithm effectively cancels only the ship engine noise signal from the linear and convolved mixtures with human voice.

• International Journal of Highway Engineering
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• v.17 no.6
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• pp.11-18
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• 2015

PURPOSES : The purpose of this thesis is to evaluate the effectiveness of an active noise cancellation (ANC) system in reducing the traffic noise level against frequencies from the predictive model developed by previous research. The predictive model is based on ISO 9613-2 standards using the Noble close proximity (NCPX) method and the pass-by method. This means that the use of these standards is a powerful tool for analyzing the traffic noise level because of the strengths of these methods. Traffic noise analysis was performed based on digital signal processing (DSP) for detecting traffic noise with the pass-by method at the test site. METHODS : There are several analysis methods, which are generally divided into three different types, available to evaluate traffic noise predictive models. The first method uses the classification standard of 12 vehicle types. The second method is based on a standard of four vehicle types. The third method is founded on 5 types of vehicles, which are different from the types used by the second method. This means that the second method not only consolidates 12 vehicle types into only four types, but also that the results of the noise analysis of the total traffic volume are reflected in a comparison analysis of the three types of methods. The constant percent bandwidth (CPB) analysis was used to identify the properties of different frequencies in the frequency analysis. A-weighting was applied to the DSP and to the transformation process from analog to digital signal. The root mean squared error (RMSE) was applied to compare and evaluate the predictive model results of the three analysis methods. RESULTS : The result derived from the third method, based on the classification standard of 5 vehicle types, shows the smallest values of RMSE and max and min error. However, it does not have the reduction properties of a predictive model. To evaluate the predictive model of an ANC system, a reduction analysis of the total sound pressure level (TSPL), dB(A), was conducted. As a result, the analysis based on the third method has the smallest value of RMSE and max error. The effect of traffic noise reduction was the greatest value of the types of analysis in this research. CONCLUSIONS : From the results of the error analysis, the application method for categorizing vehicle types related to the 12-vehicle classification based on previous research is appropriate to the ANC system. However, the performance of a predictive model on an ANC system is up to a value of traffic noise reduction. By the same token, the most appropriate method that influences the maximum reduction effect is found in the third method of traffic analysis. This method has a value of traffic noise reduction of 31.28 dB(A). In conclusion, research for detecting the friction noise between a tire and the road surface for the 12 vehicle types needs to be conducted to authentically demonstrate an ANC system in the Republic of Korea.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.238-239
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• 2008

In this paper, active noise control (ANC) of a Volterra system with a nonlinear secondary path is proposed in the presence of a linear acoustic feedback, whereby the conventional ANC of a linear system with online acoustic feedback-path modeling is further extended to ANC of a Volterra system with a linear acoustic feedback path. In particular, the proposed ANC system consists of two adaptive Volterra filters (for nonlinear noise control and nonlinear adaptive noise cancellation) and one feedback-path modeling filter. Simulation results show that the proposed approach yields more effective reduction of disturbances arising from the acoustic feedback, in addition to high nonlinear ANC performance.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.7
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• pp.499-507
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• 2003

This paper presents a method for active noise control (ANC) in a duct by using a ring-tyPe smart foam. The ring-type smart foam consists of an elastic porous material of lining shape and a PVDF film embedded In the material. The PVDF element acts as a secondary sound source to reduce the noise. Active noise control using a ring-type smart foam is only effective locally because of the way to excite radially. To enlarge the quiet zone, the duct Is lined with additional acoustic foam between the smart foam and the error microphone. When cancellation path ks optimized by the LMS/RLS algorithm, the computation power is reduced while control performance Is maintained. The filtered-x LMS algorithm is used to minimize the error signal.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.525-529
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• 2002

ANC technic can overcome the limited performance of passive noise control at the low frequency range. But it has the local quiet control region in general. In this paper, it is discussed that the global noise control in a circular duct using a ring type smart foam and a porous material. LMS algorithm and RLS algorithm are used to find optimal orders of cancellation path. Experiments are performed to compare the efficiency of RLS algorithm with that of LMS algorithm.

• Annual Conference of KIPS
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• 2023.11a
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• pp.1108-1109
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• 2023

본 논문은 선박 내부 소음을 효과적으로 감소시키기 위한 ANC(Active Noise Cancellation)및 인공 지능 (AI) 결합 시스템의 개발과 적용에 관한 연구를 다룬다. 선박 환경에서의 소음은 승원의 스트레스 증가와 불편을 초래하므로, 이를 해결하기 위한 방법을 제안하고자 한다. 외부 소음과 내부 소음 데이터를 수집하고, STFT(Short-Time Fourier Transform)알고리즘을 통해 소음 데이터를 분석 가능한 형태로 전처리한다. 그 후, LSTM(Long Short-Term Memory)알고리즘을 사용하여 선박 외부에서 발생한 소음을 입력으로 받아 내부에서 들리는 외부 소음을 예측하고 제어하는 모델을 훈련시킨다. 이후 최적화 과정을 거쳐 예측 소음의 반대 파형을 생성 및 출력을 통해 ANC 를 구현한다.

• 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.