Relevant bibliographies by topics / Noisy environments

Academic literature on the topic 'Noisy environments'

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Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Noisy environments"

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Qian, Chao, Yang Yu, and Zhi-Hua Zhou. "Analyzing Evolutionary Optimization in Noisy Environments." Evolutionary Computation 26, no. 1 (March 2018): 1–41. http://dx.doi.org/10.1162/evco_a_00170.

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Many optimization tasks must be handled in noisy environments, where the exact evaluation of a solution cannot be obtained, only a noisy one. For optimization of noisy tasks, evolutionary algorithms (EAs), a type of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on the empirical study and design of EAs for optimization under noisy conditions, while the theoretical understandings are largely insufficient. In this study, we first investigate how noisy fitness can affect the running time of EAs. Two kinds of noise-helpful problems are identified, on which the EAs will run faster with the presence of noise, and thus the noise should not be handled. Second, on a representative noise-harmful problem in which the noise has a strong negative effect, we examine two commonly employed mechanisms dealing with noise in EAs: reevaluation and threshold selection. The analysis discloses that using these two strategies simultaneously is effective for the one-bit noise but ineffective for the asymmetric one-bit noise. Smooth threshold selection is then proposed, which can be proved to be an effective strategy to further improve the noise tolerance ability in the problem. We then complement the theoretical analysis by experiments on both synthetic problems as well as two combinatorial problems, the minimum spanning tree and the maximum matching. The experimental results agree with the theoretical findings and also show that the proposed smooth threshold selection can deal with the noise better.
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SUNITHA, P., V. SAILAJA, and B. VASANTHA LAKSHMI. "NOISE ROBUST SPEECH RECOGNITION UNDER NOISY ENVIRONMENTS." i-manager’s Journal on Pattern Recognition 7, no. 2 (2020): 23. http://dx.doi.org/10.26634/jpr.7.2.18094.

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Phung, Trung-Nghia, Huy-Khoi Do, Van-Tao Nguyen, and Quang-Vinh Thai. "Eigennoise Speech Recovery in Adverse Environments with Joint Compensation of Additive and Convolutive Noise." Advances in Acoustics and Vibration 2015 (November 3, 2015): 1–9. http://dx.doi.org/10.1155/2015/170183.

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The learning-based speech recovery approach using statistical spectral conversion has been used for some kind of distorted speech as alaryngeal speech and body-conducted speech (or bone-conducted speech). This approach attempts to recover clean speech (undistorted speech) from noisy speech (distorted speech) by converting the statistical models of noisy speech into that of clean speech without the prior knowledge on characteristics and distributions of noise source. Presently, this approach has still not attracted many researchers to apply in general noisy speech enhancement because of some major problems: those are the difficulties of noise adaptation and the lack of noise robust synthesizable features in different noisy environments. In this paper, we adopted the methods of state-of-the-art voice conversions and speaker adaptation in speech recognition to the proposed speech recovery approach applied in different kinds of noisy environment, especially in adverse environments with joint compensation of additive and convolutive noises. We proposed to use the decorrelated wavelet packet coefficients as a low-dimensional robust synthesizable feature under noisy environments. We also proposed a noise adaptation for speech recovery with the eigennoise similar to the eigenvoice in voice conversion. The experimental results showed that the proposed approach highly outperformed traditional nonlearning-based approaches.
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Chen, Bin, Jinbao Long, Hongtai Xie, Chenyang Li, Luokan Chen, Bonan Jiang, and Shuai Chen. "Portable atomic gravimeter operating in noisy urban environments." Chinese Optics Letters 18, no. 9 (2020): 090201. http://dx.doi.org/10.3788/col202018.090201.

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Cucker, Felipe, and Ernesto Mordecki. "Flocking in noisy environments." Journal de Mathématiques Pures et Appliquées 89, no. 3 (March 2008): 278–96. http://dx.doi.org/10.1016/j.matpur.2007.12.002.

