• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.351-359
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• 2003

In this paper, we proposed a method for acoustic echo cancellation based on independent component analysis. When the large acoustic noise is picked up by the microphone, the performance of echo cancellation decreased. We used two microphones that received echo signal which is linearly mixed with the noise, then separated the echo signals from the received signals with independent component analysis algorithm. The separated echo signal is used for the reference signal of adaptive algorithm which leads to better performance of the echo cancellation. Computer simulation results show the validity of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1191-1198
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• 2007

In this paper, the fault detection and noise cancellation algorithm based on wavelet transform was developed to locate the fault more accurately. Specially, noise cancellation algorithm was based on the correlation of wavelet coefficients at multi-scales. Fault detection, classification and location algorithm were tested by EMTP simulation on real power cable system. From these results, the faults can be detected and located even in very difficult situations, such as at different inception angle and fault resistance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.857-866
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• 2009

In this paper, a noise cancellation-method using microphone array for digital hearing aids is proposed. The microphone array is located around the ear of a dummy. Speech sound is generated from the forward speaker positioned in the front of the dummy and noise sound is generated from the backward speaker. The speech and noise are mixed in the air space and entered into the microphones. VAD(voice activity detector) and ANC(adaptive noise cancellation) methods were used to eliminate noise in the sound of the microphones. 10 two-syllable words and 4 sentences were used for speech signals. Babble and car interior noise were used for noise signals. The performance of the proposed algorithm was evaluated by SNR(signal-to-noise ratio) and PESQ-MOS(perceptual evaluation of speech quality-mean opinion score). In babble noise condition, SNR was improved as much as $7.963{\pm}1.3620dB\;and\;3.968{\pm}0.6659dB$ for words and sentences respectively. In the case of car interior noise, SNR was improved as $10.512{\pm}2.0665dB\;and\;6.000{\pm}1.7642dB$ for words and sentences respectively. PESQ-MOS of the babble noise was improved as much as $0.1722{\pm}0.0861$ score for words and $0.083{\pm}0.0417$ score for sentences. And PESQ-MOS of the car interior noise was improved as $0.2661{\pm}0.0335$ score and $0.040{\pm}0.0201$ score for words and sentences respectively. It is verified that the proposed algorithm has a good performance in noise cancellation of microphone array for digital hearing aids.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.891-895
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• 2001

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 filtered-x 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.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.184-190
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• 2008

The fault location algorithm based on stationary wavelet transform was developed to locate the fault point more accurately. The stationary wavelet transform(SWT) was introduced instead of conventional discrete wavelet transform(DWT) because SWT has redundancy properties which is more useful in noise signal processing. In previous paper, noise cancellation technique based on the correlation of wavelet coefficients at multi-scales was introduced, and the efficiency was also proved in full. In this paper, fault section discrimination and fault location algorithm using noise cancellation technique were tested by ATP simulation on real power cable systems. From these results, the fault can be located even in very difficult and complicated situations such as different inception angle and fault resistance.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2244-2247
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• 2003

In this paper, we present a method which can minimize distortion from desired signal in thoracic sound signal processing. We firstly chose the proper wavelet mother function to reduce noise components. Secondly, we chose a clean thoracic sound, then added Gaussian noise and 3 step(10, 15, 20db) uniform noise to it. Finally, the various wavelet functions are applied for noise cancellation. To evaluate the efficiency of this study, we computed SNR and RSE value. Then we found the optimal mother wavelet function for thoracic sound.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.41-45
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• 2019

In this paper, we propose the interference cancellation scheme of end-to-end method algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information of receiver by applying a deep learning model at the receiver. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• MALSORI
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• no.52
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• pp.121-132
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• 2004

This paper is aiming at real-time implementation of adaptive noise cancellation system using DSP processor. ACHARF algorithm, which guarantees stability and fast convergence by adaptive compensator, is used on this DSP system. For the experiments, TLV320AIC23 stereo CODEC of TI Inc. is used with TMS320C6413 DSP processor. Signals of primary input and reference input are obtained by two microphones. The primary input is the voice plus noise signal and the reference input is white noise or real noise. The experimental results show that ANC system using DSP processor with ACHARF is verified to be an effective speech enhancement method for various speech processing units.

• Journal of KSNVE
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• v.10 no.6
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• pp.941-948
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• 2000

Active noise control is an approach to reduce the noise by utilizing 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 an adaptive algorithm is used to adaptively estimate the parameters of the process. Based on these parameter estimates, a canceling signal is generated. Oppenheim assumed that transfer function characteristics from the canceling source to the error sensor is only a propagation delay. This paper proposes a modified Oppenheim algorithm by considering transfer characteristics of speaker-path-sensor This transfer characteristics is adaptively cancelled by the proposed adaptive modeling technique. Feasibility of the proposed method is proved by computer simulations with artificially generated random noises and sine wave noise. The details of the proposed architecture. and theoretical simulation of the noise cancellation system for three dimension enclosure are presented in the Paper.