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Journal articles on the topic 'Noise suppression'

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Published: 4 June 2021
Last updated: 7 February 2022

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1

Vilar, J. M. G., and J. M. Rubí. "Noise Suppression by Noise." Physical Review Letters 86, no. 6 (February 5, 2001): 950–53. http://dx.doi.org/10.1103/physrevlett.86.950.

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2

Stuart, Andrew, and Alyson K. Butler. "Contralateral Suppression of Transient Otoacoustic Emissions and Sentence Recognition in Noise in Young Adults." Journal of the American Academy of Audiology 23, no. 09 (October 2012): 686–96. http://dx.doi.org/10.3766/jaaa.23.9.3.

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Background: One purported role of the medial olivocochlear (MOC) efferent system is to reduce the effects of masking noise. MOC system functioning can be evaluated noninvasively in humans through contralateral suppression of otoacoustic emissions. It has been suggested that the strength of the MOC efferent activity should be positively associated with listening performance in noise. Purpose: The objective of the study was to further explore this notion by examining contralateral suppression of transient evoked otoacoustic emissions (TEOAEs) and sentence recognition in two noises with normal hearing young adults. Research Design: A repeated measures multivariate quasi-experimental design was employed. Study Sample: Thirty-two normal hearing young adult females participated. Data Collection and Analysis: Reception thresholds for sentences (RTSs) were determined monaurally and binaurally in quiet and in competing continuous and interrupted noises. Both noises had an identical power spectrum and differed only in their temporal continuity. “Release from masking” was computed by subtracting RTS signal-to-noise ratios in interrupted from continuous noise. TEOAEs were evoked with 80 dB peSPL click stimuli. To examine contralateral suppression, TEOAEs were evaluated with 60 dB peSPL click stimuli with and without a contralateral 65 dB SPL white noise suppressor. Results: A binaural advantage was observed for RTSs in quiet and noise (p < .0001) while there was no difference between ears (p >.05). In noise, performance was superior in the interrupted noise (i.e., RTSs were lower vs. continuous noise; p < .0001). There were no statistically significant differences in TEOAE levels between ears (p >.05). There was also no significant difference in the amount of suppression between ears (p = .41). There were no significant correlations or predictive linear relations between the amount of TEOAE suppression and any indices of sentence recognition in noise (i.e., RTS signal-to-noise ratios and release from masking; p > .05). Conclusions: The findings are not consistent with the notion that increased medial olivocochlear efferent feedback, as assessed via contralateral suppression of TEOAEs, is associated with improved speech perception in continuous and interrupted noise.
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3

Nakamura, Shogo. "Noise suppression apparatus." Journal of the Acoustical Society of America 92, no. 3 (September 1992): 1796. http://dx.doi.org/10.1121/1.403853.

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4

de Cheveigné, Alain, and Jonathan Z. Simon. "Sensor noise suppression." Journal of Neuroscience Methods 168, no. 1 (February 2008): 195–202. http://dx.doi.org/10.1016/j.jneumeth.2007.09.012.

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5

Ishimaru, Kenzo. "Rotor noise suppression." Journal of the Acoustical Society of America 88, no. 4 (October 1990): 2050. http://dx.doi.org/10.1121/1.400167.

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6

Lecours, Gaetan. "Track noise suppression." Journal of the Acoustical Society of America 104, no. 2 (August 1998): 617. http://dx.doi.org/10.1121/1.423390.

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7

Riel, Frank J. "Noise suppression panel." Journal of the Acoustical Society of America 77, no. 2 (February 1985): 781. http://dx.doi.org/10.1121/1.392296.

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8

Furuta, Satoru. "Noise suppression device." Journal of the Acoustical Society of America 120, no. 5 (2006): 2413. http://dx.doi.org/10.1121/1.2395158.

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9

Vilmur, Richard J. "Noise suppression system." Journal of the Acoustical Society of America 86, no. 5 (November 1989): 2052. http://dx.doi.org/10.1121/1.398490.

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10

Shinohara, Kazuhiro. "Noise suppression structure." Journal of the Acoustical Society of America 128, no. 3 (2010): 1559. http://dx.doi.org/10.1121/1.3490346.

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11

Zhang, Ming, and Hui Lan. "System for noise suppression, transceiver and method for noise suppression." Journal of the Acoustical Society of America 120, no. 4 (2006): 1764. http://dx.doi.org/10.1121/1.2372355.

