Relevant bibliographies by topics / Reduction of noise

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Reduction of noise'

Author: Grafiati
Published: 9 November 2024
Last updated: 19 July 2025

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Journal articles on the topic "Reduction of noise"

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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 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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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 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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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 &ldquo;Real graph&rdquo;. A digital audio recorder system embedded in a personal computer was used to sample the speech signal &ldquo;Real graph&rdquo; to which we digitally added vacuum cleaner noise. The noise removal algorithm was implemented using MATLAB software by storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse Fast Fourier Transform (IFFT). The performance of the algorithm was evaluated by calculating the Speech to Noise Ratio (SNR). Frame averaging was introduced as an optional technique that could improve the SNR. Seventeen different configurations with various lengths of the Hanning time windows various degrees of data buffers overlapping, and various numbers of frames to be averaged were investigated in view of improving the SNR. Results showed that using one-fourth of overlapped data buffers with 128 points Hanning windows and no frames averaging leads to the best performance in removing noise from the noisy speech. The process begins with noise estimation, typically derived from segments of the signal where only noise is present. This noise profile is subtracted from the noisy signal's magnitude spectrum during the frequency analysis, followed by reconstruction of the audio signal through inverse transformation. Despite its effectiveness, spectral subtraction can introduce artifacts, such as residual "musical" noise, especially in non-stationary noise environments. Various enhancements, including adaptive techniques and multi-band spectral subtraction, have been developed to address these challenges. Spectral subtraction is widely applied in fields such as telecommunications, hearing aids, and audio restoration, demonstrating its importance in improving audio quality in noisy environments.</em>
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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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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. 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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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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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 noise reduction completes the suppression of narrowband interference in the noisy PD signal by the SVD algorithm with the guidance signal. The guidance signal is composed of a sinusoidal signal of the accurately estimated narrowband interference frequency component, and the amplitude is twice the maximum amplitude of the noisy PD signal. The second noise reduction decomposes the noisy PD signal after filtering the narrowband interference signal into k optimal intrinsic mode function by the MVMD after parameter optimization. By calculating the kurtosis value of each intrinsic mode function, it is determined whether it is the PD dominant component or the noise dominant component, and the noise dominant component is subjected to 3σ filtering to obtain the reconstructed PD signal. The third noise reduction uses a new wavelet threshold algorithm to denoise the reconstructed PD signal to obtain the denoised PD signal. The overall noise reduction algorithm proposed in this paper is compared with some existing methods. The results show that this method has a good effect on reducing the noise of PD signals measured in simulation and field.
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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 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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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 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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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 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 quali
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Dissertations / Theses on the topic "Reduction of noise"

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Robert, Rene Jean. "Measuring noise level reduction using an artificial noise source." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54480.

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Buildings located near airports may be subjected to significant noise levels due to aircraft flyovers. Aircraft noise is particularly annoying when compared to other traffic noises due to its intermittent nature. While noise control is typically performed at the source, sound insulation programs are in place to improve the acoustic performance of a residence affected by the flyovers. Noise Level Reduction (NLR) is a common metric used in the United States to determine whether a residence qualifies for such programs. Sound insulation programs are available to houses that have an indoor Day Night Average Sound Level (DNL) greater than 45 dBA. NLR is a single-number metric used to quantify the ability for a building or building element to reduce the transmission of external sound pressure levels generated by aircraft. In addition to determining whether a residence qualifies, NLR can be used to quantify the effectiveness of the modifications performed as a result of the sound insulation program. NLR measurements with a loudspeaker offer an alternative method to those performed with aircraft flyovers, offering flexibility to the consultants that perform these measurements in the field. The purpose of this research was to better understand and improve the loudspeaker test for measuring NLR, providing a resource to the aircraft noise industry. Testing was completed on a "test house" that was constructed on campus with construction methods typical of a mixed-humid climate. The angular dependency, repeatability, and reproducibility of NLR, among other factors, were evaluated with field measurements. Significant NLR variations were observed with changes in lateral and vertical angles of incidence.
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Kalyoncu, Ozden. "Noise Reduction In Time-frequency Domain." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608768/index.pdf.

