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Artykuły w czasopismach na temat „Noisy environments”

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Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 19 lutego 2022

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1

Qian, Chao, Yang Yu i Zhi-Hua Zhou. "Analyzing Evolutionary Optimization in Noisy Environments". Evolutionary Computation 26, nr 1 (marzec 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 i B. VASANTHA LAKSHMI. "NOISE ROBUST SPEECH RECOGNITION UNDER NOISY ENVIRONMENTS". i-manager’s Journal on Pattern Recognition 7, nr 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 i Quang-Vinh Thai. "Eigennoise Speech Recovery in Adverse Environments with Joint Compensation of Additive and Convolutive Noise". Advances in Acoustics and Vibration 2015 (3.11.2015): 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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4

Chen, Bin, Jinbao Long, Hongtai Xie, Chenyang Li, Luokan Chen, Bonan Jiang i Shuai Chen. "Portable atomic gravimeter operating in noisy urban environments". Chinese Optics Letters 18, nr 9 (2020): 090201. http://dx.doi.org/10.3788/col202018.090201.

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5

Cucker, Felipe, i Ernesto Mordecki. "Flocking in noisy environments". Journal de Mathématiques Pures et Appliquées 89, nr 3 (marzec 2008): 278–96. http://dx.doi.org/10.1016/j.matpur.2007.12.002.

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6

Zhang, Lu, Mingjiang Wang, Qiquan Zhang i Ming Liu. "Environmental Attention-Guided Branchy Neural Network for Speech Enhancement". Applied Sciences 10, nr 3 (9.02.2020): 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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7

Liu, Shi Lin, i Zheng Pei. "Voice Activity Based on Noise Estimation in Noisy Environments". Applied Mechanics and Materials 239-240 (grudzień 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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8

Zhao, Li Hua, i Xue Qing Xu. "Endpiont Detection in Noisy Speech Signal Using Teager Energy Entropy". Advanced Materials Research 926-930 (maj 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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9

Nidhyananthan, S. Selva, R. Shantha Selva Kumari i A. Arun Prakash. "A review on speech enhancement algorithms and why to combine with environment classification". International Journal of Modern Physics C 25, nr 10 (11.09.2014): 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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10

Tang, Qiang, De Xiang Zhang i Qing Yan. "Speech Stream Detection for Noisy Environments Based on Empirical Mode Decomposition". Applied Mechanics and Materials 397-400 (wrzesień 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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11

ISHIGAMI, HIROMICHI. "Gap detection in noisy environments." AUDIOLOGY JAPAN 32, nr 5 (1989): 693–94. http://dx.doi.org/10.4295/audiology.32.693.

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12

Carroll, T. L., i F. J. Rachford. "Chaotic sequences for noisy environments". Chaos: An Interdisciplinary Journal of Nonlinear Science 26, nr 10 (październik 2016): 103104. http://dx.doi.org/10.1063/1.4964348.

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13

Edelmann, Geoffrey F., Charles F. Gaumond i Jeffrey S. Rogers. "Compressive beamforming in noisy environments". Journal of the Acoustical Society of America 135, nr 4 (kwiecień 2014): 2392. http://dx.doi.org/10.1121/1.4877912.

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14

Newton, N. J. "Data synchronization and noisy environments". IEEE Transactions on Information Theory 48, nr 8 (sierpień 2002): 2253–62. http://dx.doi.org/10.1109/tit.2002.800476.

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15

Scheel, S., D. G. Welsch i and T. Opatrný. "Quantum Teleportation in Noisy Environments". Fortschritte der Physik 49, nr 10-11 (październik 2001): 1089. http://dx.doi.org/10.1002/1521-3978(200110)49:10/11<1089::aid-prop1089>3.0.co;2-l.

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16

Fitzpatrick, J. Michael, i John J. Grefenstette. "Genetic algorithms in noisy environments". Machine Learning 3, nr 2-3 (październik 1988): 101–20. http://dx.doi.org/10.1007/bf00113893.