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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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Liu, Shi Lin, and Zheng Pei. "Voice Activity Based on Noise Estimation in Noisy Environments." Applied Mechanics and Materials 239-240 (December 2012): 409–14. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.409.

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An improved project based on decision trees in noisy environments is proposed for robust endpoints detection. Firstly, the noise level of the environment is estimated by wavelet decomposition, and then whether the denoising process is done according to the noise level is determined. Next, the thresholds are obtained by decision trees for the signal. Finally, endpoints are detected by the double thresholds on different importance of the energy and zero-crossing rate (ZCR) in the corresponding situation. The simulation results indicate that the proposed method based on noise estimation can obtain the same accurate data by computing less than the one with decision trees.
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Zhao, Li Hua, and Xue Qing Xu. "Endpiont Detection in Noisy Speech Signal Using Teager Energy Entropy." Advanced Materials Research 926-930 (May 2014): 1806–9. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1806.

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In this paper, we propose a new method using teager energy operator and entropy to solve endpoint detection problem in noisy environment. With the teager energy operator, it is sensitive on AM and FM signal and noise suppression capability on noisy speech signal, calculate teager energy of noisy speech signal. According to the different teager energy probability distribution between noise and speech signal, teager energy entropy is different. Set two soft thresholds of four states to detect the endpoint of noisy speech signal. The simulation shows that the method has good effect of endpoint detection in low SNR conditions, the simulation results show that teager energy entropy of speech signal endpoint detection in noisy environments is feasible and effective, and improves the reliability of endpoint detection.
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Nidhyananthan, S. Selva, R. Shantha Selva Kumari, and A. Arun Prakash. "A review on speech enhancement algorithms and why to combine with environment classification." International Journal of Modern Physics C 25, no. 10 (September 11, 2014): 1430002. http://dx.doi.org/10.1142/s0129183114300024.

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Speech enhancement has been an intensive research for several decades to enhance the noisy speech that is corrupted by additive noise, multiplicative noise or convolutional noise. Even after decades of research it is still the most challenging problem, because most papers rely on estimating the noise during the nonspeech activity assuming that the background noise is uncorrelated (statistically independent of speech signal), nonstationary and slowly varying, so that the noise characteristics estimated in the absence of speech can be used subsequently in the presence of speech, whereas in a real time environment such assumptions do not hold for all the time. In this paper, we discuss the historical development of approaches that starts from the year 1970 to, the recent, 2013 for enhancing the noisy speech corrupted by additive background noise. Seeing the history, there are algorithms that enhance the noisy speech very well as long as a specific application is concerned such as the In-car noisy environments. It has to be observed that a speech enhancement algorithm performs well with a good estimation of the noise Power Spectral Density (PSD) from the noisy speech. Our idea pops up based on this observation, for online speech enhancement (i.e. in a real time environment) such as mobile phone applications, instead of estimating the noise from the noisy speech alone, the system should be able to monitor an environment continuously and classify it. Based on the current environment of the user, the system should adapt the algorithm (i.e. enhancement or estimation algorithm) for the current environment to enhance the noisy speech.
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Tang, Qiang, De Xiang Zhang, and Qing Yan. "Speech Stream Detection for Noisy Environments Based on Empirical Mode Decomposition." Applied Mechanics and Materials 397-400 (September 2013): 2239–42. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2239.

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A new approach for speech stream detection based on empirical mode decomposition (EMD) under a noisy environment is proposed. Accurate speech stream detection proves to significantly improve speech recognition performance under noise. The proposed algorithm relies on the Teager energy and spectral entropy characteristics of the signal to determine whether an input frame is speech or non-speech. Firstly, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions (IMFs) with the EMD. Then, spectral entropy is used to extract the desired feature for noisy IMF components and Teager energy is used to non-noisy IMF components. Finally, in order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experiments show that the proposed algorithm can suppress different noise types with different SNR.
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Dissertations / Theses on the topic "Noisy environments"

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Aschauer, Hans. "Quantum communication in noisy environments." Diss., lmu, 2005. http://nbn-resolving.de/urn:nbn:de:bvb:19-35882.