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In this thesis work, time-frequency filtering of nonstationary signals in noise using Wigner-Ville Distribution is investigated. Continuous-time, discrete-time and discrete Wigner Ville Distribution definitions, their relations, and properties are given. Time-Frequency Peak Filtering Method is presented. The effects of different parameters on the performance of the method are investigated, and the results are presented. Time-Varying Wiener Filter is presented. Using simulations it is shown that the performance of the filter is good at SNR levels down to -5 dB. It is proposed and shown that the performance of the filter improves by using Support Vector Machines. The presented time-frequency filtering techniques are applied on test signals and on a real world signal. The results obtained by the two methods and also by classical zero-phase low-pass filtering are compared. It is observed that for low sampling rates Time-Varying Wiener Filter, and for high sampling rates Time-Frequency Peak Filter performs better.
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Su, Hongjiang. "Shoeprint image noise reduction and retrieval." Thesis, Queen's University Belfast, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486207.

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A shoeprint is a mark made when the sole of a shoe comes into contact with a surfice. People committing crimes inevitably leave their shoe marks at the crime scene. A study suggests that footwear impressions could be located and retrieved at approximately 35% of all crime scenes. More and more shoeprint images have been collected, leading to a few of shoeprint image databases. The constantly increasing of the size of these databases leads to a problem that it takes too much time to classify or retrieve them manually. In addition, when a shoeprint is actually being made, distortion, capture device-dependent noise, and cutting-out can be introduced. This thesis deals with the problems involved in the development of an automated shoeprint image classification/retrieval system. Firstly, it is concerned with investigating the problem of noise and artefact reduction, and the segmentation of a shoeprint from a noisy background. It aims to provide a software package to pre-processing an input shoeprint image from variety of sources. Secondly it is concerned with developing and investigating robust descriptors for a shoeprint image, and it also addresses the problem of matching shoeprint images using these descriptors. In this thesis, some novel techniques for image quality measure, Gaussian noise and Germ-grain noise reduction, pattern segmentation and screening have been developed. In addition, a few of low-level image feature descriptors, pattern & topological spectra and local image features, have been proposed for indexing and searching a shoeprint image dataset. This thesis also has developed a prototype system to demonstrate the proposed algorithms and the application cases in forensic science. Shoeprint image retrieval tests on a few of datasets (totally more than 15, 000 images) suggest that local image features, compared with other shoeprint image descriptors, have great potential to be applied in real-world forensic investigations.
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Bace, Matthew M. (Matthew Mark). "A receiver-compatible noise reduction system." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/116488.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1990.<br>Includes bibliographical references (leaves 111-113).<br>by Matthew M. Bace.<br>M.S.
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Read, Simon John. "Noise reduction technologies for open rotors." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608127.

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Haglund, Philip. "Noise reduction solutions for compact dishwasher." Thesis, KTH, Maskinkonstruktion (Inst.), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157658.

