• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.3
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• pp.171-177
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• 2016

Noise problem that occurs in living environment is a big trouble in the economic, social and environmental aspects. In this paper, the filtered-X LMS algorithms, the adjoint LMS algorithms, and the robust adjoint LMS algorithms will be introduced for applications in active noise control(ANC). The filtered-X LMS algorithms is currently the most popular method for adapting a filter when the filter exits a transfer function in the error path. The adjoint LMS algorithms, that prefilter the error signals instead of divided reference signals in frequency band, is also used for adaptive filter algorithms to reduce the computational burden of multi-channel ANC systems such as the 3D space. To improve performance of the adjoint LMS ANC system, an off-line measured transfer function is connected parallel to the LMS filter. This parallel-fixed filter acts as a noise controller only when the LMS filter is abnormal condition. The superior performance of the proposed system was compared through simulation with the adjoint LMS ANC system when the adaptive filter is in normal and abnormal condition.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.130-135
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• 2006

There have been experiments dealing with the possibility of the actualization of the ANC system by means of operating the DSP adaptation filter. This filter is composed of various filters(including X-LMS algorithm, Filter-U algorithm, and Full-Feedback-Filter-U algorithm) that use ventilation fans and loudspeakers as a primary source in a circular duct as an experimental device. In this operation, the ANC system using the X - LMS algorithm was found to be more effective in reducing noise than without such system. When applying the input signal of the DSP board Full Feedback-Filtered-U algorithm system while having in mind that the additionally installed second control signal was gone through feedback and mixed into the detection microphone installed near the ventilation fan when using the first ventilation fan, the system was not emitted, but maintained stable during the operation of the control filter. At this point, noise tended to decrease at a maximum of l0dB compared to other algorithms at the frequency band of 170-250Hz.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.570-575
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• 2019

Active muffler is implemented by applying active noise control technique to reduce exhaust noise of automobile muffler. Conventional Filtered_x LMS algorithm has a problem that the degree of control filter becomes very large and convergence deteriorates when acoustic feedback is present. The recursive LMS algorithm can compensate for this problem because it can be easily diverted in the adaptive filter adaptation process. In this paper, the structure of the primary path and the secondary path transfer function is designed as the IIR filter to improve the convergence performance and the computational burden, and the stabilization filter algorithm is applied to secure stability which is a disadvantage of the IIR filter structure. The stabilization filter algorithm plays a role of pulling the pole into the unit circle to prevent the pole of the transfer function corresponding to the acoustic feedback from diverging during the adaptation process. In this way, the computational burden of the active muffler system and the convergence performance can be improved. In order to show the usefulness of the proposed system, we compared the performance of the proposed Filtered_x LMS algorithm with the performance of the proposed system for the exhaust sound of a diesel engine, which is a variable environment. Compared to conventional algorithm, proposed algorithm's computational burden is less than half, and convergence performances are more than 4 times.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.2
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• pp.146-155
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• 2012

The filtered-x LMS(FX-LMS) algorithm has been applied to the active noise control(ANC) system in an acoustic duct. This algorithm is designed based on the FIR(finite impulse response) filter, but it has a slow convergence problem because of a large number of zero coefficients. In order to improve the convergence performance, the step size of the LMS algorithm was modified from fixed to variable. However, this algorithm is still not suitable for the ANC system of a short acoustic duct since the reference signal is affected by the backward acoustic wave propagated from a secondary source. Therefore, the recursive filtered-u LMS algorithm(FU-LMS) based on infinite impulse response(IIR) is developed by considering the backward acoustic propagation. This algorithm, unfortunately, generally has a stability problem. The stability problem was improved by using an error smoothing filter. In this paper, the recursive LMS algorithm with variable step size and smoothing error filter is designed. This recursive LMS algorithm, called FU-VSSLMS algorithm, uses an IIR filter. With fast convergence and good stability, this algorithm is suitable for the ANC system in a short acoustic duct such as the intake system of an automotive. This algorithm is applied to the ANC system of a short acoustic duct. The disturbance signals used as primary noise source are a sinusoidal signal embedded in white noise and the chirp signal of which the instantaneous frequency is variable. Test results demonstrate that the FU-VSSLMS algorithm has superior convergence performance to the FX-LMS algorithm and FX-LMS algorithm. It is successfully applied to the ANC system in a short duct.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.89-98
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• 2000