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Jaisimha, M. Y. "Compound document retrieval in noisy environments /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/6007.

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Parikh, Devangi Nikunj. "Improving the quality of speech in noisy environments." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45889.

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In this thesis, we are interested in processing noisy speech signals that are meant to be heard by humans, and hence we approach the noise-suppression problem from a perceptual perspective. We develop a noise-suppression paradigm that is based on a model of the human auditory system, where we process signals in a way that is natural to the human ear. Under this paradigm, we transform an audio signal in to a perceptual domain, and processes the signal in this perceptual domain. This approach allows us to reduce the background noise and the audible artifacts that are seen in traditional noise-suppression algorithms, while preserving the quality of the processed speech. We develop a single- and dual-microphone algorithm based on this perceptual paradigm, and conduct subjecting tests to show that this approach outperforms traditional noise-suppression techniques. Moreover, we investigate the cause of audible artifacts that are generated as a result of suppressing the noise in noisy signals, and introduce constraints on the noise-suppression gain such that these artifacts are reduced.
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Nayfeh, Taysir H. "Multi-signal processing for voice recognition in noisy environments." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-10222009-125021/.

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Chen, Aimin. "Speech recognition and enhancement in noisy cellular mobile environments." Thesis, Brunel University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251198.

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Laidler, Jonathan. "Modelling of glimpses for speech recognition in noisy environments." Thesis, University of Sheffield, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575364.

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Noisy environments pose significant problems to automatic speech recognition (ASR) systems. A common scenario is the cocktail party problem, where there are competing speakers. Human listeners perform well in these situations despite the fact that the target and the noise share similar characteristics. However, traditional ASR systems struggle to deal with non-stationary noise. The glimpsing theory of speech perception states that human listeners are able to focus their attention on spectre-temporal glimpses where the target speech is not masked by noise. Glimpses of clean speech are highly available when the noise source is another speaker, due to the sparse nature of spectra-temporal representations of speech. ASR systems which aim to model the behaviour of human listeners should also take advantage of glimpses. Existing studies have detected glimpses based on features such as pitch, which is known to be valuable for separating competing talkers. This thesis takes the opposite approach, using no prior knowledge of speech features but rather learning the features of glimpses from samples of clean speech. This is considered to be a model-driven approach, in contrast to previous source-driven approaches. This thesis draws inspiration from computational vision, where the analogous problem is that of partial object recognition. The proposed glimpse detection system identifies spectre-temporal interest points which are small patches of speech, then forms glimpses from connected regions of interest points. In addition to a detailed description of the novel ASR framework, the thesis presents three new investigations. The first discovers what size of spectra-temporal speech patch can be recognised by human listeners. The second investigates what kind of encoding should be applied to patches in order to best capture the features of clean speech. The third takes grouping algorithms that are popular in vision research and compares their success in creating glimpses from speech interest points. Finally the full end-to-end ASR system is evaluated on a speech separation task.
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Jimenez, Blazquez Lara. "Mathematical Methods for Maritime Signal Curation in Noisy Environments." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-43653.

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QTAGG has designed a real-time autonomous system that continuously calculates an optimum propulsion plan controlling the engines and propellers of a vessel. In this way, the precision of the signals that are used is very important, as any little error in the signal can produce incorrect control effects and cause critical damages to the equipment or passengers. This thesis describes the mathematics and implementation of a system to detect and correct disturbances in the data signals of a vessel. The system applies a signal curation based on mathematical modelling and statistics leading to clean data to use in QTAGG’s control system.
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Mahfoudia, Osama. "DVB-T based bistatic passive radars in noisy environments." Doctoral thesis, Universite Libre de Bruxelles, 2017. https://dipot.ulb.ac.be/dspace/bitstream/2013/258499/5/contratOM.pdf.

