Journal articles on the topic 'Noise'
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1
Yang, Ren Di, and Yan Li Zhang. "Denoising of ECG Signal Based on Empirical Mode Decomposition and Adaptive Noise Cancellation." Applied Mechanics and Materials 40-41 (November 2010): 140–45. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.140.
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To remove the noises in ECG and to overcome the disadvantage of the denoising method only based on empirical mode decomposition (EMD), a combination of EMD and adaptive noise cancellation is introduced in this paper. The noisy ECG signals are firstly decomposed into intrinsic mode functions (IMFs) by EMD. Then the IMFs corresponding to noises are used to reconstruct signal. The reconstructed signal as the reference input of adaptive noise cancellation and the noisy ECG as the basic input, the de-noised ECG signal is obtained after adaptive filtering. The de-noised ECG has high signal-to-noise ratio, preferable correlation coefficient and lower mean square error. Through analyzing these performance parameters and testing the denoising method using MIT-BIH Database, the conclusion can be drawn that the combination of EMD and adaptive noise cancellation has considered the frequency distribution of ECG and noises, eliminate the noises effectively and need not to select a proper threshold.
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S. Ashwin, J., and N. Manoharan. "Audio Denoising Based on Short Time Fourier Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (January 1, 2018): 89. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp89-92.
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<p>This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio de-noising technique based on Short Time Fourier Transform (STFT) is implemented. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. Here original speech signals are given as input signal. Using AWGN, noises are added to the signal. Then noised signals are de-noised using STFT techniques. Finally Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR) values for noised and de-noised signals are obtained.</p>
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Li, Yongsong, Zhengzhou Li, Kai Wei, Weiqi Xiong, Jiangpeng Yu, and Bo Qi. "Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation." Sensors 19, no. 2 (January 16, 2019): 339. http://dx.doi.org/10.3390/s19020339.
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Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.
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Farshi, Taymaz Rahkar. "Image Noise Reduction Method Based on Compatibility with Adjacent Pixels." International Journal of Image and Graphics 17, no. 03 (July 2017): 1750014. http://dx.doi.org/10.1142/s0219467817500140.
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This paper proposes an efficient noise reduction method for gray and color images that are contaminated by salt-and-pepper noise. In the proposed method, the pixels that are more compatible with adjacent pixels are replaced with target (noisy) pixels. The algorithm is applied on noisy Lena and Mansion images that are contaminated by salt-and-pepper noise with 0.1 and 0.2 noise intensities. Although this method is developed for reducing noise from the images that are contaminated by salt-and-pepper noise, it can also reduce the noise from the images that are contaminated by other types of noises; yet it is more efficient for reducing salt-and-pepper noise. Both numerical and visual comparisons are demonstrated in the experimental simulations. The results show the proposed algorithm can successfully remove impulse noise from images that are contaminated by salt-and-pepper noise.
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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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Wang, Runjie, Wenzhong Shi, Xianglei Liu, and Zhiyuan Li. "An Adaptive Cutoff Frequency Selection Approach for Fast Fourier Transform Method and Its Application into Short-Term Traffic Flow Forecasting." ISPRS International Journal of Geo-Information 9, no. 12 (December 7, 2020): 731. http://dx.doi.org/10.3390/ijgi9120731.