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12

Rhode, W. S., and S. Greenberg. "Lateral suppression and inhibition in the cochlear nucleus of the cat." Journal of Neurophysiology 71, no. 2 (February 1, 1994): 493–514. http://dx.doi.org/10.1152/jn.1994.71.2.493.

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1. The ability of cells in the cochlear nucleus (CN) to encode frequency information in the presence of background noise on the basis of "place/rate" information was investigated by measuring the threshold, magnitude, and extent of lateral suppression in the ventral and dorsal CN of the anesthesized cat. The suppression regions were delineated through the use of "masked" response areas (MRAs). The MRA is a family of isointensity curves derived from the average discharge rate in response to a tone of variable frequency and sound pressure level in the presence of a concurrently presented broadband, quasi-flat-spectrum noise. Tonal stimuli of sufficient intensity are often effective in significantly reducing the average discharge rate of CN neurons over a wide frequency range. 2. Most units in the CN exhibit prominent lateral suppressive sidebands, but the variability in threshold, magnitude, and extent of suppression is large. Primary-like and onset units of the ventral CN manifest the least suppression and have the highest suppression thresholds. Pauser/buildup units in the dorsal division and choppers distributed throughout the CN show the largest amount of suppression and have the lowest suppression thresholds. 3. Auditory nerve fibers manifest some degree of lateral suppression, particularly fibers of low and medium spontaneous rate. However, in few instances are the threshold, magnitude, and extent comparable with that observed among the majority of chopper and pauser/buildup units. For this reason the lateral suppression observed among the latter unit types is unlikely to originate entirely from cochlear processes, but rather is likely to reflect largely neural mechanisms intrinsic to the CN. In contrast, the MRAs of most primary-like and onset units suggest that the suppression behavior of most of these cells originates mostly, if not entirely, in the cochlea and auditory nerve. 4. A primary consequence of lateral suppression is to preserve the sharp frequency selectivity of CN neurons at moderate to high sound pressure levels, particularly in background noise. In this fashion lateral suppressive mechanisms potentially enhance the representation of spectral information on the basis of place/rate information relative to that in the auditory nerve under noisy background conditions. 5. Lateral suppressive mechanisms probably underlie the dynamic range shift seen in the presence of a simultaneously presented noise. This mechanism may be crucial for preserving the ability to perceive signals in a noisy background.
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13

Hao, Yiya, Shuai Cheng, Gong Chen, Yaobin Chen, and Liang Ruan. "A neural network based noise suppression method for transient noise control with low-complexity computation." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 1 (August 1, 2021): 5902–9. http://dx.doi.org/10.3397/in-2021-11598.

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Over the decades, the noise-suppression (NS) methods for speech enhancement (SE) have been widely utilized, including the conventional signal processing methods and the deep neural networks (DNN) methods. Although stationary-noise can be suppressed successfully using conventional or DNN methods, it is significantly challenging while suppressing the non-stationary noise, especially the transient noise. Compared to conventional NS methods, DNN NS methods may work more effectively under non-stationary noises by learning the noises' temporal-frequency characteristics. However, most DNN methods are challenging to be implemented on mobile devices due to their heavy computation complexity. Indeed, even a few low-complexity DNN methods are proposed for real-time purposes, the robustness and the generalization degrade for different types of noise. This paper proposes a single channel DNN-based NS method for transient noise with low computation complexity. The proposed method enhanced the signal-to-noise ratio (SNR) while minimizing the speech's distortion, resulting in a superior improvement of the speech quality over different noise types, including transient noise.
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14

Lin, Tingting, Xiaokang Yao, Sijia Yu, and Yang Zhang. "Electromagnetic Noise Suppression of Magnetic Resonance Sounding Combined with Data Acquisition and Multi-Frame Spectral Subtraction in the Frequency Domain." Electronics 9, no. 8 (August 5, 2020): 1254. http://dx.doi.org/10.3390/electronics9081254.