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Passive coherent location (PCL) radars employ illuminators of opportunity to detect and track targets. This silent operating mode provides many advantages such as low cost and interception immunity. Many radiation sources have been exploited as illumination sources such as broadcasting and telecommunication transmitters. The classical architecture of the bistatic PCL radars involves two receiving channels: a reference channel and a surveillance channel. The reference channel captures the direct-path signal from the transmitter, and the surveillancesignal collects the possible target echoes. The two major challenges for the PCL radars are the reference signal noise and the surveillance signal static clutter. A noisy reference signal degrades the detection probability by increasing the noise-floor level of the detection filter output. And the static clutter presence in the surveillance signal reduces the detector dynamic range and buries low magnitude echoes.In this thesis, we consider a PCL radar based on the digital video broadcasting-terrestrial (DVB-T) signals, and we propose a set of improved methods to deal with the reference signal noise and the static clutter in the surveillance signal. The DVB-T signals constitute an excellentcandidate as an illumination source for PCL radars; they are characterized by a wide bandwidth and a high radiated power. In addition, they provide the possibility of reconstructing the reference signal to enhance its quality, and they allow a straightforward static clutter suppressionin the frequency domain. This thesis proposes an optimum method for the reference signal reconstruction and an improved method for the static clutter suppression.The optimum reference signal reconstruction minimizes the mean square error between the reconstructed signal and the exact one. And the improved static clutter suppression method exploits the possibility of estimating the propagation channel. These two methods extend thefeasibility of a single receiver PCL radar, where the reference signal is extracted from the direct-path signal present in the surveillance signal.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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Gajic, Bojana. "Feature Extraction for Automatic Speech Recognition in Noisy Acoustic Environments." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-441.

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This thesis presents a study of alternative speech feature extraction methods aimed at increasing robustness of automatic speech recognition (ASR) against additive background noise.

Spectral peak positions of speech signals remain practically unchanged in presence of additive background noise. Thus, it was expected that emphasizing spectral peak positions in speech feature extraction would result in improved noise robustness of ASR systems. If frequency subbands are properly chosen, dominant subband frequencies can serve as reasonable estimates of spectral peak positions. Thus, different methods for incorporating dominant subband frequencies into speech feature vectors were investigated in this study.

To begin with, two earlier proposed feature extraction methods that utilize dominant subband frequency information were examined. The first one uses zero-crossing statistics of the subband signals to estimate dominant subband frequencies, while the second one uses subband spectral centroids. The methods were compared with the standard MFCC feature extraction method on two different recognition tasks in various background conditions. The first method was shown to improve ASR performance on both recognition tasks at sufficiently high noise levels. The improvement was, however, smaller on the more complex recognition task. The second method, on the other hand, led to some reduction in ASR performance in all testing conditions.

Next, a new method for incorporating subband spectral centroids into speech feature vectors was proposed, and was shown to be considerably more robust than the standard MFCC method on both ASR tasks. The main difference between the proposed method and the zero-crossing based method is in the way they utilize dominant subband frequency information. It was shown that the performance improvement due to the use of dominant subband frequency information was considerably larger for the proposed method than for the ZCPA method, especially on the more complex recognition task. Finally, the computational complexity of the proposed method is two orders of magnitude lower than that of the zero-crossing based method, and of the same order of magnitude as the standard MFCC method.

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Wu, Mingyang. "Pitch tracking and speech enhancement in noisy and reverberant environments." Columbus, Ohio : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1064341479.

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Thesis (Ph. D.)--Ohio State University, 2003.
Title from first page of PDF file. Document formatted into pages; contains xvi, 149 p.; also includes graphics. Includes abstract and vita. Advisor: DeLiang Wang, Dept. of Computer and Information Science. Includes bibliographical references (p. 136-149).
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Books on the topic "Noisy environments"

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Buchleitner, Andreas, and Klaus Hornberger, eds. Coherent Evolution in Noisy Environments. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45855-7.