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Historical measurements are usually used to build assimilation models in sequential data assimilation (S-DA) systems. However, they are always disturbed by local noises. Simultaneously, the accuracy of assimilation model construction and assimilation forecasting results will be affected. The fast Fourier transform (FFT) method can be used to acquire de-noised historical traffic flow measurements to reduce the influence of local noises on constructed assimilation models and improve the accuracy of assimilation results. In the practical signal de-noising applications, the FFT method is commonly used to de-noise the noisy signal with known noise frequency. However, knowing the noise frequency is difficult. Thus, a proper cutoff frequency should be chosen to separate high-frequency information caused by noises from the low-frequency part of useful signals under the unknown noise frequency. If the cutoff frequency is too high, too much noisy information will be treated as useful information. Conversely, if the cutoff frequency is too low, part of the useful information will be lost. To solve this problem, this paper proposes an adaptive cutoff frequency selection (A-CFS) method based on cross-validation. The proposed method can determine a proper cutoff frequency and ensure the quality of de-noised outputs for a given dataset using the FFT method without noise frequency information. Experimental results of real-world traffic flow data measurements in a sub-area of a highway near Birmingham, England, demonstrate the superior performance of the proposed A-CFS method in noisy information separation using the FFT method. The differences between true and predicted traffic flow values are evaluated using the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage (MAPE) values. Compared to the results of the two commonly used de-noising methods, i.e., discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) methods, the short-term traffic flow forecasting results of the proposed A-CFS method are much more reliable. In terms of the MAE value, the average relative improvements of the assimilation model built using the proposed method are 19.26%, 3.47%, and 4.25%, compared to the model built using raw data, DWT method, and EEMD method, respectively; the corresponding average relative improvements in RMSE are 19.05%, 5.36%, and 3.02%, respectively; lastly, the corresponding average relative improvements in MAPE are 18.88%, 2.83%, and 2.28%, respectively. The test results show that the proposed method is effective in separating noises from historical measurements and can improve the accuracy of assimilation model construction and assimilation forecasting results.
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Lingamaiah Kurva, Naga, and S. Varadarajan. "Dual tree complex wavelet transform based image denoising for Kalpana satellite images." International Journal of Engineering & Technology 7, no. 3.29 (August 24, 2018): 269. http://dx.doi.org/10.14419/ijet.v7i3.29.18810.
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This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and Salt & Pepper noise. Finally the performance of the DTCWT wavelet measures in terms of Peak Signal to Noise Ratio and Structural Similarity Index for both noisy & denoised Kalpana images.
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Wu, Xiao Hong, Bin Wu, and Jie Wen Zhao. "Noise Fuzzy Learning Vector Quantization." Key Engineering Materials 439-440 (June 2010): 367–71. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.367.
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Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it is a FCM-based algorithm (FCM is sensitive to noises). Here, a new fuzzy learning vector quantization model, called noise fuzzy learning vector quantization (NFLVQ), is proposed to handle the noises sensitivity problem of FLVQ. NFLVQ integrates LVQ and generalized noise clustering (GNC), uses the membership values from GNC as learning rates and clusters data containing noisy data better than FLVQ. Experimental results show the better performances of NFLVQ.
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Oh, Soo Hee, and Kyoungwon Lee. "Aircraft Noise of Airport Community in Korea." Audiology and Speech Research 16, no. 1 (January 31, 2020): 1–10. http://dx.doi.org/10.21848/asr.200001.
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Aircraft noise is one of the serious environmental noises with the increased use of flight traffic. The purpose of this study is to understand aircraft noise levels of airport communities in Korea using baseline data for audiologic management. Aircraft noise levels were retrieved from the National Noise Information System every month between 2004 and 2018. We reviewed aircraft noise levels obtained from total of 111 airport communities across 14 airports. In order to understand aircraft noise levels of civil and military airports, the aircraft noise levels measured in civil and military airport communities compared with the noise levels from civil airport communities. The data showed average 71-73 weight equivalent continuous perceived noise level (WECPNL) for fifteen years across airport cities and the average noise levels did not increase over time between 2004 and 2018 years. The civil and military airports showed about 12 WECPNLs of increased noise levels compared to the civil airports. The most civil and military airport communities, including Gwangju, Gunsan, Daegu, Wonju, and Cheongju generated the maximum noise levels and ranked as the highest airport for aircraft noise levels. Although aircraft noise levels in airport communities were similar over the past decade, civil and military airports generated increased noised levels compared to civil airports due to jet plane noises and other military-related noises. Careful consideration is necessary to implement noise reduction policy for civil and military airport communities. Ongoing noise control, hearing monitoring, education, and relevant policies are required to improve the quality of life in the airport community residences.
