• New & Renewable Energy
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• v.16 no.2
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• pp.1-13
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• 2020

The reduction of noise from wind turbines has been studied using various methods. Some examples include controlling wind turbine blades, designing low-noise-emitting wind turbine blades, and using trailing-edge serrations. Among these methods, serration is considered an effective noise reduction method. Various studies have aimed to understand the effects of trailing-edge serration parameters. Most studies, however, have focused on fixed-wing concepts, and few have analyzed noise reduction or developed a prediction method for rotor-type blades. Herein, a noise prediction method, composed of two noise prediction methods for a wind turbine with trailing-edge serrations, is proposed. From the flow information obtained by an in-house program (WINFAS), the noise from non-serrated blades is calculated by turbulent ingestion noise and airfoil self-noise prediction methods. The degree of noise reduction caused by the trailing-edge serrations is predicted in the frequency domain by Lyu's method. The amount of noise reduction is subtracted from the predicted result of the non-serrated blade and the total reduction of the noise from the rotor blades is calculated.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.260-263
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• 2000

A technique that uses linear prediction to achieve noise reduction in a voice signal which has been mixed with an ambient noise (Signal to Noise (S-N) ratio = about 0dB) is proposed. This noise reduction method which is based on the linear prediction estimates the voice spectrum while ignoring the spectrum of the noise. The performance of the noise reduction method is first examined using the transversal linear predictor filter. However, with this method there is deterioration in the tone quality of the predicted voice due to the low level of the S-N ratio. An additional processing circuit is then proposed so as to adjust the noise reduction circuit with an aim of improving the problem of tone deterioration. Next, we consider a practical application where the effects of round on errors arising from fixed-point computation has to be minimized. This minimization is achieved by using the lattice predictor filter which in comparison to the transversal type, is Down to be less sensitive to the round-off error associated with finite word length operations. Finally, we consider a practical application where noise reduction is necessary. In this noise reduction method, both the voice spectrum and the actual noise spectrum are estimated. Noise reduction is achieved by using the linear predictor filter which includes the control of the predictor filter coefficient’s update.

• Journal of Ocean Engineering and Technology
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• v.25 no.2
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• pp.1-6
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• 2011

Vibration-based Structural Health Monitoring (SHM) systems use field measurements of operational signals, which are distorted by noise from many sources. Reducing this noise allows a more accurate assessment of the original "clean" signal and improves analysis results. The implementation of a noise reduction methodology for the Modal Distribution Method (MDM) is reported here. The spectral subtraction method is a popular broadband noise reduction technique used in speech signal processing. Its basic principle is to subtract the magnitude of the noise from the total noisy signal in the frequency domain. The underlying assumption of the method is that noise is additive and uncorrelated with the signal. In speech signal processing, noise can be measured when there is no signal. In the MDM, however, the magnitude of the noise profile can be estimated only from the magnitude of the Power Spectral Density (PSD) at higher frequencies than the frequency range of the true signal associated with structural vibrations under the additional assumption of white noise. The implementation of the spectral subtraction method to MDM may decrease the energy of the individual mode. In this work, a modification of the spectral subtraction method is introduced that enables the conservation of the energies of individual modes. The main difference is that any (negative) bars with a height below zero after subtraction are set to the absolute value of their height. Both noise reduction methods are implemented in the MDM, and an application example is presented that demonstrates its effectiveness when used with a signal corrupted by noise.

• Journal of Environmental Science International
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• v.25 no.10
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• pp.1407-1415
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• 2016

In this study, we investigated the relationship between noise reduction and the community structure of nine groups of typical plant communities as well as the reduction in noise at different frequencies. The semantic differential method was adopted to explore the perception of noise reduction. The results indicated that there was a significantly positive correlation between noise reduction and coverage, a significantly negative correlation between noise reduction and bifurcate height, and a negative correlation between noise reduction and bare rate. However, there was no significant correlation between noise reduction and height, diameter at breast height, or crown width. The reduction of middle-frequency noise was better than that of low- and high-frequency noise. The indicators "quiet" and "calm" showed that plant communities could reduce the noise perceived by humans. However, overly dense woodland caused nervousness, fear, depression, and other negative effects. Relatively open environments and those with large forest gaps obtained the highest evaluation.

