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Статті в журналах з теми "Reduction of noise"

Автор: Grafiati

Опубліковано: 9 листопада 2024

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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 (July 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 noises; yet it is more efficient for reducing salt-and-pepper noise. Both numerical and visual comparisons are demonstrated in the experimental simulations. The results show the proposed algorithm can successfully remove impulse noise from images that are contaminated by salt-and-pepper 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 (September 4, 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 is assessed using various metrics, including Mean Squared Error, Signal-to-Noise Ratio, and Peak Signal-to-Noise Ratio. Notably, our findings indicate that SNR and PSNR metrics are especially effective in enhancing the handling of pink and babble noises.

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3

Miura, Grant. "Noise reduction." Nature Chemical Biology 16, no. 2 (January 23, 2020): 106. http://dx.doi.org/10.1038/s41589-020-0464-6.

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4

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 (April 25, 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. We have developed a system that based on the constrained adaptive noise canceller to preserve speech signal from straight ahead and minimize background noise arriving from other directions. This system also uses subband filtering technique to reduce the requirement for computation and enhance the flexibility of the system. The performance of this system is illustrated using simulated and real-world noises. The results show that the developed system can reserve the right ahead speech signal and substantially reject noises from other directions.

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5

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 objective of this research is to conduct a noise reduction experiment for a multi-channel speech enhancement (MCSE) system in stationary and non-stationary environmental noisy situations with varying speech signal SNR levels. The experiments examined the performance of the existing and the proposed MCSE systems for environmental noises in filtering low to high SNRs environmental noises (−10 dB to 20 dB). The experiments were conducted using the AURORA and LibriSpeech datasets, which consist of different types of environmental noises. The existing MCSE (BAV-MCSE) makes use of beamforming, adaptive noise reduction and voice activity detection algorithms (BAV) to filter the noises from speech signals. The proposed MCSE (DWT-CNN-MCSE) system was developed based on discrete wavelet transform (DWT) preprocessing and convolution neural network (CNN) for denoising the input noisy speech signals to improve the performance accuracy. The performance of the existing BAV-MCSE and the proposed DWT-CNN-MCSE were measured using spectrogram analysis and word recognition rate (WRR). It was identified that the existing BAV-MCSE reported the highest WRR at 93.77% for a high SNR (at 20 dB) and 5.64% on average for a low SNR (at −10 dB) for different noises. The proposed DWT-CNN-MCSE system has proven to perform well at a low SNR with WRR of 70.55% and the highest improvement (64.91% WRR) at −10 dB SNR.

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6

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 (September 2, 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 challenges come with a need to develop more effective and efficient noise-reduction techniques. Methods: This study proposes an end-to-end deep learning framework for low-light medical image enhancement. We present a comprehensive deep-learning framework to enhance low-light medical images by integrating Convolutional Neural Networks with denoising autoencoders to build a robust noise reduction model. The CNN extracts the feature from the noisy input images, while the autoencoder does so for the reconstruction of clean images through the encoding of a noisy input in a lower-dimensional representation for the reduction of noise while retaining critical information. Findings: This study validates the proposed model through rigorous quantitative metrics such as peak signal-to-noise ratio and structural similarity index. These metrics are designed to provide a full assessment of image quality concerning noise reduction capability and preservation of details related to structure. Our model improves traditional methods in PSNR by about 5 dB on average and SSIM by 0.15, which means better noise reduction and preservation of image details. A comparative analysis of traditional techniques for noise reduction has been included, pointing out the advantages of deep learning approaches. Experimental results depict significant improvements over previous approaches. For instance, the proposed model reduces the noise level by up to 40% and facilitates clear and sharp images by up to 30%. In terms of quantification, these improvements manifest in a PSNR value of 35 dB and an SSIM score of 0.85 compared to 30 dB and 0.70 using traditional techniques. Furthermore, the study illustrates the training dynamics, feature maps, and evolution of images to present the model's incremental learning process. Novelty: This study's findings validate the proposed model's efficacy in enhancing diagnosis accuracy and improving patient outcomes in medical imaging. Keywords: Low-light medical imaging, Noise reduction, Convolutional Neural Networks, Denoising autoencoders, Medical diagnostics

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7

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 (July 20, 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 Ratio (PSNR) compared to 25.87 dB via the wavelet transform is evidence that the DClet outperforms the wavelet transform for removing noise. The proposed method is robust, which makes it suitable for biomedical applications. It is a candidate for gray and color image enhancement and applicable for compression or efficient coding in which critical sampling might be relevant.

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8

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 line scan/mapping option to a Philips EM420 STEM. The EDAX unit controls the x-y raster and digitizes the detector signal at each pixel. When reading the raw signal from the standard Philips SED detector and amplifier the images were very noisy. This is because the ADC actually samples the signal for a very small portion of the total conversion time and then holds the sampled value for the actual conversion, effectively reading the signal for 0.3 usee out of 170 usec.