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Zhang, Jiangbo, and Yiyi Zhao. "The Robust Consensus of a Noisy Deffuant-Weisbuch Model." Mathematical Problems in Engineering 2018 (December 30, 2018): 1–10. http://dx.doi.org/10.1155/2018/1065451.
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We construct a new opinion formation of the Deffuant-Weisbuch model with the interference of the outer noise, where there are finite n agents and the evolution is discrete-time. The opinion interaction occurs by one randomly chosen pair at each time step. The difference to the original Deffuant-Weisbuch model is that communications of any selected pairs will be affected by noises. The aim of this paper is to study the robust consensus of this noisy Deffuant-Weisbuch model. We first define the noise strength as the maximum noise absolute value. We will then show that when the noise strength is less than a certain threshold, this noisy model will achieve T-robust consensus when t is sufficiently large; next we prove that the noisy model achieves robust consensus with a positive probability; finally, we demonstrate these results and provide numerical relations among the noise strength and some model parameters.
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Selvaraj, Poovarasan, and E. Chandra. "A variant of SWEMDH technique based on variational mode decomposition for speech enhancement." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 3 (November 10, 2021): 299–308. http://dx.doi.org/10.3233/kes-210072.
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In Speech Enhancement (SE) techniques, the major challenging task is to suppress non-stationary noises including white noise in real-time application scenarios. Many techniques have been developed for enhancing the vocal signals; however, those were not effective for suppressing non-stationary noises very well. Also, those have high time and resource consumption. As a result, Sliding Window Empirical Mode Decomposition and Hurst (SWEMDH)-based SE method where the speech signal was decomposed into Intrinsic Mode Functions (IMFs) based on the sliding window and the noise factor in each IMF was chosen based on the Hurst exponent data. Also, the least corrupted IMFs were utilized to restore the vocal signal. However, this technique was not suitable for white noise scenarios. Therefore in this paper, a Variant of Variational Mode Decomposition (VVMD) with SWEMDH technique is proposed to reduce the complexity in real-time applications. The key objective of this proposed SWEMD-VVMDH technique is to decide the IMFs based on Hurst exponent and then apply the VVMD technique to suppress both low- and high-frequency noisy factors from the vocal signals. Originally, the noisy vocal signal is decomposed into many IMFs using SWEMDH technique. Then, Hurst exponent is computed to decide the IMFs with low-frequency noisy factors and Narrow-Band Components (NBC) is computed to decide the IMFs with high-frequency noisy factors. Moreover, VVMD is applied on the addition of all chosen IMF to remove both low- and high-frequency noisy factors. Thus, the speech signal quality is improved under non-stationary noises including additive white Gaussian noise. Finally, the experimental outcomes demonstrate the significant speech signal improvement under both non-stationary and white noise surroundings.
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Alokaily, Ahmad O., Abdulaziz F. Alqabbani, Adham Aleid, and Khalid Alhussaini. "Toward Accessible Hearing Care: The Development of a Versatile Arabic Word-in-Noise Screening Tool: A Pilot Study." Applied Sciences 12, no. 23 (December 6, 2022): 12459. http://dx.doi.org/10.3390/app122312459.
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Speech-in-noise tests are used to assess the ability of the human auditory system to perceive speech in a noisy environment. Early diagnosis of hearing deficits helps health professionals to plan for the most appropriate management. However, hospitals and auditory clinics have a shortage of reliable Arabic versions of speech-in-noise tests. Additionally, access to specialized healthcare facilities is associated with socioeconomic status. Hence, individuals with compromised socioeconomic status do not have proper access to healthcare. Thus, In the current study, a mobile and cost-effective Arabic speech-in-noise test was developed and tested on 30 normal-hearing subjects, and their ability to perceive words-in-noise was evaluated. Moreover, a comparison between two different background noises was explored (multi-talker babble noise and white noise). The results revealed a significant difference in the thresholds between the two types of background noises. The percent-correct scores ranged from 100% to 54.17% for the white background noise and 91.57% to 50% for the multi-talker babble background noise. The proposed Arabic word-in-noise screening tool has the potential to be used effectively to screen for deteriorated speech perception abilities, particularly in low-resource settings.
