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Добірка наукової літератури з теми "Reduction of noise"

Автор: Grafiati

Опубліковано: 9 листопада 2024

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Статті в журналах з теми "Reduction of noise"

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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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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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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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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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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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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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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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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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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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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Дисертації з теми "Reduction of noise"

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.
Includes bibliographical references (leaves 111-113).
by Matthew M. Bace.
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.
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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Книги з теми "Reduction of noise"

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

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

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Cohen, Israel, Yiteng Huang, Jingdong Chen, and Jacob Benesty. Noise Reduction in Speech Processing. Berlin, Heidelberg: 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. New York, NY: 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. [Cleveland, Ohio]: 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. Sudbury: HSE Books, 1994.

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

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

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

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Частини книг з теми "Reduction of noise"

Wagner, Siegfried, Rainer Bareiß, and Gianfranco Guidati. "Noise Reduction." In Wind Turbine Noise, 163–79. Berlin, Heidelberg: 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, 135–66. Berlin, Heidelberg: 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, 5–42. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4237-7_2.

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Huber, David Miles, and Robert E. Runstein. "Noise Reduction." In Modern Recording Techniques, 443–48. Edition 9. | New York; London : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315666952-16.

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Sinambari, Gh Reza. "Noise Reduction." In Design Acoustics, 149–215. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40183-2_5.

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Keller, Warren A. "Nonlinear Noise Reduction." In The Patrick Moore Practical Astronomy Series, 167–74. Cham: 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, 401–16. Boston, MA: 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, 149–58. New York, NY: 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, 191–99. Cham: 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, 178–82. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003460510-19.

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Тези доповідей конференцій з теми "Reduction of noise"

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), 554–59. IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00102.

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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. 400 Commonwealth Drive, Warrendale, PA, United States: 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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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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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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Birman, Chaim, Alan S. Nasar, and Daniel R. Raichel. "Noise Reduction System for Telephones." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0531.

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Abstract The goal of this project is to design a system that increases the intelligibility of voice transmission through a telephone or similar device when used in a noisy environment. The designed system incorporates an auxiliary microphone into the transmitter portion of an ordinary telephone handset. This microphone, strategically located so that it receives the ambient noise and virtually no speech from the user, develops a signal that becomes phase-inverted by the system.. The resultant signal is mixed with the signal from the primary microphone to produce a final signal that contains significantly less background noise than the original signal. The amended signal is then relayed to the telephone receiver in the same manner as in an ordinary telephone. This system has many applications in the design of telephones and two-way radios for use in noisy environments such as subway stations, city streets, aircraft cabins, truck cabs, construction sites, etc.

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Звіти організацій з теми "Reduction of noise"

Lauchle, G. C., and S. Park. Sensor Flow-Induced Self Noise Reduction. Fort Belvoir, VA: Defense Technical Information Center, October 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. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada388005.

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Kodres, C. A. Jet Engine Test Cell Noise Reduction. Fort Belvoir, VA: Defense Technical Information Center, August 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.), September 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), June 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. Fort Belvoir, VA: Defense Technical Information Center, July 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), December 2011. http://dx.doi.org/10.2172/1034487.

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Houtsma, Adrianus J. Noise and Reverberation Reduction in Post Chapel Activity Room. Fort Belvoir, VA: Defense Technical Information Center, November 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. Fort Belvoir, VA: Defense Technical Information Center, July 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), August 2023. http://dx.doi.org/10.2172/2430343.

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