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17

Gaydos, Steven. "Clinical Auscultation in Noisy Environments". Journal of Emergency Medicine 43, nr 3 (wrzesień 2012): 492–93. http://dx.doi.org/10.1016/j.jemermed.2011.07.039.

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Matsumoto, Y., T. Asakura, Tokyo University of Science, Faculty of Scien, Tokyo University of Science, Faculty of Scien, Tokyo University of Science, Faculty of Scien, Tokyo University of Science, Faculty of Scien, Tokyo University of Science, Faculty of Scien i Tokyo University of Science, Faculty of Scien. "Case study: Experimental study on optimum level of synthetic speech simulating Lombard effect". Noise Control Engineering Journal 68, nr 4 (1.07.2020): 257–68. http://dx.doi.org/10.3397/1/376822.

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The Lombard effect is an acoustic effect associated with human speech in noisy environments. By simulating the Lombard effect in synthetic speech, the intelligibil- ity of synthetic speech in noisy environments is improved. However, it is known that human speech characterized by the Lombard effect increases annoyance in addition to intelligibility. Therefore, the influence of the Lombard effect on synthetic speech in a noisy environment was examined in detail, considering not only intelligibility but also other criteria. It was found that the suitability of speech reproduction in- creased upon using the Lombard effect in a noisy environment. By simulating the Lombard effect in synthetic speech, the intelligibility of synthetic speech in noisy environments is improved. However, it is known that human speech characterized by the Lombard effect increases annoyance in addition to intelligibility. Therefore, the influence of the Lombard effect on synthetic speech in a noisy environment was examined in detail, considering not only intelligibility but also other criteria. It was found that the suitability of speech reproduction increased upon using the Lombard effect in a noisy environment.

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19

Gu, Shanzhi, Mingyang Geng i Long Lan. "Attention-Based Fault-Tolerant Approach for Multi-Agent Reinforcement Learning Systems". Entropy 23, nr 9 (31.08.2021): 1133. http://dx.doi.org/10.3390/e23091133.

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The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. Typically, an agent receives its private observations providing a partial view of the true state of the environment. However, in realistic settings, the harsh environment might cause one or more agents to show arbitrarily faulty or malicious behavior, which may suffice to allow the current coordination mechanisms fail. In this paper, we study a practical scenario of multi-agent reinforcement learning systems considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. The previous state-of-the-art work that coped with extremely noisy environments was designed on the basis that the noise intensity in the environment was known in advance. However, when the noise intensity changes, the existing method has to adjust the configuration of the model to learn in new environments, which limits the practical applications. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) model, which can select not only correct, but also relevant information for each agent at every time step in noisy environments. The multihead attention mechanism enables the agents to learn effective communication policies through experience concurrent with the action policies. Empirical results showed that FT-Attn beats previous state-of-the-art methods in some extremely noisy environments in both cooperative and competitive scenarios, much closer to the upper-bound performance. Furthermore, FT-Attn maintains a more general fault tolerance ability and does not rely on the prior knowledge about the noise intensity of the environment.

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20

JIA, LI-XING, BO LI, R. H. YUE i HENG FAN. "SUDDEN CHANGE OF QUANTUM DISCORD UNDER SINGLE QUBIT NOISE". International Journal of Quantum Information 11, nr 05 (sierpień 2013): 1350048. http://dx.doi.org/10.1142/s0219749913500482.

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We show that the sudden change of quantum correlation can occur even when only one part of the composite entangled state is exposed to a noisy environment. Our results are illustrated through the action of different noisy environments individually on a single qubit of quantum system. Composite noise on the whole of the quantum system is thus not the necessarily condition for the occurrence of sudden transition for quantum correlation.

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21

SUGUNA, C., i SOMDATTA SINHA. "CELLULAR DYNAMICS IN NOISY ENVIRONMENT". Fluctuation and Noise Letters 02, nr 04 (grudzień 2002): L313—L326. http://dx.doi.org/10.1142/s0219477502000889.

