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Artículos de revistas sobre el tema "Reduction of noise"

Autor: Grafiati

Publicado: 9 de noviembre de 2024

Última modificación: 31 de julio de 2025

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1

Farshi, Taymaz Rahkar. "Image Noise Reduction Method Based on Compatibility with Adjacent Pixels." International Journal of Image and Graphics 17, no. 03 (2017): 1750014. http://dx.doi.org/10.1142/s0219467817500140.

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This paper proposes an efficient noise reduction method for gray and color images that are contaminated by salt-and-pepper noise. In the proposed method, the pixels that are more compatible with adjacent pixels are replaced with target (noisy) pixels. The algorithm is applied on noisy Lena and Mansion images that are contaminated by salt-and-pepper noise with 0.1 and 0.2 noise intensities. Although this method is developed for reducing noise from the images that are contaminated by salt-and-pepper noise, it can also reduce the noise from the images that are contaminated by other types of noise

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2

C, Shraddha, Chayadevi M L, Anusuya M A, and Vani H Y. "Enhancing Noise Reduction with Bionic Wavelet and Adaptive Filtering." Inteligencia Artificial 27, no. 74 (2024): 214–26. http://dx.doi.org/10.4114/intartif.vol27iss74pp214-226.

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Speech signals often contain different forms of background and environmental noise. For the development of an efficient speech recognition system, it is essential to preprocess noisy speech signals to reduce the impact of these disturbances. Notably, prior research has paid limited attention to pink and babble noises. This gap in knowledge inspired us to develop and implement hybrid algorithms tailored to handle these specific noise types. We introduce a hybrid method that combines the Bionic Wavelet transform with Adaptive Filtering to enhance signal strength. The performance of this method i

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3

Pradeep, M. Patil, A.S Soumyashree, Subhramanya Chaitanaya A.K, and K. Harish. "Audio Noise Reduction Using Spectral Subtraction." Advancement and Research in Instrumentation Engineering 7, no. 3 (2024): 87–97. https://doi.org/10.5281/zenodo.13789207.

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<em>Spectral subtraction is used in this research as a method to remove noise from noisy speech signals by operating in the frequency domain. This method leverages the distinct spectral characteristics of noise and the desired audio signal to attenuate unwanted noise while preserving the integrity of the original content and consists of computing the spectrum of the noisy speech using the Fast Fourier Transform (FFT) and subtracting the average magnitude of the noise spectrum from the noisy speech spectrum. We applied spectral subtraction to the speech signal “Real graph”. A digita

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4

Miura, Grant. "Noise reduction." Nature Chemical Biology 16, no. 2 (2020): 106. http://dx.doi.org/10.1038/s41589-020-0464-6.

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5

TAI, CHENG-CHI, CHIH-HSING CHANG, CHUAN-CHING TAN, TSUNG-WEN HUANG, and CHING-CHAU SU. "ADAPTIVE BEAMFORMER WITH COMBINATION OF SUBBAND FILTERING FOR HEARING-AID SYSTEMS BACKGROUND NOISE REDUCTION." Biomedical Engineering: Applications, Basis and Communications 14, no. 02 (2002): 55–66. http://dx.doi.org/10.4015/s1016237202000097.

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In this paper, we present a noise reduction technique for hearing-aid systems. The proposed algorithm adopted adaptive beamformer with combination of subband filtering technique. The structure of conventional hearing aids is relatively simple. They amplify ambient sounds that include speech signal as well as noise. Because noise and human speech signal are amplified at the same time, hearing-aid users can't clearly hear speech signal in noisy environment. The direction of sound can be used to discriminate speech signal from noise by combining adaptive noise canceller and adaptive beamformer. W

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6

Cherukuru, Pavani, and Mumtaz Begum Mustafa. "CNN-based noise reduction for multi-channel speech enhancement system with discrete wavelet transform (DWT) preprocessing." PeerJ Computer Science 10 (February 28, 2024): e1901. http://dx.doi.org/10.7717/peerj-cs.1901.