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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 (February 9, 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 network (DNN)-based methods, which learn speech reconstruction capabilities with a common neural structure from all training noises, the proposed branchy model obtains greater performance benefits from the specially trained branches of prior known noise interference types. Experimental results show that the proposed branchy DNN model not only preserved better enhanced speech quality and intelligibility in seen noisy environments, but also obtained good generalization in unseen noisy environments.

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10

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 effective information. This method not only made traffic flow which is reduced noise higher goodness of fit, and the prediction accuracy is higher than the traditional threshold methods, thereby we can significantly enhance prediction performance.

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11

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 voice microphone takes both speech and noise, while the noise mike takes only the noise signal. Because of the phase shift of the two signals, simple subtraction in time domain is ineffective. In this paper, we discuss a spectral representation modeling the noise and voice signals. A frequency spectrum based noise cancellation method is proposed and verified in real industrial environment.

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12

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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13

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

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14

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

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15

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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16

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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17

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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18

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

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19

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

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20

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

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21

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

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22

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

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23

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

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24

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 its compliance with regulations.

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25

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

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26

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

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27

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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28

Sehgal, Jai, and Yojna Arora. "Image Noise Reduction with Autoencoder using Tensor Flow." International Journal of Science and Research (IJSR) 9, no. 10 (October 5, 2020): 1626–28. http://dx.doi.org/10.21275/sr201020180621.

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29

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 (October 10, 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 of restoration of an audio signal, which was alternately noisy with one of five types of noise - white, pink, brown, impulse, Gaussian with different power. To achieve the goal, the following tasks were solved: an analysis of the types of noise was carried out and analysis of noise reduction and filtering methods. A generalized model of noise reduction and filtering was developed, and an experiment was planned depending on the type and power of noise. Simulation of the experiment was performed by comparing the parameters of the signal-to-noise ratio before and after the experiment and the peak signal-to-noise ratio in the processed file. The following methods are used: spectral subtraction, filtering based on frequency filters and wavelet transformation.The following results were obtained: depending on the selected noises and algorithms, it was possible to achieve the lowest value of the peak signal-to-noise ratio of 21.52db, and the signal-to-noise ratio increased, which allowed further work with these audio files. The practical significance of this work is the increase in the number of available audio files for further work.Conclusions: the analysis of the obtained results showed that filtering based on frequency filters only worsened the output signal, that is, not only noise, but also useful information is filtered. In all runs, the SNR deteriorates to - 18dB. which is worse than no filtering. Algorithms of spectral subtraction and wavelet transformation improved SNR parameters and output audio files noisy with the most powerful noises in the range of 20dB, which can be considered acceptable for further processing. The results highlight the importance of using denoising and filtering for complex audio processing tasks, particularly neural network training tasks.

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Uzakkyzy, Nurgul, Aisulu Ismailova, Talgatbek Ayazbaev, Zhanar Beldeubayeva, Shynar Kodanova, Balbupe Utenova, Aizhan Satybaldiyeva, and Mira Kaldarova. "Image noise reduction by deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (December 1, 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 images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.</span>

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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 (September 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 quasi-experimental study. The study involved 24 hearing aid users. Each participant was asked to rate the parameters of speech clarity, transient noise loudness, and overall impression for speech stimuli under the algorithm-on and algorithm-off conditions. During the evaluation, three types of stimuli were used: transient noises, speech, and background noises. The transient noises included “knife on a ceramic board,” “mug on a tabletop,” “office door slamming,” “car door slamming,” and “pen tapping on countertop.” The speech sentences used for the test were presented by a male speaker in Mandarin. The background noises included “party noise” and “traffic noise.” All of these sounds were combined into five listening situations: (1) speech only, (2) transient noise only, (3) speech and transient noise, (4) background noise and transient noise, and (5) speech and background noise and transient noise. Results: There was no significant difference on the ratings of speech clarity between the algorithm-on and algorithm-off (t-test, p = 0.103). Further analysis revealed that speech clarity was significant better at 70 dB SLP than 55 dB SPL (p < 0.001). For transient noise loudness: under the algorithm-off condition, the percentages of subjects rating the transient noise to be somewhat soft, appropriate, somewhat loud, and too loud were 0.2, 47.1, 29.6, and 23.1%, respectively. The corresponding percentages under the algorithm-on were 3.0, 72.6, 22.9, and 1.4%, respectively. A significant difference on the ratings of the transient noise loudness was found between the algorithm-on and algorithm-off (t-test, p < 0.001). For overall impression for speech stimuli: under the algorithm-off condition, the percentage of subjects rating the algorithm to be not helpful at all, somewhat helpful, helpful, and very helpful for speech stimuli were 36.5, 20.8, 33.9, and 8.9%, respectively. Under the algorithm-on condition, the corresponding percentages were 35.0, 19.3, 30.7, and 15.0%, respectively. Statistical analysis revealed there was a significant difference on the ratings of overall impression on speech stimuli. The ratings under the algorithm-on condition were significantly more helpful for speech understanding than the ratings under algorithm-off (t-test, p < 0.001). Conclusions: The transient noise reduction strategy appropriately controlled the loudness for most of the transient noises and did not affect the sound quality, which could be beneficial to hearing aid wearers.