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Cells are continuously subjected to noisy internal and external environments. Though cellular functions are carried on quite reliably under such noise, they can also result in fluctuations of parameters of the various biochemical processes and cause the dynamics of the reaction pathway to change. To show the varied effects of noisy environment on cellular dynamics, we consider a model cell containing a minimal biochemical pathway that exhibits a wide array of dynamical behaviors – from equilibrium, simple limit cycles and higher periodics, birhythmicity, complex oscillations and chaos - that are observed in many cellular functions. We show that, even under constant parameters, small fluctuations in the variables (i.e., substrate concentrations) can facilitate switching of the dynamics between oscillatory states with very different amplitudes and frequencies. The final temporal behavior of the pathway is unpredictable for the range of parameters where birhythmicity is observed. Parametric noise can mask the original dynamics of the pathway in the birhythmic state, though considerable robustness in dynamics is seen in the presence of noise for other values of parameters. Thus we show that the cellular dynamics can exhibit both robust and non-robust behavior in noisy environment for different parameters in the same pathway.

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22

Lee, Soojeong, i 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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23

KYEONG SEONG, Woo, Ji HUN PARK i Hong KOOK KIM. "Reducing Speech Noise for Patients with Dysarthria in Noisy Environments". IEICE Transactions on Information and Systems E97.D, nr 11 (2014): 2881–87. http://dx.doi.org/10.1587/transinf.2014edp7130.

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Willmore, Ben D. B., James E. Cooke i Andrew J. King. "Hearing in noisy environments: noise invariance and contrast gain control". Journal of Physiology 592, nr 16 (24.06.2014): 3371–81. http://dx.doi.org/10.1113/jphysiol.2014.274886.

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Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi i De-Min Wang. "α-spectrum estimation for 1/f processes in noisy environments". Noise & Vibration Worldwide 50, nr 2 (luty 2019): 46–55. http://dx.doi.org/10.1177/0957456519827937.

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In the past, 1/ f noise was regarded as a stochastic process that accords with Gaussian distribution. According to our studies, the output transistor 1/ f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear S αS processes and propose a samples normalized cross-correlations–based α-spectrum method effective in noisy environments. Simulation results and diodes noise spectrum estimation results exhibit good performance.

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26

Lowry, Hélène, Alan Lill i Bob Wong. "Do the Calls of a Bird, the Noisy Miner (Manorina melanocephala), Need Adjustment for Efficient Communication in Urban Anthropogenic Noise?" Animals 9, nr 3 (26.03.2019): 118. http://dx.doi.org/10.3390/ani9030118.

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Urban environments are characteristically noisy and this can pose a challenge for animals that communicate acoustically. Although evidence suggests that some birds can make acoustic adjustments that preclude masking of their signals in high-disturbance environments such as cities, studies to date have tended to focus on acoustic signals important in mate attraction (e.g., songs). Far less attention has been given to the impact of urban noise on other kinds of calls. To redress this, we compared a range of different vocalizations (encompassing alarm calls, begging calls and parent response calls) among urban and rural individuals of a successful Australian ‘urban adapter’, the Noisy miner, Manorina melanocephala. We found that urban miners had significantly higher minimum sound frequencies for calls with low base-frequencies (<2 kHz); however, calls with base-frequencies ‘naturally’ above the main frequency range of urban noise (>2 kHz) had the same minimum frequency in urban and rural birds. Dominant frequency and call duration did not differ between urban and rural individuals. Although urban Noisy miners exhibited differences from rural individuals in the minimum frequency of calls, this shift was not large enough to avoid masking from low-frequency, anthropogenic noise. Nevertheless, our findings suggest that the calls of Noisy miners may be naturally well suited to being heard in noisy urban environments by having (a) dominant frequencies higher than low-level, anthropogenic noise and (b) several important call-types with frequencies above the main frequency range associated with urban noise.

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27

MAEDA, CHIKAKO. "Articulation of speech in noisy environments." AUDIOLOGY JAPAN 32, nr 5 (1989): 443–44. http://dx.doi.org/10.4295/audiology.32.443.

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28

Wee, Jinwoo. "Robust Speaker Verification in Noisy Environments". Korean Society of Technical Education and Training 24, nr 4 (31.12.2019): 53–60. http://dx.doi.org/10.29279/kostet.2019.24.4.53.