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Rao, K. Sreenivasa, and Sourjya Sarkar. Robust Speaker Recognition in Noisy Environments. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07130-5.

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Grimm, Simon. Directivity Based Multichannel Audio Signal Processing For Microphones in Noisy Acoustic Environments. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-25152-9.

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Ontario. Ministry of the Environment. Environmental noise for environmental officers. [Toronto, ON: Ministry of the Environment], 1990.

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R, Forrest M., ed. Noise in the military environment. London: Brassey's Defence Publishers, 1988.

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Environmental noise survey: Guidance document. Johnstown Castle, Co. Wexford: Environmental Protection Agency, 2003.

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Ontario. Ministry of the Environment. Environmental Approvals and Land Use Planning Branch. Noise Assessment and Systems Support Unit. Introductory environmental noise: Course manual. S.l: s.n, 1988.

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Sass-Kortsak, Andrea M. Workplace noise and noise measurement: A basic guide. Hamilton, Ont: CCOHS, 1988.

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Feather, Timothy D. Reducing environmental noise impacts: A USAREUR noise management program handbook. Ft. Belvoir, Va: U.S. Army Corps of Engineers, Water Resources Center, Institute for Water Resources, 1991.

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Ontario. Ministry of the Environment. Manual for environmental noise: Certificate course. [Toronto: Ministry of the Environment], 1997.

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Book chapters on the topic "Noisy environments"

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Li, Junfeng, Masato Akagi, and Yôiti Suzuki. "Multi-channel Noise Reduction in Noisy Environments." In Chinese Spoken Language Processing, 258–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11939993_30.

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Branke, Jürgen, and Christian Schmidt. "Sequential Sampling in Noisy Environments." In Lecture Notes in Computer Science, 202–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30217-9_21.

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Ingold, Gert-Ludwig. "Path Integrals and Their Application to Dissipative Quantum Systems." In Coherent Evolution in Noisy Environments, 1–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45855-7_1.

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Englert, Berthold-Georg, and Giovanna Morigi. "Five Lectures on Dissipative Master Equations." In Coherent Evolution in Noisy Environments, 55–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45855-7_2.

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Wiesenfeld, Kurt, Thomas Wellens, and Andreas Buchleitner. "Stochastic Resonance." In Coherent Evolution in Noisy Environments, 107–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45855-7_3.

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Kümmerer, Burkhard. "Quantum Markov Processes." In Coherent Evolution in Noisy Environments, 139–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45855-7_4.

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Strunz, Walter T. "Decoherence in Quantum Physics." In Coherent Evolution in Noisy Environments, 199–233. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45855-7_5.

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Aschauer, Hans, and Hans J. Briegel. "Quantum Communication and Decoherence." In Coherent Evolution in Noisy Environments, 235–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45855-7_6.

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Keyl, Michael, and Reinhard F. Werner. "How to Correct Small Quantum Errors." In Coherent Evolution in Noisy Environments, 263–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45855-7_7.

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Castro Hoyos, C., S. Murillo-Rendón, and C. G. Castellanos-Dominguez. "Heart Sound Segmentation in Noisy Environments." In Natural and Artificial Models in Computation and Biology, 254–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38637-4_26.

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Conference papers on the topic "Noisy environments"

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Zivic, Natasa. "Information security in noisy environments." In 2013 2nd Mediterranean Conference on Embedded Computing (MECO). IEEE, 2013. http://dx.doi.org/10.1109/meco.2013.6601385.

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Glenn, James. "Evaluation scheduling in noisy environments." In 2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI). IEEE, 2013. http://dx.doi.org/10.1109/foci.2013.6602457.

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Trautmann, Heike, Jorn Mehnen, and Boris Naujoks. "Pareto-dominance in noisy environments." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983338.

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Freire, Izabela, and José Jr. "Gunshot detection in noisy environments." In VII International Telecommunications Symposium. Sociedade Brasileira de Telecomunicações, 2010. http://dx.doi.org/10.14209/sbrt.2010.92.