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As an advanced groundwater detection method, magnetic resonance sounding (MRS) has received more and more attention. However, the biggest challenge is that MRS measurements always suffer with a bad signal-to-noise ratio (SNR). Aiming at the problem of noise interference in MRS measurement, we propose a novel noise-suppression approach based on the combination of data acquisition and multi-frame spectral subtraction (DA-MFSS). The pure ambient noise from the measurement area is first collected by the receiving coil, and then the noisy MRS signal is recorded following the pulse moments transmitting. The procedure of the pure noise and the noisy MRS signal acquisition will be repeated several times. Then, the pure noise and the noisy signal are averaged to preliminarily suppress the noise. Secondly, the averaged pure noise and the noisy signal are divided into multiple frames. The framed signal is transformed into the frequency domain and the spectral subtraction method is applied to further suppress the electromagnetic noise embedded in the noisy MRS signal. Finally, the de-noised signal is recovered by the overlap-add method and inverse Fourier transformation. The approach was examined by numerical simulation and field measurements. After applying the proposed approach, the SNR of the MRS data was improved by 16.89 dB and both the random noise and the harmonic noise were well suppressed.
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15

Yamaguchi, Noboru, and Masao Konomi. "Ambient noise suppression circuit." Journal of the Acoustical Society of America 105, no. 4 (1999): 2071. http://dx.doi.org/10.1121/1.426779.

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16

Franklin, David. "Automatic background noise suppression." Journal of the Acoustical Society of America 78, no. 5 (November 1985): 1928. http://dx.doi.org/10.1121/1.392721.

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17

Ewbank, Michael E. "Noise suppression in torpedoes." Journal of the Acoustical Society of America 84, no. 5 (November 1988): 1964. http://dx.doi.org/10.1121/1.397110.

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18

Downs, Edward F., and Kevin M. Venturella. "High Noise suppression microphone." Journal of the Acoustical Society of America 112, no. 3 (2002): 798. http://dx.doi.org/10.1121/1.1514516.

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19

Li, Xianye, Nan Qi, Shan Jiang, Yurong Wang, Xun Li, and Baoqing Sun. "Noise Suppression in Compressive Single-Pixel Imaging." Sensors 20, no. 18 (September 18, 2020): 5341. http://dx.doi.org/10.3390/s20185341.

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Compressive single-pixel imaging (CSPI) is a novel imaging scheme that retrieves images with nonpixelated detection. It has been studied intensively for its minimum requirement of detector resolution and capacity to reconstruct image with underdetermined acquisition. In practice, CSPI is inevitably involved with noise. It is thus essential to understand how noise affects its imaging process, and more importantly, to develop effective strategies for noise compression. In this work, two ypes of noise classified as multiplicative and additive noises are discussed. A normalized compressive reconstruction scheme is firstly proposed to counteract multiplicative noise. For additive noise, two types of compressive algorithms are studied. We find that pseudo-inverse operation could render worse reconstructions with more samplings in compressive sensing. This problem is then solved by introducing zero-mean inverse measurement matrix. Both experiment and simulation results show that our proposed algorithms significantly surpass traditional methods. Our study is believed to be helpful in not only CSPI but also other denoising works when compressive sensing is applied.
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20

Zhang, Yu Hua, Li Min Jia, and Zhong Li. "Far Field Noise Suppression Method in McWiLL Intercom Based on Double Uni-Directional Microphone." Advanced Materials Research 267 (June 2011): 104–8. http://dx.doi.org/10.4028/www.scientific.net/amr.267.104.

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To satisfy McWiLL communication requirement in noisy environment, a far field noise suppression method based on double uni-direction microphone in McWiLL intercom was studied. The method arranges two uni-direction microphones rationally and uses analog noise cancelling processor to accomplish surrounding noise reduction in McWiLL intercom in noisy environment. To verify validity of the method, several contrast experiments using diagnostic rhyme test method were done. Experiments results show that the far field noise suppression method based on double uni-direction microphone is effective for surrounding noise reduction.
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21

Yu, Wen, and Wei Chen. "Smart Noise Jamming Suppression Technique Based on Blind Source Separation." International Journal of Signal Processing Systems 7, no. 1 (March 2019): 14–19. http://dx.doi.org/10.18178/ijsps.7.1.14-19.

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22

Sudha, S., G. R. Suresh, and R. Sukanesh. "Comparative Study on Speckle Noise Suppression Techniques for Ultrasound Images." International Journal of Engineering and Technology 1, no. 1 (2009): 57–62. http://dx.doi.org/10.7763/ijet.2009.v1.10.

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23

Ding, Yong, Xiao Hua Luo, Min Yong Wan, and Ming Yu Liu. "A Novel Transient Improvement with Overshoot Suppression." Advanced Materials Research 217-218 (March 2011): 119–24. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.119.