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Ma, Haiping, Minrui Fei, Dan Simon i Zixiang Chen. "Biogeography-based optimization in noisy environments". Transactions of the Institute of Measurement and Control 37, nr 2 (23.06.2014): 190–204. http://dx.doi.org/10.1177/0142331214537015.

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Kingdon, Jonathan. "Primate Visual Signals in Noisy Environments". Folia Primatologica 78, nr 5-6 (2007): 389–404. http://dx.doi.org/10.1159/000105151.

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Bergadano, F., A. Giordana i L. Saitta. "Automated concept acquisition in noisy environments". IEEE Transactions on Pattern Analysis and Machine Intelligence 10, nr 4 (lipiec 1988): 555–78. http://dx.doi.org/10.1109/34.3917.

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Varela, Oscar, Ruben San-Segundo i Luis A. Hernandez. "Robust speech detection for noisy environments". IEEE Aerospace and Electronic Systems Magazine 26, nr 11 (listopad 2011): 16–23. http://dx.doi.org/10.1109/maes.2011.6070277.

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Molnar-Matei, Florin. "Voltage Dip Detection in Noisy Environments Using Tracking Filters". Advanced Engineering Forum 8-9 (czerwiec 2013): 139–46. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.139.

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Due of its significant economical impact, voltage dip analysis is an important domain today. Monitoring these phenomena (also called sags) require powerful techniques. Digital Signal Processing (DSP) algorithms are currently employed to fulfill this task. One of the possible algorithms is the αβγ filter. Discovered and tested during the cold war, this algorithm can estimate a parameter of interest one-step-ahead based on the previously observed values. In previous work, this filter revealed very good performance in the absence of noise. However, noise is present in every technical environment. This paper examines the dip detection performance using the above mentioned tracking filter in a noisy environment.

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Habeeb, Imad Qasim, Tamara Z. Fadhil, Yaseen Naser Jurn, Zeyad Qasim Habeeb i Hanan Najm Abdulkhudhur. "An ensemble technique for speech recognition in noisy environments". Indonesian Journal of Electrical Engineering and Computer Science 18, nr 2 (1.05.2020): 835. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp835-842.

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<span>Automatic speech recognition (ASR) is a technology that allows a computer and mobile device to recognize and translate spoken language into text. ASR systems often produce poor accuracy for the noisy speech signal. Therefore, this research proposed an ensemble technique that does not rely on a single filter for perfect noise reduction but incorporates information from multiple noise reduction filters to improve the final ASR accuracy. The main factor of this technique is the generation of K-copies of the speech signal using three noise reduction filters. The speech features of these copies differ slightly in order to extract different texts from them when processed by the ASR system. Thus, the best among these texts can be elected as final ASR output. The ensemble technique was compared with three related current noise reduction techniques in terms of CER and WER. The test results were encouraging and showed a relatively decreased by 16.61% and 11.54% on CER and WER compared with the best current technique. ASR field will benefit from the contribution of this research to increase the recognition accuracy of a human speech in the presence of background noise.</span>

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Yang, Jie. "Combining Speech Enhancement and Cepstral Mean Normalization for LPC Cepstral Coefficients". Key Engineering Materials 474-476 (kwiecień 2011): 349–54. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.349.

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A mismatch between the training and testing in noisy circumstance often causes a drastic decrease in the performance of speech recognition system. The robust feature coefficients might suppress this sensitivity of mismatch during the recognition stage. In this paper, we investigate the noise robustness of LPC Cepstral Coefficients (LPCC) by using speech enhancement with feature post-processing. At front-end, speech enhancement in the wavelet domain is used to remove noise components from noisy signals. This enhanced processing adopts the combination of discrete wavelet transform (DWT), wavelet packet decomposition (WPD), multi-thresholds processing etc to obtain the estimated speech. The feature post-processing employs cepstral mean normalization (CMN) to compensate the signal distortion and residual noise of enhanced signals in the cepstral domain. The performance of digit speech recognition systems is evaluated under noisy environments based on NOISEX-92 database. The experimental results show that the presented method exhibits performance improvements in the adverse noise environment compared with the previous features.