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.1-11
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• 2017

PURPOSES : The purpose of this study is to compare noise reduction quantities between before/after two-layer low noise pavement implementation using equivalent noise level analysis and to analyze the noise reduction effects of the two layer low noise pavement with a statistical method such as the Anderson-Darling Test. METHODS : In order to compare and to analyze noise reduction effects between before/after two-layer low noise pavement implementation, data acquisition as noise levels on a roadside and an apartment rooftop was conducted in the study area. The equivalent noise level was estimated in order to compare noise reduction quantities and the Anderson-Darling Test was carried out for estimating noise reduction effects of the two-layer low noise pavement. RESULTS : The equivalent noise levels of before/after two-layer low noise pavement implementation for the roadside during the daytime are 65.355 dB and 63.520 dB and during the nighttime are 62.463 dB and 59.088 dB. The equivalent noise levels for the apartment rooftop during daytime are 57.301 dB and 59.088 dB and during the nighttime are 54.616 dB and 52.464 dB. Also two-layer low noise pavement decreased the noise reduction effects estimated with the statistical method as the Anderson-Darling test for the roadside during the daytime by around 66.68% and decreased noise reduction effects on the roadside during the nighttime by 0.70%. Moreover it reduced noise reduction effects in the apartment rooftop during the daytime and nighttime by 0% and 96.32%, respectively. CONCLUSIONS : Based on the result of this study, two-layer low noise pavement can positively affect noise reduction during both the daytime and nighttime according to the results of estimating the equivalent noise levels and the Anderson-Darling test.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.1016-1019
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• 2004

The study of the noise reduction of an automobile has been concentrated on the reduction of the automotive engine noise because the engine noise is the major cause of automotive noise. However, many studies of automotive engine noise led to the interest of the noise reduction of the exhaust and intake system. The method of the reduction of the 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, 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. So, in order to this problem, the modified FXLMS algorithm using Moving Bandpass Filter was proposed. In this study, MBPF was implemented and use ANC for automotive intake under revived rapidly accelerated driving conditions and it was verified its performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.1
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• pp.1-7
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• 2008

In this paper, a MSS-NOVO method that combines the HMM composition method with a noise reduction method is proposed for speech recognition in noisy environments. This combined method starts with noise reduction with modified spectral subtraction (MSS) to enhance the input noisy speech, then the noise and voice composition (NOVO) method is applied for making noise adapted models by using the noise in the non-utterance regions of the enhanced noisy speech. In order to evaluate the effectiveness of our proposed method, we compare MSS-NOVO method with other methods, i.e., SS-NOVO, MWF-NOVO. To set up the noisy speech for test, we add White noise to KLE 452 database with different SNRs range from 0dB to 15dB, at 5dB intervals. From the tests, MSS-NOVO method shows average improvement of 66.5% and 13.6% compared with the existing SS-NOVO method and MWF-NOVO method, respectively. Especially our proposed MSS-NOVO method shows a big improvement at low SNRs.

• Speech Sciences
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• v.11 no.1
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• pp.43-52
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• 2004

This paper concerns an effective dual-channel noise reduction method to increase the performance of speech recognition in a car environment. While various single channel methods have already been developed and dual-channel methods have been studied somewhat, their effectiveness in real environments, such as in cars, has not yet been formally proven in terms of achieving acceptable performance level. Our aim is to remedy the low performance of the single and dual-channel noise reduction methods. This paper proposes an effective dual-channel noise reduction method based on a high-pass filter and front-end processing of the eigendecomposition method. We experimented with a real multi-channel car database and compared the results with respect to the microphones arrangements. From the analysis and results, we show that the enhanced eigendecomposition method combined with high-pass filter indeed significantly improve the speech recognition performance under a dual-channel environment.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.9
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• pp.791-796
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• 2010

Installing noise barriers is the most common method for reducing the highway traffic noise to the road side residential area. After the report about edge potential concept of a noise barrier, various types of noise reducing devices(NRDs) called "noise reducers" have been suggested for getting more shielding effect on the top of highway noise barriers. But, it has been doubtful about effect of the NRDs in field because there was no appropriate and unified method to estimate the acoustic performance by using field measurement of the NRDs in Korea. In this study, the authors have considered to setup a practical method to test and estimate the acoustic performance of NRDs. For eliminating the noise reduction effect of the NRDs height itself, the source and measuring points are adjusted as highly as the NRDs height. For the frequency weighting in the estimation of the NRDs effect, the highway noise spectra were measured at asphalt and concrete road side and then averaged for a unit spectral parameter.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.619-628
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• 2012

Noise reduction is an important issue in the field of image processing because image noise lowers the quality of the original pure image. The basic difficulty is that the noise and the signal are not easily distinguished. Simple smoothing is the most basic and important procedure to effectively remove the noise; however, the weakness is that the feature area is simultaneously blurred. In this research, we use ways to measure the degree of noise with respect to the degree of image features and propose a Bayesian noise reduction method based on MAP (maximum a posteriori). Simulation results show that the proposed adaptive noise reduction algorithm using Bayesian MAP provides good performance regardless of the level of noise variance.