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Speech enhancement algorithms are applied in multiple levels of enhancement to improve the quality of speech signals under noisy environments known as multi-channel speech enhancement (MCSE) systems. Numerous existing algorithms are used to filter noise in speech enhancement systems, which are typically employed as a pre-processor to reduce noise and improve speech quality. They may, however, be limited in performing well under low signal-to-noise ratio (SNR) situations. The speech devices are exposed to all kinds of environmental noises which may go up to a high-level frequency of noises. The

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7

Sundarrajan, M., Mani Deepak Choudhry, J. Biju, S. Krishnakumar, and K. Rajeshkumar. "Enhancing Low-Light Medical Imaging through Deep Learning-Based Noise Reduction Techniques." Indian Journal Of Science And Technology 17, no. 34 (2024): 3567–79. http://dx.doi.org/10.17485/ijst/v17i34.2489.

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Background/ Objectives: Low-light medical imaging is highly challenging in clinical diagnostics due to increased noise levels that mask or obscure important anatomical details. In this respect, conventional noise reduction methods such as Gaussian filtering and median filtering usually lead to a trade-off between noise suppression and the preservation of important features in an image, thus resulting in poor-quality images. More advanced wavelet-based denoising and Non-Local Means methods exhibit superior noise reduction but remain computationally intensive and introduce artifacts. These chall

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8

Cao, Jiyuan, Yanwen Wang, Weixiong Zhu, and Yihe Zhang. "A Noise Reduction Algorithm for White Noise and Periodic Narrowband Interference Noise in Partial Discharge Signals." Applied Sciences 15, no. 4 (2025): 1760. https://doi.org/10.3390/app15041760.

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Partial discharge (PD) detection plays an important role in online condition monitoring of electrical equipment and power cables. However, the noise of PD measurement will significantly reduce the performance of the detection algorithm. In this paper, we focus on the study of a PD noise reduction algorithm based on improved singular value decomposition (SVD) and multivariate variational mode decomposition (MVMD) for white Gaussian noise (WGN) and periodic narrowband interference signal noise. The specific noise reduction algorithm is divided into three noise reduction processes: The first nois

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9

Zhang, Lu, Mingjiang Wang, Qiquan Zhang, and Ming Liu. "Environmental Attention-Guided Branchy Neural Network for Speech Enhancement." Applied Sciences 10, no. 3 (2020): 1167. http://dx.doi.org/10.3390/app10031167.

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The performance of speech enhancement algorithms can be further improved by considering the application scenarios of speech products. In this paper, we propose an attention-based branchy neural network framework by incorporating the prior environmental information for noise reduction. In the whole denoising framework, first, an environment classification network is trained to distinguish the noise type of each noisy speech frame. Guided by this classification network, the denoising network gradually learns respective noise reduction abilities in different branches. Unlike most deep neural netw

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10

Nurgul, Uzakkyzy, Ismailova Aisulu, Ayazbaev Talgatbek, et al. "Image noise reduction by deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6855–61. https://doi.org/10.11591/ijece.v13i6.pp6855-6861.

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Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In

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11

Sagun, Allan Godfrey S., Celso B. Co, and III Bartolome T. Tanguilig. "Active Noise Reduction Using Wavelet Transform." Innovatus 3, no. 1 (2020): 10–17. https://doi.org/10.5281/zenodo.5173065.

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This study focuses on Active Noise Reduction Using Discrete Wavelet Transform. Noises emanating from electric drill, pneumatic system, bearing of rotating motors and many others, can be classified as non-stationary signals. These examples of noises typically coming from machine shops contributes to the interference of desired signals. The objective of this study is to provide a system that simulates how to reduce unwanted signal generated by electrical system noise. In order to perform this task, a recorded voice and noise were processed by wavelet transform to get the wavelet coefficients of

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12

Czap, Laszlo, and Judit Pinter. "Noise Reduction in Voice Controlled Logistic Systems." Applied Mechanics and Materials 309 (February 2013): 260–67. http://dx.doi.org/10.4028/www.scientific.net/amm.309.260.

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The most comfortable way of human communication is speech, which is a possible channel of human-machine interface as well. Moreover, a voice driven system can be controlled with busy hands. Performance of a speech recognition system is highly decayed by presence of noise. Logistic systems typically work in noisy environment, so noise reduction is crucial in industrial speech processing systems. Traditional noise reduction procedures (e.g. Wiener and Kalman filters) are effective on stationary or Gaussian noise. The noise of a real workplace can be captured by an additional microphone: The voic

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13

Li, Rui Xian. "Multi-Scale Noise Reduction Based Wavelet." Applied Mechanics and Materials 484-485 (January 2014): 896–901. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.896.