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Seper, Eric, Francis Kuk, Petri Korhonen i Christopher Slugocki. "Tracking of Noise Tolerance to Predict Hearing Aid Satisfaction in Loud Noisy Environments". Journal of the American Academy of Audiology 30, nr 04 (kwiecień 2019): 302–14. http://dx.doi.org/10.3766/jaaa.17101.

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AbstractA method that tracked tolerable noise level (TNL) over time while maintaining subjective speech intelligibility was reported previously. Although this method was reliable and efficacious as a research tool, its clinical efficacy and predictive ability of real-life hearing aid satisfaction were not measured.The study evaluated an adaptive method to estimate TNL using slope and variance of tracked noise level as criteria in a clinical setting. The relationship between TNL and subjective hearing aid satisfaction in noisy environments was also investigated.A single-blinded, repeated-measures design.Seventeen experienced hearing aid wearers with bilateral mild-to-moderately-severe sensorineural hearing loss.Participants listened to 82-dB SPL continuous speech and tracked the background noise level that they could “put up with” while subjectively understanding >90% of the speech material. Two trials with each babble noise and continuous speech-shaped noise were measured in a single session. All four trials were completed aided using the participants’ own hearing aids. The stimuli were presented in the sound field with speech from 0° and noise from the 180° azimuth. The instantaneous tolerable noise level was measured using a custom program and scored in two ways; the averaged TNL (aTNL) over the 2-min trial and the estimated TNL (eTNL) as soon as the listeners reached a stable noise estimate. Correlation between TNL and proportion of satisfied noisy environments was examined using the MarkeTrak questionnaire.All listeners completed the tracking of noise tolerance procedure within 2 min with good reliability. Sixty-five percent of the listeners yielded a stable noise estimate after 59.9 sec of actual test time. The eTNL for all trials was 78.6 dB SPL (standard deviation [SD] = 4.4 dB). The aTNL for all trials was 78.0 dB SPL (SD = 3.3 dB) after 120 sec. The aTNL was 79.2 dB SPL (SD = 5.4 dB) for babble noise and 77.0 dB SPL (SD = 5.9 dB) for speech-shaped noise. High within-session test–retest reliability was evident. The 95% confidence interval was 1.5 dB for babble noise and 2.8 dB for continuous speech-shaped noise. No significant correlation was measured between overall hearing aid satisfaction and the aTNL (ρ = 0.20 for both noises); however, a significant relationship between aTNL and proportion of satisfied noisy situations was evident (ρ = 0.48 for babble noise and ρ = 0.55 for speech-shaped noise).The eTNL scoring method yielded similar results as the aTNL method although requiring only half the time for 65% of the listeners. This time efficiency, along with its reliability and the potential relationship between TNL and hearing aid satisfaction in noisy listening situations suggests that this procedure may be a good clinical tool to evaluate whether specific features on a hearing aid would improve noise tolerance and predict wearer satisfaction with the selected hearing aid in real-life loud noisy situations. A larger sample of hearing aid wearers is needed to further validate these potential uses.

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Qian, Chao, Yang Yu, Ke Tang, Yaochu Jin, Xin Yao i Zhi-Hua Zhou. "On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments". Evolutionary Computation 26, nr 2 (czerwiec 2018): 237–67. http://dx.doi.org/10.1162/evco_a_00201.

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In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness of a solution, it evaluates the fitness multiple ([Formula: see text]) times independently and then uses the sample average to approximate the true fitness. Obviously, sampling can make the fitness estimation closer to the true value, but also increases the estimation cost. Previous studies mainly focused on empirical analysis and design of efficient sampling strategies, while the impact of sampling is unclear from a theoretical viewpoint. In this article, we show that sampling can speed up noisy evolutionary optimization exponentially via rigorous running time analysis. For the (1[Formula: see text]1)-EA solving the OneMax and the LeadingOnes problems under prior (e.g., one-bit) or posterior (e.g., additive Gaussian) noise, we prove that, under a high noise level, the running time can be reduced from exponential to polynomial by sampling. The analysis also shows that a gap of one on the value of [Formula: see text] for sampling can lead to an exponential difference on the expected running time, cautioning for a careful selection of [Formula: see text]. We further prove by using two illustrative examples that sampling can be more effective for noise handling than parent populations and threshold selection, two strategies that have shown to be robust to noise. Finally, we also show that sampling can be ineffective when noise does not bring a negative impact.