This paper presents an active noise cancelling model using wavelet transform and subband filter banks based on adaptive filter. The analysis filter banks decompose input and error signals into QMF filter banks of lowpass and highpass bands. Each filter bank uses wavelet filter with dyadic tree structure. The decomposed input and error signals are iterated by adaptive filter coefficients of each subband using filtered-X LMS algorithm. The synthesis filter banks make output signal of wideband with perfect reconstruction to prepare adaptive filter output signals of each subband. The analysis and synthesis niter hants use conjugate quadrature filters for Pefect reconstruction. Also, The delayed LMS algorithm model for on-line identification of error path transfer characteristics is used gain and acoustic time delay factors. The proposed adaptive active noise cancelling modelis suggested by system retaining the computational and convergence speed advantage using wavelet subband filter banks.

• 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.

• Journal of KSNVE
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• v.2 no.2
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• pp.107-116
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• 1992

An adaptive signal processing technique is implemented for the active noise cancellation of the plane acoustic wave propagating in a duct. To avoid the instability caused by the acoustic feedback from the control speaker to the detect microphone, an off-line modeling of the acoustic feedback plant is done using the FIR filter. Auxiliary path required for the filtered-x LMS algorithm is modeled as well. Before going into the experiments, a simulation is carried out under the same conditions with experiments. The simulation shows that the longer the length of the adaptive filter is, the better the results are achieved. Experiments have been carried out at lower audio frequency range (50 - 400Hz), and the results are in good agreements with those of simulation study. As a results of this adaptive noise control, around 50dB is reduced for a pure tone noise, and for a bandlimited noise with the bandwidth of 316Hz, a maximum of 30dB noise reduction is attained.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.993-995
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• 1996

This paper proposes an adaptive Filtered-X LMS algorithm with robustness. The proposed system updates filter parameters to keep the performance even if the conventional Active Noise Control (ANC) system well does not work. As a result of the computer simulation, the proposed system has all the advantage of the system using the conventional Filtered-X LMS algorithm. Also some results from the proposed algorithm present that the system is stable when the Adaptive filter does not work. Therefore, the proposed system using the Adaptive Filtered-X LMS algorithm must have much advantage than the system using the conventional algorithm in the side of the stability and performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.343-348
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• 2011

This paper introduces a design for real time wireless headset using ANC (active noise control) system based on NFxLMS adaptive filter algorithm. The training time of the proposed system is significantly reduced by using the RMS delay spread of a channel as an error correction parameter, and convergence rate of the FxLMS filter has been improved with updating the coefficients of the NFxLMS filter, which we have got during the training process. Our system has shorter training time and better convergence rate at the same noise reduction level than the conventional system under real noisy environment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.231-239
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• 2009

The filtered-x LMS (FX-LMS) algorithm has been applied to the active noise control (ANC) system in an acoustic duct. This algorithm is designed based on the FIR (finite impulse response) filter, but it has a slow convergence problem because of a large number of zero coefficients. In order to improve the convergence performance, the step size of the LMS algorithm was modified from fixed to variable. However, this algorithm is still not suitable for the ANC system of a short acoustic duct since the reference signal is affected by the backward acoustic wave propagated from a secondary source. Therefore, the recursive filteredu LMS algorithm (FU-LMS) based on infinite impulse response (IIR) is developed by considering the backward acoustic propagation. This algorithm, unfortunately, generally has a stability problem. The stability problem was improved by using an error smoothing filter. In this paper, the recursive LMS algorithm with variable step size and smoothing error filter is designed. This recursive LMS algorithm, called FU-VSSLMS algorithm, uses an IIR filter. With fast convergence and good stability, this algorithm is suitable for the ANC system in a short acoustic duct such as the intake system of an automotive. This algorithm is applied to the ANC system of a short acoustic duct. The disturbance signals used as primary noise source are a sinusoidal signal embedded in white noise and the chirp signal of which the instantaneous frequency is variable. Test results demonstrate that the FU-VSSLMS algorithm has superior convergence performance to the FX-LMS algorithm and FX-LMS algorithm. It is successfully applied to the ANC system in a short duct.