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Traffic flow sampled data is noisy and chaotic time series. Complex noise component affects the traffic flow predictability. In this paper，it used multi-scale noise reduction based on wavelet/wavelet packet to shield the traffic flow’s noise components interference in deterministic component.It aimed at the contradiction between similarity and predictability in traffic flow noise reduction process, then proposed multi-state threshold method. Experimental results show that, compare with the traditional threshold value method, the threshold method can more effectively extract the traffic flow’s

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14

Anaraki, Marjan Sedighi, Fangyan Dong, Hajime Nobuhara, and Kaoru Hirota. "Dyadic Curvelet Transform (DClet) for Image Noise Reduction." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (2007): 641–47. http://dx.doi.org/10.20965/jaciii.2007.p0641.

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Dyadic Curvelet transform (DClet) is proposed as a tool for image processing and computer vision. It is an extended curvelet transform that solves the problem of conventional curvelet, of decomposition into components at different scales. It provides simplicity, dyadic scales, and absence of redundancy for analysis and synthesis objects with discontinuities along curves, i.e., edges via directional basis functions. The performance of the proposed method is evaluated by removing Gaussian, Speckles, and Random noises from different noisy standard images. Average 26.71 dB Peak Signal to Noise Rat

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15

Uzakkyzy, Nurgul, Aisulu Ismailova, Talgatbek Ayazbaev, et al. "Image noise reduction by deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6855. http://dx.doi.org/10.11591/ijece.v13i6.pp6855-6861.

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<span lang="EN-US">Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the i

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16

Christenson, K. K. "Noise reduction in digitized images." Proceedings, annual meeting, Electron Microscopy Society of America 44 (August 1986): 878–79. http://dx.doi.org/10.1017/s042482010014573x.

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In order to produce useable images at high scan rates, the detectors on modern electron microscopes often have a very high bandwidth (short response time to signal changes). This bandwidth makes the signal noisey; the signal has large fluctuations about the mean. Because the film integrates the signal this noise is not a problem when photographing an image. But it causes an annoying blurring of the trace In slow y-modulated line scans and can result in large errors if the signal if measured with a fast analog to digital converter (ADC).We interfaced an EDAX 9100/70 x-ray analyzer with a PV9242

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17

Барковська, Олеся Юріївна, and Антон Олегович Гаврашенко. "Research of the impact of noise reduction methods on the quality of audio signal recovery." Інформаційно-керуючі системи на залізничному транспорті 29, no. 3 (2024): 57–65. http://dx.doi.org/10.18664/ikszt.v29i3.313606.

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The subject of the study is the analysis of various filtering algorithms for the quality of the resulting audio files. The importance of audio line filtering has grown significantly in recent years due to its key role in a variety of applications such as speech reduction and artificial intelligence. Taking into account the growing demand for solving problems related to speech recognition, the processing of audio series becomes important for determining the accuracy and efficiency of the obtained solution.The purpose of the work is to study the impact of noise suppression methods on the quality

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18

KAGEYAMA, Keitaro, Shinya KIJIMOTO, Koichi MATSUDA, Yousuke KOBA, and Ikuma IKEDA. "Active Noise Reduction of Impact Noise." Transactions of the Japan Society of Mechanical Engineers Series C 74, no. 748 (2008): 2904–9. http://dx.doi.org/10.1299/kikaic.74.2904.

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19

Jaenul, Ariep, Shahad Alyousif, Ali Amer Ahmed Alrawi, and Samer K. Salih. "Robust Approach of De-noising ECG Signal Using Multi-Resolution Wavelet Transform." International Journal of Engineering & Technology 7, no. 4.11 (2018): 5. http://dx.doi.org/10.14419/ijet.v7i4.11.20678.