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Kathania, Hemant Kumar, Sudarsana Reddy Kadiri, Paavo Alku i Mikko Kurimo. "Using Data Augmentation and Time-Scale Modification to Improve ASR of Children’s Speech in Noisy Environments". Applied Sciences 11, nr 18 (10.09.2021): 8420. http://dx.doi.org/10.3390/app11188420.

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Current ASR systems show poor performance in recognition of children’s speech in noisy environments because recognizers are typically trained with clean adults’ speech and therefore there are two mismatches between training and testing phases (i.e., clean speech in training vs. noisy speech in testing and adult speech in training vs. child speech in testing). This article studies methods to tackle the effects of these two mismatches in recognition of noisy children’s speech by investigating two techniques: data augmentation and time-scale modification. In the former, clean training data of adult speakers are corrupted with additive noise in order to obtain training data that better correspond to the noisy testing conditions. In the latter, the fundamental frequency (F0) and speaking rate of children’s speech are modified in the testing phase in order to reduce differences in the prosodic characteristics between the testing data of child speakers and the training data of adult speakers. A standard ASR system based on DNN–HMM was built and the effects of data augmentation, F0 modification, and speaking rate modification on word error rate (WER) were evaluated first separately and then by combining all three techniques. The experiments were conducted using children’s speech corrupted with additive noise of four different noise types in four different signal-to-noise (SNR) categories. The results show that the combination of all three techniques yielded the best ASR performance. As an example, the WER value averaged over all four noise types in the SNR category of 5 dB dropped from 32.30% to 12.09% when the baseline system, in which no data augmentation or time-scale modification were used, was replaced with a recognizer that was built using a combination of all three techniques. In summary, in recognizing noisy children’s speech with ASR systems trained with clean adult speech, considerable improvements in the recognition performance can be achieved by combining data augmentation based on noise addition in the system training phase and time-scale modification based on modifying F0 and speaking rate of children’s speech in the testing phase.

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Abu-El-Quran, Ahmad R., Adrian D. C. Chan i Rafik A. Goubran. "Multiengine Speech Processing Using SNR Estimator in Variable Noisy Environments". Advances in Acoustics and Vibration 2012 (8.02.2012): 1–10. http://dx.doi.org/10.1155/2012/172625.

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We introduce a multiengine speech processing system that can detect the location and the type of audio signal in variable noisy environments. This system detects the location of the audio source using a microphone array; the system examines the audio first, determines if it is speech/nonspeech, then estimates the value of the signal to noise (SNR) using a Discrete-Valued SNR Estimator. Using this SNR value, instead of trying to adapt the speech signal to the speech processing system, we adapt the speech processing system to the surrounding environment of the captured speech signal. In this paper, we introduced the Discrete-Valued SNR Estimator and a multiengine classifier, using Multiengine Selection or Multiengine Weighted Fusion. Also we use the SI as example of the speech processing. The Discrete-Valued SNR Estimator achieves an accuracy of 98.4% in characterizing the environment's SNR. Compared to a conventional single engine SI system, the improvement in accuracy was as high as 9.0% and 10.0% for the Multiengine Selection and Multiengine Weighted Fusion, respectively.

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Zhang, Junqi, Xixun Zhu, Yuheng Wang i MengChu Zhou. "Dual-Environmental Particle Swarm Optimizer in Noisy and Noise-Free Environments". IEEE Transactions on Cybernetics 49, nr 6 (czerwiec 2019): 2011–21. http://dx.doi.org/10.1109/tcyb.2018.2817020.