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In conventional image enhancement methods, the noises and overshoot occur which degrade the image quality significantly accompanied with transient improvement. To obtain high quality image enhancement, a novel transient improvement with overshoot suppression and noise reduction is proposed. It employs a Laplacian of Gaussian operator and a Sobel operator to achieve transient improvement, and uses a nonlinear clamp filter in luminance and a median filter in chrominance for overshoot suppression. Experimental results indicate that it can achieve better transient improvement and sharpen the blurred edge effectively while suppressing or preventing overshoot.
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24

Li, Ke Ming, and Chao Zhang. "Research on Linear Array TDI-CCD System with Noise Suppression." Applied Mechanics and Materials 274 (January 2013): 266–69. http://dx.doi.org/10.4028/www.scientific.net/amm.274.266.

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Noises in the imaging process will result in image measurement errors, so whether these noises can be effectively suppressed is the key to improve the measurement accuracy. This paper analyzes the noise components of TDI-CCD system from three perspectives, builds the mathematical model of typical noises, designs correlated double sampling noise processing circuit for TDI-CCD system, realizes the software implementation of hardware design and achieves the purpose to improve the signal-to-noise ratio and image quality
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25

Thibodeau, Linda M. "Evaluation of Auditory Enhancement and Auditory Suppression in Listeners With Normal Hearing and Reduced Speech Recognition in Noise." Journal of Speech, Language, and Hearing Research 39, no. 5 (October 1996): 947–56. http://dx.doi.org/10.1044/jshr.3905.947.

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A number of individuals complain of difficulties with speech recognition in noise in spite of normal hearing. This has prompted a search for disruptions in other areas of auditory processing that may account for these deficits. Two processes that may be related to speech recognition, auditory suppression and auditory enhancement, were evaluated in five listeners with normal speech recognition in noise (NSRN) and five listeners with reduced speech recognition in noise (RSRN). Although differences between the two groups were not observed for enhanced forward masking, significant differences were observed in two-tone suppression when the duration of the suppressor was varied. Those with RSRN showed greater suppression than those with NSRN when the suppressor onset preceded the masker onset.
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26

Cao, Chun Yan, Shui Dong Xiong, Zheng Liang Hu, and Yong Ming Hu. "Suppression of Double Rayleigh Scattering Induced Coherent Noise in a Remote Fiber Sensor System Using PGC Technique." Advanced Materials Research 571 (September 2012): 185–89. http://dx.doi.org/10.4028/www.scientific.net/amr.571.185.

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Double Rayleigh scattering (DRS) induces coherent noises in remotely interrogated optical fiber sensor systems especially when high coherence laser sources are used. Phase generation carried (PGC) technique has been used in optical fiber sensors to overcome bias induced signal fading and eliminated incoherent noises at low frequency. In this paper we demonstrated that PGC technique can also suppress DRS induced coherent noises. In an experimental setup with total 50-km input and output lead fibers, we achieved maximum 7dB of intensity noise suppression and maximum 10dB of phase noise suppression. With PGC technique, DRS induced phase noise has been suppressed to the sensor self-noise level.
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27

Ma, Jingyan, Sho Muroga, Yasushi Endo, Shuichiro Hashi, Masayuki Naoe, Hiroo Yokoyama, Yoshiaki Hayashi, and Kazushi Ishiyama. "Noise suppression and crosstalk analysis of on-chip magnetic film-type noise suppressor." AIP Advances 8, no. 5 (May 2018): 056613. http://dx.doi.org/10.1063/1.5007315.

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28

Yashaswini, L., and Sandeep Maruthy. "The Influence of Efferent Inhibition on Speech Perception in Noise: A Revisit Through Its Level-Dependent Function." American Journal of Audiology 28, no. 2S (August 28, 2019): 508–15. http://dx.doi.org/10.1044/2019_aja-ind50-18-0098.