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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 (October 2, 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 of noisy signal. The proposed noise reduction approach is validated by some ECG records from MIT-BIH database. Also, the performance of the proposed approach is evaluated graphically using different SNR levels and some standard metrics. The results improve the ability of the proposed approach to reduce noise from the ECG signal with high accuracy in comparison to the existing methods of noise reduction.

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33

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 when the installation is made of metal materials. The article analyzes noise in sanitary facilities, describes the methods of reducing noise in places where noise occurs. The given results of noise tests of sanitary facilities and workplaces in one of the municipal companies of the city of the Subcarpathian province. 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 when the installation is made of metal materials. It has been shown that the main causes of noise in plumbing systems can be rigid pipe fittings. Noises in the sewage system are related to the outflow of used water in vertical and horizontal sections. The phenomenon most often occurs in places connecting vertical pipes with horizontal pipes, as well as the use of too small diameters of pipes. Material noise reduction in sewage systems can be ensured thanks to a properly designed system of fastening pipes to fixed elements. An important step is to use appropriate sound insulation to stop unwanted sounds. The reason for noise in the air-conditioning and ventilation system are changes in the velocity of the flowing air mass and the occurrence of turbulences during the change of the air flow direction. This causes the ducts to resonate and the air flow noise through the diffusers. The most common noise problem in the central heating installation is the use of a solid fuel boiler, the maintenance work of the device is a problem, as it requires cleaning the furnace, which is related to the noise that is transmitted through the installation pipes to the rooms. Based on the analysis of workstations at the municipal company plant, it has been shown that the highest conformity deviation level is in the drying room in the position of a machine and device operator

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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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35

Scollie, Susan, Charla Levy, Nazanin Pourmand, Parvaneh Abbasalipour, Marlene Bagatto, Frances Richert, Shane Moodie, Jeff Crukley, and Vijay Parsa. "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 (March 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 noise reduction processors in hearing aids and the verification of these processors and (2) determine whether these differences are perceived by 13 children who have hearing loss. Finally, we aimed to develop a verification protocol for use in pediatric clinical practice. Study Sample: A set of hearing aids was tested using both clinically available test systems and a reference system, so that the impacts of noise reduction signal processing in hearing aids could be characterized for speech in a variety of background noises. A second set of hearing aids was tested across a range of audiograms and across two clinical verification systems to characterize the variance in clinical verification measurements. Finally, a set of hearing aid recordings that varied by type of noise reduction was rated for sound quality by children with hearing loss. Results: Significant variation across makes and models of hearing aids was observed in both the speed of noise reduction activation and the magnitude of noise reduction. Reference measures indicate that noise-only testing may overestimate noise reduction magnitude compared to speech-in-noise testing. Variation across clinical test signals was also observed, indicating that some test signals may be more successful than others for characterization of hearing aid noise reduction. Children provided different sound quality ratings across hearing aids, and for one hearing aid rated the sound quality as higher with the noise reduction system activated. Conclusions: Implications for clinical verification systems may be that greater standardization and the use of speech-in-noise test signals may improve the quality and consistency of noise reduction verification cross clinics. A suggested clinical protocol for verification of noise management in children’s hearing aids is suggested.

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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-reduction technique. We show that the Wiener optimal linear filter can indeed reduce the level of noise. Most importantly, we prove that the output SNR is always greater than, or at least equal to the input SNR, which reveals that the Wiener optimal linear filtering technique is indeed able to make noisy speech signals cleaner.

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37

Altinoz, Tolga. "A Noise Reduction Method for Semi-Noisy Multiobjective Optimization Problems." International Conference on Scientific and Innovative Studies 1, no. 1 (May 4, 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 case, noise of the objective function may be extracted. In this case, the noise of other objective functions can be reduced by using the statistical properties of the known noise signal. The aim of this study is to reduce the noise in the objective functions as explained by using the statistical properties of the noise. For this purpose, two optimization algorithms and eight test problems will be used. In addition, statistical properties will be obtained from the data recorded with different window sizes.