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Li, Jiakang, Xiongwei Zhang, Meng Sun, Xia Zou i Changyan Zheng. "Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments". Applied Sciences 9, nr 8 (13.04.2019): 1539. http://dx.doi.org/10.3390/app9081539.

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Even though audio replay detection has improved in recent years, its performance is known to severely deteriorate with the existence of strong background noises. Given the fact that different frames of an utterance have different impacts on the performance of spoofing detection, this paper introduces attention-based long short-term memory (LSTM) to extract representative frames for spoofing detection in noisy environments. With this attention mechanism, the specific and representative frame-level features will be automatically selected by adjusting their weights in the framework of attention-based LSTM. The experiments, conducted using the ASVspoof 2017 dataset version 2.0, show that the equal error rate (EER) of the proposed approach was about 13% lower than the constant Q cepstral coefficients-Gaussian mixture model (CQCC-GMM) baseline in noisy environments with four different signal-to-noise ratios (SNR). Meanwhile, the proposed algorithm also improved the performance of traditional LSTM on audio replay detection systems in noisy environments. Experiments using bagging with different frame lengths were also conducted to further improve the proposed approach.

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Chen, Zi Qin, De Xiang Zhang i Da Ling Yuan. "Speech Enhancement Based on EMD and Wavelet Threshold in Noisy Environments". Advanced Materials Research 989-994 (lipiec 2014): 3654–57. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3654.

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Speech enhancement is crucial for speech recognition accuracy. How to eliminate the effect of the noise constitutes a challenging problem in speech processing. This paper presents a new technique for speech enhancement in a noisy environment based on the empirical mode decomposition (EMD) algorithm and wavelet threshold. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then wavelet threshold of the IMF components can be used to eliminate the effect of the noise for speech enhancement. Experimental results show that the proposed speech enhancement by de-noising algorithm is possible to achieve an excellent balance between suppresses noise effectively and preserves as many target characteristics of original signal as possible.

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Ueng, Shyh-Kuang, Cheng-Ming Luo, Tsung-Yu Tsai i Hsuan-Chen Yeh. "Human voice quality measurement in noisy environments". Technology and Health Care 24, s1 (8.12.2015): S313—S324. http://dx.doi.org/10.3233/thc-151089.

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Knyaz, I. A. "Small Capacitance Josephson Junction in Noisy Environments". Journal of Nano- and Electronic Physics 10, nr 2 (2018): 02037–1. http://dx.doi.org/10.21272/jnep.10(2).02037.

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Eskandari, Hamidreza, i Christopher D. Geiger. "Evolutionary multiobjective optimization in noisy problem environments". Journal of Heuristics 15, nr 6 (3.06.2008): 559–95. http://dx.doi.org/10.1007/s10732-008-9077-z.

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Fortuna, Luigi, Mattia Frasca, Manuela La Rosa i Alessandro Spata. "Dynamics of neuron populations in noisy environments". Chaos: An Interdisciplinary Journal of Nonlinear Science 15, nr 1 (marzec 2005): 014102. http://dx.doi.org/10.1063/1.1865372.

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Yuan, Chengzhi, i Cong Wang. "Performance of deterministic learning in noisy environments". Neurocomputing 78, nr 1 (luty 2012): 72–82. http://dx.doi.org/10.1016/j.neucom.2011.05.037.

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West, James E., G. W. Elko, D. R. Morgan i R. A. Kubli. "Position‐tolerant differential microphones for noisy environments". Journal of the Acoustical Society of America 96, nr 5 (listopad 1994): 3262. http://dx.doi.org/10.1121/1.411009.

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Cheng, P. S., S. H. Billis, H. Heskiaoff i J. F. Y. Cheung. "Directional line detectors in correlated noisy environments". IEEE Transactions on Image Processing 9, nr 12 (2000): 2061–70. http://dx.doi.org/10.1109/83.887974.

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Moreira, A. A., A. Mathur, D. Diermeier i L. A. N. Amaral. "Efficient system-wide coordination in noisy environments". Proceedings of the National Academy of Sciences 101, nr 33 (5.08.2004): 12085–90. http://dx.doi.org/10.1073/pnas.0400672101.

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