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The ECG signal expresses the behavior of human heart against time. The analysis of this signal performs great information for diagnosing different cardiac diseases. In other hand, the ECG signal used for analyzing must be clean from any type of noises that corrupted it by the external environment. In this paper, a new approach of ECG signal noise reduction is proposed to minimize noise from all parts of ECG signal and maintains main characteristics of ECG signal with lowest changes. The new approach applies simple scaling down operation on the detail resolution in the wavelet transform space o

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20

Adlersberg, Shabtai. "Noise reduction system." Journal of the Acoustical Society of America 92, no. 4 (1992): 2283. http://dx.doi.org/10.1121/1.405165.

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21

Brocker, J., U. Parlitz, and M. Ogorzalek. "Nonlinear noise reduction." Proceedings of the IEEE 90, no. 5 (2002): 898–918. http://dx.doi.org/10.1109/jproc.2002.1015013.

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22

SASAOKA, Naoto, and Yoshio ITOH. "Noise Reduction Technique." IEICE ESS FUNDAMENTALS REVIEW 5, no. 2 (2011): 136–45. http://dx.doi.org/10.1587/essfr.5.136.

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23

Yang, Jin, and Andrew Sendyk. "Noise-reduction system." Journal of the Acoustical Society of America 99, no. 1 (1996): 18. http://dx.doi.org/10.1121/1.414487.

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24

Nadim, Mohammed. "Active noise reduction." Journal of the Acoustical Society of America 100, no. 3 (1996): 1282. http://dx.doi.org/10.1121/1.416049.

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25

Pedley, Mark, and William J. Fitzerald. "Flow noise reduction." Journal of the Acoustical Society of America 90, no. 2 (1991): 1211. http://dx.doi.org/10.1121/1.401996.

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26

Twiney, Robert C., and Anthony J. Salloway. "Noise reduction system." Journal of the Acoustical Society of America 91, no. 2 (1992): 1192. http://dx.doi.org/10.1121/1.402619.

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27

Donahue, Lawrence E. "Valve noise reduction." Journal of the Acoustical Society of America 91, no. 4 (1992): 2303. http://dx.doi.org/10.1121/1.403617.

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28

Powell, Ronald L. "Noise reduction method." Journal of the Acoustical Society of America 92, no. 2 (1992): 1197. http://dx.doi.org/10.1121/1.404022.

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29

Mueller, H. Gustav, and Todd A. Ricketts. "Digital noise reduction." Hearing Journal 58, no. 1 (2005): 10–18. http://dx.doi.org/10.1097/01.hj.0000324427.07288.f4.

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30

Cole, William A. "Noise reduction system." Journal of the Acoustical Society of America 85, no. 2 (1989): 984. http://dx.doi.org/10.1121/1.397520.

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31

Nesterov, Nikita, and Aleksei Bykov. "Locomotive Noise Reduction." MATEC Web of Conferences 320 (2020): 00013. http://dx.doi.org/10.1051/matecconf/202032000013.

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This paper presents the results of the investigation of the noise characteristics of the dual-voltage mainline electric locomotive. The study identified noise levels generated by the locomotive equipment that violate regulations. The analysis of points, where the noise is above regulations, indicated the primary source of the elevated noise is the operation of the traction transformer. We proposed installing a noise barrier below the locomotive body to reduce noise and bring the locomotive in compliance with the noise regulations. This solution allowed reducing the locomotive noise and ensure

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32

HANSEN, JAMES A., and LEONARD A. SMITH. "Probabilistic noise reduction." Tellus A 53, no. 5 (2001): 585–98. http://dx.doi.org/10.1034/j.1600-0870.2001.00118.x.

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33

Hansen, James A., and Leonard A. Smith. "Probabilistic noise reduction." Tellus A: Dynamic Meteorology and Oceanography 53, no. 5 (2001): 585–98. http://dx.doi.org/10.3402/tellusa.v53i5.12226.

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34

Ohashi, Toshihiko. "Noise reduction apparatus." Journal of the Acoustical Society of America 120, no. 3 (2006): 1169. http://dx.doi.org/10.1121/1.2355969.

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35

Liu, HaiHong, Hua Zhang, Ruth A. Bentler, Demin Han, and Luo Zhang. "Evaluation of a Transient Noise Reduction Strategy for Hearing Aids." Journal of the American Academy of Audiology 23, no. 08 (2012): 606–15. http://dx.doi.org/10.3766/jaaa.23.8.4.