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Purpose The study aimed to assess the relationship between the level-dependent function of efferent inhibition and speech perception in noise across different intensities of suppressor and across different signal-to-noise ratios (SNRs) of speech. Method Twenty-six young normal-hearing adults participated in the study. Contralateral suppression of transient evoked otoacoustic emissions (TEOAEs) was measured for 3 levels of suppressor (40, 50, and 60 dB SPL). Speech identification score (SIS) was measured at 5 ipsilateral SNR conditions (quiet, 0, −5, −10, and −15 dB), with and without contralateral broadband noise at 3 levels (40, 50, and 60 dB SPL). Furthermore, SNR-50 was measured with and without the same 3 levels of contralateral broadband noise. Results The results showed that the suppression magnitude of TEOAE increased with an increase in suppressor level. However, neither SIS nor SNR-50 was influenced by the contralateral noise. In addition, SIS and SNR-50 did not show significant correlation with contralateral suppression of TEOAEs. This was true at all the SNRs and contralateral noise levels used in the study. Conclusions The findings suggest that the intensity of noise directly influences medial olivocochlear bundle–mediated efferent inhibition. However, the role of the medial olivocochlear bundle in regulating speech perception in noise needs to be revisited. Supplemental Material https://doi.org/10.23641/asha.9336353
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29

Kim, B., and H. C. Lee. "Noise bandwidth suppression for low noise readout circuit." Electronics Letters 38, no. 12 (2002): 558. http://dx.doi.org/10.1049/el:20020414.

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30

Whitehouse, Ian R., and James K. Chu. "Wing flap aerodynamic noise suppression." Journal of the Acoustical Society of America 91, no. 5 (May 1992): 3085. http://dx.doi.org/10.1121/1.402895.

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31

Wu, Fuke, and Shigeng Hu. "Suppression and stabilisation of noise." International Journal of Control 82, no. 11 (September 14, 2009): 2150–57. http://dx.doi.org/10.1080/00207170902968108.

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32

Torkelson, Delbert W. "Jet engine noise suppression system." Journal of the Acoustical Society of America 89, no. 3 (March 1991): 1486. http://dx.doi.org/10.1121/1.400600.

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33

Shekarforoush, H. "Noise suppression by removing singularities." IEEE Transactions on Signal Processing 48, no. 7 (July 2000): 2175–79. http://dx.doi.org/10.1109/78.847805.

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34

Pawluczyk, Romuald. "Holographic microinterferometer with noise suppression." Applied Optics 28, no. 18 (September 15, 1989): 3871. http://dx.doi.org/10.1364/ao.28.003871.

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35

Liu, T. I. "Development of noise‐suppression saws." Journal of the Acoustical Society of America 85, S1 (May 1989): S24. http://dx.doi.org/10.1121/1.2026875.

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36

Nocera, F. "LIGO laser intensity noise suppression." Classical and Quantum Gravity 21, no. 5 (February 3, 2004): S481—S485. http://dx.doi.org/10.1088/0264-9381/21/5/014.

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37

NISHIMURA, Masaharu. "Active Suppression of Aerodynamic Noise." Journal of the Japan Society for Aeronautical and Space Sciences 43, no. 493 (1995): 67–74. http://dx.doi.org/10.2322/jjsass1969.43.67.

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38

Kochelap, V. A., V. N. Sokolov, O. M. Bulashenko, and J. M. Rubı́. "Coulomb suppression of surface noise." Applied Physics Letters 78, no. 14 (April 2, 2001): 2003–5. http://dx.doi.org/10.1063/1.1360227.

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39

Thyssen, Jes. "Linear prediction based noise suppression." Journal of the Acoustical Society of America 120, no. 6 (2006): 3452. http://dx.doi.org/10.1121/1.2409449.

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40

Trickett, Stewart R. "F‐xy eigenimage noise suppression." GEOPHYSICS 68, no. 2 (March 2003): 751–59. http://dx.doi.org/10.1190/1.1567245.

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F‐xy eigenimage filtering works by replacing constant‐frequency slices with the sum of their first few weighted eigenimages, and can be used to remove random noise from stacked 3D seismic volumes. It performs equally well on flat or dipping events, and has no effect on noiseless data when the number of dips is no more than the number of summed eigenimages. It is also independent of many x‐ and y‐consistent effects such as reordering, statics, and filtering. One consequence is that it tends to do a good job along the boundaries of the 3D grid. Execution time is comparable to f‐xy prediction filtering, but can be greatly reduced using approximations based on Lanczos bidiagonalization.
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41

Miles, Michael W. "Headset for ambient noise suppression." Journal of the Acoustical Society of America 83, no. 6 (June 1988): 2472. http://dx.doi.org/10.1121/1.396302.

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42

Gebert, Anton M., and Yori Gokhin. "Hearing aid noise suppression system." Journal of the Acoustical Society of America 84, no. 6 (December 1988): 2304. http://dx.doi.org/10.1121/1.396751.