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Han, Zhongyi, Xian-Jin Gui, Chaoran Cui, and Yilong Yin. "Towards Accurate and Robust Domain Adaptation under Noisy Environments." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/314.

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In non-stationary environments, learning machines usually confront the domain adaptation scenario where the data distribution does change over time. Previous domain adaptation works have achieved great success in theory and practice. However, they always lose robustness in noisy environments where the labels and features of examples from the source domain become corrupted. In this paper, we report our attempt towards achieving accurate noise-robust domain adaptation. We first give a theoretical analysis that reveals how harmful noises influence unsupervised domain adaptation. To eliminate the effect of label noise, we propose an offline curriculum learning for minimizing a newly-defined empirical source risk. To reduce the impact of feature noise, we propose a proxy distribution based margin discrepancy. We seamlessly transform our methods into an adversarial network that performs efficient joint optimization for them, successfully mitigating the negative influence from both data corruption and distribution shift. A series of empirical studies show that our algorithm remarkably outperforms state of the art, over 10% accuracy improvements in some domain adaptation tasks under noisy environments.
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Fookes, G., K. Hawkins, and S. Barnes. "Data Integrity in Noisy Marine Environments." In 59th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 1997. http://dx.doi.org/10.3997/2214-4609-pdb.131.gen1997_b039.

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Bartholomew, J. C., and G. E. Miller. "Voice control for noisy industrial environments." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.95354.

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Socas, Rafael, Sebastian Dormido, and Raquel Dormido. "Event-based controller for noisy environments." In 2014 Second World Conference on Complex Systems (WCCS). IEEE, 2014. http://dx.doi.org/10.1109/icocs.2014.7060977.

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Danahy, Ethan, Sos Agaian, and Karen Panetta. "Detecting Edges in Noisy Multimedia Environments." In Eighth IEEE International Symposium on Multimedia (ISM'06). IEEE, 2006. http://dx.doi.org/10.1109/ism.2006.59.

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Shi, Guangji, and Changxue Ma. "Subband dereverberation algorithm for noisy environments." In 2012 IEEE International Conference on Emerging Signal Processing Applications (ESPA 2012). IEEE, 2012. http://dx.doi.org/10.1109/espa.2012.6152462.

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Reports on the topic "Noisy environments"

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Dykman, Mark, and Lora Billings. Controlling Interacting Systems in Noisy Environments. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada532768.

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Billings, Lora, and Mark Dykman. Controlling Interacting Systems in Noisy Environments. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada518960.

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Plenio, Martin B., and Susana F. Huelga. Employing Noisy Environments to Support Quantum Information Processing. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada426946.

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Scrymgeour, David, Andrew N. Fisher, Calvin Chan, Jason M. Meeks, Daniel Robert Ward, and Craig Y. Nakakura. Localized Electromagnetic Probing for Failure Analysis in Noisy Environments. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1569344.

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Montanari, Andrea. Distributed Matrix Completion: Applications to Cooperative Positioning in Noisy Environments. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada602609.

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Montanari, Andrea. Distributed Matrix Completion: Application to Cooperative Positioning in Noisy Environments. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada595375.

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Lyons, Damiam, Ronald Arkin, Stephen Fox, Shu Jiang, Prem Nirmal, and Munzir Zafar. Characterizing Performance Guarantees for Multiagent, Real-Time Systems Operating in Noisy and Uncertain Environments. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada558875.

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Arrieta, Rudy, Connie Minish, Donal Myrick, and Larry McGlothlin. Residual Noise Environment. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada406626.

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Warren, Daniel E. Low Noise Measurements in an RF Environment. Fort Belvoir, VA: Defense Technical Information Center, August 1993. http://dx.doi.org/10.21236/ada273813.

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Candy, J. V., S. N. Franco, M. C. Emmons, I. M. Lopez, and R. A. Fellini. MODAL CLASSIFICATION: Processing and Anomaly Detection in a Noisy Vibrational Environment. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1497270.

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