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Background: Transient noise can be disruptive for people wearing hearing aids. Ideally, the transient noise should be detected and controlled by the signal processor without disrupting speech and other intended input signals. A technology for detecting and controlling transient noises in hearing aids was evaluated in this study. Purpose: The purpose of this study was to evaluate the effectiveness of a transient noise reduction strategy on various transient noises and to determine whether the strategy has a negative impact on sound quality of intended speech inputs. Research Design: This was a

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36

Galyna Kalda, Tomasz Paździorny, and Katarzyna Pietrucha-Urbanik. "NOISE ANALYSIS AND REDUCTION METHODS IN SANITATION FACILITIES AND EQUIPMENT." Journal of Civil Engineering, Environment and Architecture 69 (July 22, 2022): 17–26. http://dx.doi.org/10.7862/rb.2022.2.

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The article presents an analysis of noise in sanitary devices, and the described methods of reducing noise in places where noise occurs. The given results of noise tests of sanitary facilities and workplaces concerns one of the municipal company. Described sources of noise in water supply, sewage as well as ventilation and air conditioning systems. It has been shown that the noises occurring in residential buildings may be caused by excessively high pressure inside the installation, where, for example, when closing the valve, a water hammer phenomenon arises, causing audible noises, especially

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37

Scollie, Susan, Charla Levy, Nazanin Pourmand, et al. "Fitting Noise Management Signal Processing Applying the American Academy of Audiology Pediatric Amplification Guideline: Verification Protocols." Journal of the American Academy of Audiology 27, no. 03 (2016): 237–51. http://dx.doi.org/10.3766/jaaa.15060.

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Background: Although guidelines for fitting hearing aids for children are well developed and have strong basis in evidence, specific protocols for fitting and verifying some technologies are not always available. One such technology is noise management in children’s hearing aids. Children are frequently in high-level and/or noisy environments, and many options for noise management exist in modern hearing aids. Verification protocols are needed to define specific test signals and levels for use in clinical practice. Purpose: This work aims to (1) describe the variation in different brands of no

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38

Vijayan Sukanya Sundaresh, Lekshmi. "Ground Bounce Noise Reduction Using Power Gating Techniques." International Journal of Scientific Engineering and Research 2, no. 7 (2014): 20–26. https://doi.org/10.70729/j2013329.

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39

Sehgal, Jai, and Yojna Arora. "Image Noise Reduction with Autoencoder using Tensor Flow." International Journal of Science and Research (IJSR) 9, no. 10 (2020): 1626–28. http://dx.doi.org/10.21275/sr201020180621.

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40

Belov, Sergey Valerevich, Sergey Sergeevich Katunin, Ivan Yurevich Kuchin, and Mikhail Fedorovich Rudenko. "Testing a hybrid noise reduction technique effectiveness in a speech signal for a video conferencing system." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2025, no. 1 (2025): 93–102. https://doi.org/10.24143/2072-9502-2025-1-93-102.

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The issue of audio signal quality during video conferences is considered. The effect of noise on the quality and intelligibility of the speech signal is described. The analysis of the noise reduction process in the audio signal in real time is carried out. The main approaches to solving the problem of noise reduction in real time are highlighted, namely the approach with recognition and elimination of noise and the approach with voice recognition and elimination of sounds different from the speech signal. An algorithm for intelligent noise reduction using artificial intelligence methods has be

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41

Xue, Lin, and Yun Shan Hou. "Performance Analysis of the Wiener Optimal Filtering Noise-Reduction Technique." Advanced Materials Research 816-817 (September 2013): 484–87. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.484.

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Noise reduction, which aims at extracting the clean speech from noisy observations, has attracted a considerable amount of research attention over the past several decades. Although many methods have been developed to achieve the goal of noise reduction and signal-to-noise ratio (SNR) improvement, there has been remarkably little theoretical justification of their performance due to the difficulty in quantizing the combinatorial effect between noise reduction and speech distortion. This paper attempts to provide a theoretical analysis on the performance of the Wiener optimal filtering noise-re

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42

Nimmagadda, Padmaja, Kondru Ayyappa Swamy, Samuda Prathima, Sushma Chintha, and Zachariah Callottu Alex. "Short-term uncleaned signal to noise threshold ratio based endto-end time domain speech enhancement in digital hearing aids." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (2022): 131–38. https://doi.org/10.11591/ijeecs.v27.i1.pp131-138.