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Electrolux are currently developing a new kind of compact dishwasher, distinctly different from conventional dishwashers. A key concern that emerged through the development process was noise. The sound was characterized as loud and annoying. The goal of this thesis was to develop a mechanical design solution that would guarantee a noise level below 55 dBA. Initial tests reaffirmed the concern regarding noise. Noise was prevalent and the sound pressure level ranging from 58,8 to 59,8 was considered intolerable. Tests were performed in an acoustic lab to determine vibration source and noise factors. The pump was identified as the vibration source and the suspension was identified as the major noise factor. The rubber flange supporting the pump was fastened with screws. Loose screws failed to keep the pump in place whilst tight screws deformed the flange and pushed the pump against an adjacent plastic feature. There was a perceptible increase of 4,5 dBA when the two were in contact, and a palpable increase of 9,4 dBA when they were hitting each other. Metal spacers were the most reliable and effective solution for lowering the noise. Placed between the flange and the screws, the spacers kept the rubber flange from deforming and prevented it from hitting the adjacent feature. Metal spacers reduced the noise level from 60,6 to 51,3 dBA, a difference perceived half as loud. The efficiency of the metal washer was demonstrated in field tests, during which no complaints were made on the noise level. Pumps and dishwashers were tested in a hydraulic lab to determine operating point and best efficiency point. Pump compatibility determines performance and reliability, as poor operating conditions cause additional vibration, noise and wear. Tests concluded that the hydraulic performance and overall efficiency was poor. The operating point ranged from 4 to 8%, far below the best efficiency point of 15%. Pumps were put through a reliability test, none of which met the company requirement for longevity. Conclusions from the research were used to develop a new suspension concept. The design enabled vibrations to be absorbed and ensured infallible assembly. Rubber straps stretch between the pump and three surrounding support rods and provide supported during operation. The elastic suspension counteracts pump motion and dislocation. A prototype was manufactured to demonstrate the principle function and design.<br>Electrolux håller på att utveckla en ny typ av diskbänksdiskmaskin som skiljer sig från konventionella diskmaskiner. Ett problem som framkom under utvecklingsarbetet var ljudnivån som ansågs ovanligt hög och störande. Målet med denna avhandling var att utveckla en mekanisk konstruktionslösning som kunde garanterade en ljudnivå under 55 dBA. Tidiga tester bekräftade problematiken kring ljudnivån. Oljudet var påtagligt och ljudtrycksnivån varierade mellan 58,8 och 59,8 dBa, vilket ansågs oacceptabelt. Tester gjordes i ett akustiskt laboratorium för att fastställa vibrationskällan och ljudsfaktorer. Pumpen identifierades som vibrationskälla och upphängningen identifierades som en viktig ljudsfaktor. Gummiflänsen som höll pumpen på plats fästes med skruvar. Lösa skruvar misslyckades med att hålla pumpen på plats medan åtsittande skruvar deformerade flänsen och tryckte pumpen mot en intilliggande plastdetalj. Ljudet ökade märkbart med 4,5 dBA när de två trycktes mot varandra. När de slogs mot varande blev det en påtaglig ökning med 9,4 dBA. Metalldistanser var den mest tillförlitliga och effektiva lösningen för att dämpa ljudet. De placerades mellan flänsen och skruvarna för att förhindra flänsen från att deformeras och träffa den intilliggande plastdetaljen. Ljudnivån minskade från 60,6 till 51,3 dBA. För en människa upplevs skillnaden som en halvering av ljudnivån. Metalldistansernas effektivitet påvisades i fältprover, där inga klagomål gjordes på ljudnivån. Pumpar och diskmaskiner testades i ett hydrauliklaboratorium för att bestämma driftspunkten och bästa verkningsgradspunkten. Pumpkompatibilitet avgör prestanda och tillförlitlighet, eftersom dåliga driftsförhållanden orsakar ytterligare vibrationer, ljud och slitage. Slutsatsen av testerna var att den hydrauliska prestanda och totala effektiviteten var låg. Driftspunkten varierade från 4 till 8 %, långt under den bästa verkningsgradspunkten kring 15 %. Pumpar sattes på prov i ett tillförlitlighetstest. Ingen av de pumpar som provades uppfyllde kravet på livslängd. En konceptuell pumpupphängning utvecklades baserad på slutsatserna från forskningen. Den var utformades för att absorbera vibrationer och garantera korrekt montering. Pumpen hålls på plats med hjälp av gummiband istället för en gummifläns. Den elastiska upphängningen motverkar pumpens rörelser och förhindrar att den förskjuts. En prototyp tillverkades för att demonstrera konceptets funktion och design.
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Gruber, M. "Airfoil noise reduction by edge treatments." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/349012/.

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The general aim of this thesis is to investigate experimentally airfoil trailing edge noise reduction using various trailing edge geometries. The work presented in this thesis is part of the FP7 European Project FLOCON. This thesis focuses on sawtooth serrations and a detailed study is conducted in which thirty seven sawtooth trailing edges are tested for reducing the noise at various flow velocities and angles of attack. Broadband noise reductions of up to 5 dB are obtained below some critical frequency above which the noise is increased. The mechanisms by which the noise is changed in the presence of sawtooth serrations are also investigated experimentally by measuring the changes introduced in the unsteady surface pressure near the edge, the turbulence in the boundary layer and in the near wake, and also using Howe's model [66] as a reference for comparisons. Generally, it is shown that noise reductions occur due to an attenuation of the interaction between incident and scattered pressures, which results in a decrease of up to a half of the phase speed along the edges compared with the corresponding straight edge. The noise increase is shown to be caused by a cross-flow being forced through the valleys of the serrations by the pressure difference between the two sides of the airfoil near the trailing edge. Four novel trailing edge geometries are also tested to address the high frequency noise increase observed with sawtooth serrations. These are the slits, the sawtooth with holes, the slitted sawtooth and the random trailing edges. The slitted sawtooth are shown to provide a good alternative to sawtooth serrations, and afford similar levels of noise reductions while limiting the high frequency noise increase to no more than 1 dB. Random trailing edges also show reasonable levels of broadband noise reductions of up to 3 dB and no increase at high frequencies. Finally, serrations are used simultaneously at the trailing edge of an upstream airfoil and at the leading edge of a downstream airfoil to reduce trailing edge noise and interaction noise of the airfoils in a tandem configuration. Broadband reductions of up to 8.5 dB are obtained using the slitted sawtooth trailing edge and the leading edge serrations designed by ONERA. It is shown that most of the noise reduction is provided by a reduction of the airfoil leading edge response due to the leading edge serrations, but that sawtooth slitted serrations provide up to about 3.5 dB additional broadband noise reductions due to a reduction in its wake turbulence.
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Kaymak, Erkan. "Noise reduction and active noise control of high frequency narrow band dental drill noise." Thesis, Brunel University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445923.