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43

Abrams, Michael L. "Noise suppression during seismic exploration." Journal of the Acoustical Society of America 84, no. 6 (December 1988): 2302. http://dx.doi.org/10.1121/1.397004.

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44

Berry, M. V. "Suppression of superoscillations by noise." Journal of Physics A: Mathematical and Theoretical 50, no. 2 (December 5, 2016): 025003. http://dx.doi.org/10.1088/1751-8113/50/2/025003.

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45

Ivanov, Eugene N., and Michael E. Tobar. "Noise Suppression With Cryogenic Resonators." IEEE Microwave and Wireless Components Letters 31, no. 4 (April 2021): 405–8. http://dx.doi.org/10.1109/lmwc.2021.3059291.

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46

Lee, Soojeong, and Gangseong Lee. "Noise Estimation and Suppression Using Nonlinear Function withA PrioriSpeech Absence Probability in Speech Enhancement." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5352437.

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This paper proposes a noise-biased compensation of minimum statistics (MS) method using a nonlinear function anda priorispeech absence probability (SAP) for speech enhancement in highly nonstationary noisy environments. The MS method is a well-known technique for noise power estimation in nonstationary noisy environments; however, it tends to bias noise estimation below that of the true noise level. The proposed method is combined with an adaptive parameter based on a sigmoid function anda prioriSAP for residual noise reduction. Additionally, our method uses an autoparameter to control the trade-off between speech distortion and residual noise. We evaluate the estimation of noise power in highly nonstationary and varying noise environments. The improvement can be confirmed in terms of signal-to-noise ratio (SNR) and the Itakura-Saito Distortion Measure (ISDM).
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47

Bobryk, R. V., and D. Yurchenko. "Beneficial Effect of Noise in Suppression of Self-Excited Vibrations." Fluctuation and Noise Letters 13, no. 03 (July 20, 2014): 1450022. http://dx.doi.org/10.1142/s0219477514500229.

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We discuss the possibility of full suppressions of self-excited vibrations by noise. Recently, periodic excitations have been intensively studied for this aim. We compare the used periodic and random noise excitations in the case of a two-mass system. It is shown that the random noise excitations can be more efficient under certain conditions. The telegraphic process is used as the source of noise. The mean-square (energetic) asymptotic stability of the system is a tool in study of the suppression. The stability charts are presented for different values of the transition rate of the telegraphic noise.
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48

Nageswara Rao, S., K. Jaya Sankar, and C. D. Naidu. "An Improved Bi-Level Thresholding Based Uncertainty Evaluation for Speech Enhancement in Non-Stationary Noises." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 436. http://dx.doi.org/10.14419/ijet.v7i2.24.12130.

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This paper proposes a new speech enhancement framework to improve the quality of speeches recorded under adverse acoustic environments based on the speech presence uncertainty. Since the uncertainty evaluation gives a more and clear discrimination about the speech and noise, this paper proposes a new uncertainty evaluation mechanism as a preprocessing mechanism to the noise suppression methods. This mechanism relates with energies of a noisy speech signal and classifies the speech segments and noise segments more perfectly. In addition to the quality enhancement, this approach also reduces the unnecessary computational burden over the speech processing system. Extensive simulations are carried out over the speech signals with different types of non-stationary noises like babble noise, exhibition noise, restaurant noise and train station noises and the performance is measured with the performance metrics namely the Output SNR, AvgSegSNR, PESQ and COMP. The comparative analysis of proposed approach over the conventional approaches shows an outstanding performance in all environments.
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49

Jena, Bibekananda, Punyaban Patel, and G. R. Sinha. "An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter." International Journal of Rough Sets and Data Analysis 5, no. 2 (April 2018): 148–63. http://dx.doi.org/10.4018/ijrsda.2018040108.

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Abstract:
A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.
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Ephraim, Gutmark, Cuppoletti Dan, Mora Pablo, and Heeb Nicholas. "IL04 CHARACTERIZATION OF SUPERSONIC JET NOISE PRODUCTION AND METHODS FOR ITS SUPPRESSION." Proceedings of the International Conference on Jets, Wakes and Separated Flows (ICJWSF) 2013.4 (2013): _IL04–1_—_IL04–11_. http://dx.doi.org/10.1299/jsmeicjwsf.2013.4._il04-1_.

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