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This paper presents the improvements in the combined solution for the noise estimation and the speech enhancement in digital hearing aids in time domain. This study focuses on the single channel statistical temporal speech enhancement using adaptive Wiener filtering. In this technique, the noise is updated based on the short-term uncleaned signal to noise threshold ratio (ST-USNTR) of the frame. It works best if and only if the background noise level is low compared to that of speech of interest. We considered the time domain algorithms in order to consider the time varying nature of speech si

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43

Altinoz, Tolga. "A Noise Reduction Method for Semi-Noisy Multiobjective Optimization Problems." International Conference on Scientific and Innovative Studies 1, no. 1 (2023): 14–19. http://dx.doi.org/10.59287/icsis.568.

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In engineering problems, variables such as temperature, speed, location are noisy variables that are included in the system, and they become objective function variables. Because these variables are noisy, the objective functions are also noisy. Because there is more than one objective in multi-objective optimization problems, these variables may not affect each objective. Not all variables may be included for each objective function as variables. Therefore, in multi-objective optimization problems, it may be known to know both the noisy and noiseless states of one or more purposes. In this ca

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44

Rani, Shalu. "Review: Audio Noise Reduction Using Filters and Discrete Wavelet Transformation." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 2, no. 6 (2015): 17–21. http://dx.doi.org/10.53555/nneee.v2i6.192.

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Audio noise reduction using filters and Discrete Wavelet Transformation” our applications include noise propagation problem in industrial air handling systems, noise in aircrafts and tonal noise from electric power, as well as isolation of vibration from which noise is one kind of sound that is unexpected or undesired . The noise related problem can be divided into non-additive noise and additive noise. The non-additive noise includes multiplier noise and convolution noise, which can be transformed into additive noise throughhomomorphism transform. The additive noise includes periodical noise,

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45

Muhammad Basharat, Muhammad Basharat, Ming Ding Ming Ding, Yang Li Yang Li, Hongwei Cai Hongwei Cai, and Jiancheng Fang Jiancheng Fang. "Noise reduction and signal to noise ratio improvement in magneto-optical polarization rotation measurement." Chinese Optics Letters 16, no. 8 (2018): 081201. http://dx.doi.org/10.3788/col201816.081201.

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46

Astrauskas, Tomas, Pranas Baltrėnas, Tomas Januševičius, and Raimondas Grubliauskas. "Louvred Noise Barrier for Traffic Noise Reduction." Baltic Journal of Road and Bridge Engineering 16, no. 1 (2021): 140–54. http://dx.doi.org/10.7250/bjrbe.2021-16.519.

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Environmental issues near roads become more and more important in our society daily life. One of the most critical environmental issues is traffic noise. The present paper study louvred noise barrier designed by authors. The louvred noise barrier provides sound attenuation while allowing airflow and sunlight through it. Since the airflow resistance of the barrier is low, it requires a shallow foundation compared to conventional noise barriers. The sound attenuation performance of the louvred noise barrier was tested experimentally in a sound transmission chamber. Airflow resistance simulated u

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Kageyama, Keitaro, Sinya Kijimoto, Koichi Matuda, Yosuke Koba, and Ikuma Ikeda. "F15 Active Noise Reduction for impact noise." Proceedings of Conference of Kyushu Branch 2008.61 (2008): 177–78. http://dx.doi.org/10.1299/jsmekyushu.2008.61.177.

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KAGEYAMA, Keitaro, Shinya KIJIMOTO, Koichi MATSUDA, Yosuke KOBA, and Ikuma IKEDA. "133 Active Noise Reduction of impact noise." Proceedings of the Symposium on Environmental Engineering 2008.18 (2008): 148–51. http://dx.doi.org/10.1299/jsmeenv.2008.18.148.

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Brown, Charles H. "Motorcycle Helmet Noise and Active Noise Reduction." Open Acoustics Journal 4, no. 1 (2011): 14–24. http://dx.doi.org/10.2174/1874837601104010014.

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Zwicker, Eberhard. "Meaningful Noise Measurement and Effective Noise Reduction." Noise Control Engineering Journal 29, no. 3 (1987): 66. http://dx.doi.org/10.3397/1.2827693.

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