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Zheng, Haosheng, and Kaichun Zhang. "Noise Analysis of Computer Chassis and Secondary Sound Source Noise Reduction." Thesis, Blekinge Tekniska Högskola, Institutionen för maskinteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18547.

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This article focuses on computer noise analysis and noise reduction processing. With the popularity of computers, people are increasingly demanding the comfort of using computers. Solving the noise problem of the computer case can make the working environment more comfortable. People working in a noisy environment for a long time can cause anxiety and the quality of work is not high. The main purpose of this paper is to analyse the characteristics of computer noise and to reduce the noise of the chassis through the secondary sound source. Through the comparison of the experimental and simulation results, the noise reduction effect of the secondary sound source on the computer case is obtained. This paper can provide a scientific reference for the manufacture of computer chassis and improvement of noise.
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Lariviere, Jeffrey P. "Transform-domain echo cancellation and noise reduction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0016/NQ57614.pdf.

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Books on the topic "Reduction of noise"

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Ingard, K. Uno. Noise reduction analysis. Infinity Science Press, 2010.

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Attenborough, K. Aircraft noise propagation, exposure & reduction. Spon Press, 2011.

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Cohen, Israel, Yiteng Huang, Jingdong Chen, and Jacob Benesty. Noise Reduction in Speech Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00296-0.

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Jansen, Maarten. Noise Reduction by Wavelet Thresholding. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0145-5.

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Center, NASA Glenn Research, ed. Fan noise reduction: An overview. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Great Britain. Health and Safety Executive. and Great Britain. Foundries Industry Advisory Committee., eds. Noise reduction at foundry shakeouts. HSE Books, 1994.

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Envia, Edmane. Fan noise reduction: An overview. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Israel, Cohen. Noise Reduction in Speech Processing. Springer-Verlag Berlin Heidelberg, 2009.

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Kontos, K. B. Improved NASA-ANOPP noise prediction computer code for advanced subsonic propulsion systems. National Aeronautics and Space Administration, 1996.

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Kontos, K. B. Improved NASA-ANOPP noise prediction computer code for advanced subsonic propulsion systems. National Aeronautics and Space Administration, 1996.

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Book chapters on the topic "Reduction of noise"

1

Wagner, Siegfried, Rainer Bareiß, and Gianfranco Guidati. "Noise Reduction." In Wind Turbine Noise. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-88710-9_8.

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Tischenko, Oleg, and Christoph Hoeschen. "Noise Reduction." In Imaging in Nuclear Medicine. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31415-5_8.

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Kovalevsky, Vladimir. "Noise Reduction." In Modern Algorithms for Image Processing. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4237-7_2.

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Sinambari, Gh Reza. "Noise Reduction." In Design Acoustics. Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40183-2_5.

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Huber, David Miles, and Robert E. Runstein. "Noise Reduction." In Modern Recording Techniques. Routledge, 2017. http://dx.doi.org/10.4324/9781315666952-16.

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Keller, Warren A. "Nonlinear Noise Reduction." In The Patrick Moore Practical Astronomy Series. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25682-5_15.

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Hegger, Rainer, Holger Kantz, and Thomas Schreiber. "Nonlinear Noise Reduction." In Modelling and Forecasting Financial Data. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0931-8_19.

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Hallas, Tony. "Noise Reduction Techniques." In Lessons from the Masters. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7834-8_8.

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Keller, Warren A. "Nonlinear Noise Reduction." In The Patrick Moore Practical Astronomy Series. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97689-1_15.

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Talbot-Smith, Michael. "Noise reduction (analogue)." In Broadcast Sound Technology. Routledge, 2023. http://dx.doi.org/10.4324/9781003460510-19.