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38

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 (June 30, 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, pulse noise, and broadband noise related problems. There are many kinds of broadband noise, which may include heat noise, wind noise, quantization noise, and all kinds of random noise such as white noise and pink noise. In acoustics applications, noise from the surrounding environment severely reduces the quality ofspeech and audio signals. Therefore, basic linear are used to denoise the audio signals and enhance speech and audio signal quality. Our main objective is to reduce noise from system which is heavily dependent on the specific context and application. As, we want to increase the intelligibility or improve the overall speech perception quality. Such as SNR, PSNR, MSE and the Time to reduce the noise for noisy signals for removing noise.

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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 (March 29, 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 using a computational fluid dynamics model. The simulation and experimental study were done with different louvred noise barrier setup: change of louvre blade angle and sound-absorbing material thickness. The results showed potential for future development for the field testing. Sound attenuation was highest in 2500 Hz and 3150 Hz octave frequency bands. Depending on the louvred barrier setup, sound attenuation was up to 28 dB(A) in mentioned frequency bands. The equivalent sound pressure level reduced up to 17 dB(A). The results showed that an increase in the louvre blade angle increases sound attenuation and increases airflow resistance.

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40

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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41

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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42

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

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43

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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44

Segal, Alexander. "Outdoor‐indoor noise reduction and outdoor noise." Journal of the Acoustical Society of America 88, S1 (November 1990): S159. http://dx.doi.org/10.1121/1.2028700.

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45

KOBA, Yosuke, Shinya KIJIMOTO, Koichi MATSUDA, Ikuma IKEDA, and Keitaro KAGEYAMA. "363 Noise Reduction Using Active Noise Shielding." Proceedings of the Dynamics & Design Conference 2009 (2009): _363–1_—_363–5_. http://dx.doi.org/10.1299/jsmedmc.2009._363-1_.

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46

Hecht, Markus, Thilo Hanisch, Anastasia Ullrich, Jean-Marc Wunderli, Jonas Jäggi, Fredy Fischer, Sandro Ferrari, and Franz Kuster. "Effects of Locomotive Noise Reduction to Freight Train Noise in Switzerland and Europe." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 8 (November 30, 2023): 586–92. http://dx.doi.org/10.3397/in_2023_0097.

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As freight trains are very frequently operated at night there is still big concern about freight train noise in Europe. Freight wagons in Switzerland and Germany are now all equipped with composite brake blocks instead of cast iron brake blocks which results in significant noise reductions. Using data from the Swiss noise monitoring stations the contribution of different locomotives to the overall train noise was evaluated. For locomotive types without cast iron brake blocks the year of construction is not related to the noise behavior. Newer types are not in general less noisy than old ones. The 30 years old type Lok 2000 is significantly less noisy than all other locomotive types, even if operated by different companies. European regulations concerning type approval allow higher noise levels for locomotives than for wagons. Therefore, the question was addressed how the overall noise can be reduced by noise abatement at the locomotive.

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47

B, Baseena. "Traffic Noise Pollution Modelling and Noise Reduction Asphalt Pavement." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5554–58. http://dx.doi.org/10.22214/ijraset.2023.52874.

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Abstract: Noise pollution leads to the lack of concentration of people, as a result, they will be finding longer time for completing the work than that which would be done in a quiet environment and in addition they feel more tired in the noisy area. Road traffic is a complete system which wide comprises of varieties of road user, vehicle and environment interact the congestion of road intersections is due to the motorization from and increase in single occupancy vehicle. This study is carried out to understand the noise traffic pattern of Ottapalam city. So proper planning of the road should be done and proper laws should be available. A study area consisting of two locations, a busy urban street of Ottapalam city, with high population density was selected for the study to determine the impact of the noise pollution at residential zone.

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Dr. S.S.Gawade, Dr S. S. Gawade, M. B. Mandale M. B. Mandale, and B. P. Karamkar B. P. Karamkar. "Experimental Determination of Noise Level Characteristics and Noise Reduction for Industrial Fan Using CFD Software." Paripex - Indian Journal Of Research 3, no. 7 (January 1, 2012): 1–3. http://dx.doi.org/10.15373/22501991/july2014/27.

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Dr. P, Ratna Babu, and Lokaiah P. "An effective noise reduction technique for class imbalance classification." International Journal of Psychosocial Rehabilitation 24, no. 04 (February 28, 2020): 985–90. http://dx.doi.org/10.37200/ijpr/v24i4/pr201070.

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50

Xing, Yannan, Weijie Ke, Gaetano Di Caterina, and John Soraghan. "Noise Reduction Using Neural Lateral Inhibition for Speech Enhancement." International Journal of Machine Learning and Computing 11, no. 5 (September 2021): 357–61. http://dx.doi.org/10.18178/ijmlc.2021.11.5.1061.

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