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Conference papers on the topic "Reduction of noise"

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Hema, R., S. Shivani, and M. Yuvarai. "Enhancing Underwater Acoustic Noise Reduction Method With Advanced Noise Reduction Techniques." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00102.

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Roebben, Arnout, Toon Van Waterschoot, and Marc Moonen. "Cascaded Noise Reduction and Acoustic Echo Cancellation Based on an Extended Noise Reduction." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10714941.

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Bennouna, Saâd, Thibaud Matharan, and Olivier Cheriaux. "Automotive HVAC Noise Reduction." In 10th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference. SAE International, 2018. http://dx.doi.org/10.4271/2018-01-1519.

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KRUGER, J., and P. LEISTNER. "EFFECTIVE NOISE REDUCTION WITH HYBRID SILENCERS." In Inter-Noise 1996. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/19497.

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OISHI, T., and Y. NAKAMURA. "NOISE REDUCTION OF MIXER-EJECTOR NOZZLE." In Inter-Noise 1996. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/19858.

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Chelidze, David. "Smooth Projective Nonlinear Noise Reduction." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13344.

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Many nonlinear or chaotic time series exhibit an innate broad spectrum, which makes noise reduction difficult. Locally projective noise reduction is one of the most effective tools. It is based on proper orthogonal decomposition (POD), and works for both map-like and continuously sampled time series. However, POD only looks at geometrical or topological properties of data and does not take into account the temporal characteristics of time series. Here we present a new smooth projective noise reduction method. It uses bundles of locally reconstructed trajectory strands and their smooth orthogonal decomposition (SOD) to identify smooth local subspaces. Restricting trajectories to these subspaces imposes temporal smoothness on the filtered time series. It is shown that SOD-based noise reduction significantly outperforms the POD-based method for continuously sampled noisy time series.
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ROSENHOUSE, G., and J. SOKER. "NOISE REDUCTION IN SUBURBS BY SEQUENTIAL LOCATION OF TYPICAL ROAD AND PASSAGE ELEMENTS OF NOISE REDUCTION CAPACITIES." In Inter-Noise 1996. Institute of Acoustics, 2024. http://dx.doi.org/10.25144/19423.

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Holzfuss, Joachim. "Active3 noise reduction." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51031.

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Balint, Agneta M., and Stefan Balint. "Aircraft noise reduction." In TIM14 PHYSICS CONFERENCE - PHYSICS WITHOUT FRONTIERS. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4937253.

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McLean, I. "Telecine noise reduction." In IEE Seminar Digital Restoration of Film and Video Archives. IEE, 2001. http://dx.doi.org/10.1049/ic:20010022.

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Reports on the topic "Reduction of noise"

1

Lauchle, G. C., and S. Park. Sensor Flow-Induced Self Noise Reduction. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada408916.

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Harley, Thomas R., F. D. Shields, and James E. Hendrix. Digital Active Noise Reduction Ear Plugs. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada388005.

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Kodres, C. A. Jet Engine Test Cell Noise Reduction. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada380948.

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Truong, Vuong, James Chafouleas, Thomas Mosdal, Sophia Bragdon, Megan Bishop, and Jay Clausen. Thermography conversion for optimal noise reduction. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49414.

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Computer vision applications in terms of raw thermal radiance are limited by byte size. Normalizing the raw imagery reduces functional complexities that could otherwise aide a computer processing algorithm. This work explores a method to normalize 16-bit signed integer (I16) into unsigned 8-bit (U8) while maintaining the integrity of the correlation coefficients between the raw data sets and the environmental parameters that affects thermal anomaly detectability.
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Rohatgi, Upendra, and Michael Furey. Drag and Noise Reduction for Road Vehicles. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1083749.

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Russotti, Joseph S., Robert P. Jackman, Thomas P. Santoro, and Deborah D. White. Noise Reduction Stethoscope for United States Navy Application. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada390349.

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Cohen, Z. Noise Reduction with Microphone Arrays for Speaker Identification. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1034487.

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Houtsma, Adrianus J. Noise and Reverberation Reduction in Post Chapel Activity Room. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada491523.

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Cox, Cary, and Richard Lewis. Noise Reduction Methods for Detecting Impulses in Seismic Data. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada327963.

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Albert, Sarah, and Michael Fleigle. Noise Reduction Capability of the Trampoline Fabric Wind Dome. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2430343.

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