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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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TAI, CHENG-CHI, CHIH-HSING CHANG, CHUAN-CHING TAN, TSUNG-WEN HUANG, and CHING-CHAU SU. "ADAPTIVE BEAMFORMER WITH COMBINATION OF SUBBAND FILTERING FOR HEARING-AID SYSTEMS BACKGROUND NOISE REDUCTION." Biomedical Engineering: Applications, Basis and Communications 14, no. 02 (April 25, 2002): 55–66. http://dx.doi.org/10.4015/s1016237202000097.
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In this paper, we present a noise reduction technique for hearing-aid systems. The proposed algorithm adopted adaptive beamformer with combination of subband filtering technique. The structure of conventional hearing aids is relatively simple. They amplify ambient sounds that include speech signal as well as noise. Because noise and human speech signal are amplified at the same time, hearing-aid users can't clearly hear speech signal in noisy environment. The direction of sound can be used to discriminate speech signal from noise by combining adaptive noise canceller and adaptive beamformer. We have developed a system that based on the constrained adaptive noise canceller to preserve speech signal from straight ahead and minimize background noise arriving from other directions. This system also uses subband filtering technique to reduce the requirement for computation and enhance the flexibility of the system. The performance of this system is illustrated using simulated and real-world noises. The results show that the developed system can reserve the right ahead speech signal and substantially reject noises from other directions.
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Pan, Mei-Sen, Jing-Tian Tang, and Xiao-Li Yang. "A MODIFIED ADAPTIVE MEDIAN FILTER METHOD AND ITS APPLICATIONS IN MEDICAL IMAGES." Biomedical Engineering: Applications, Basis and Communications 22, no. 06 (December 2010): 489–96. http://dx.doi.org/10.4015/s1016237210002237.
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Since the medical image is usually corrupted by noise, the filter method is applied to remove the noise and improve the image quality. In this paper, a modified adaptive median filter method is proposed for filtering the medical images. When identifying noises, by selecting the maximum and the minimum gray values in the image as a criterion of judging the noise pixels, the probability that a nonnoise pixel is misjudged to be a noisy one is reduced, and the processing time for finding the maximum and minimum gray values in each local window is drastically decreased as well. When filtering the image, according to the noise granularity function (NGF) in a 3×3 window, the filtering window size is adaptively adjusted, then the median filter is used to eliminate the current noise-marked pixel in the median image (MI) generated by the adaptive median filter, and at the same time the noise mark is cancelled. The proposed method may both effectively remove the noises, and preserve image detail information well. The experimental results reveal that the proposed method is particularly effective in filtering the impulse noises, also called salt-and-pepper noises superimposed on images, including computed tomography (CT) and magnetic resonance (MR) images.
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Cherukuru, Pavani, and Mumtaz Begum Mustafa. "CNN-based noise reduction for multi-channel speech enhancement system with discrete wavelet transform (DWT) preprocessing." PeerJ Computer Science 10 (February 28, 2024): e1901. http://dx.doi.org/10.7717/peerj-cs.1901.
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Speech enhancement algorithms are applied in multiple levels of enhancement to improve the quality of speech signals under noisy environments known as multi-channel speech enhancement (MCSE) systems. Numerous existing algorithms are used to filter noise in speech enhancement systems, which are typically employed as a pre-processor to reduce noise and improve speech quality. They may, however, be limited in performing well under low signal-to-noise ratio (SNR) situations. The speech devices are exposed to all kinds of environmental noises which may go up to a high-level frequency of noises. The objective of this research is to conduct a noise reduction experiment for a multi-channel speech enhancement (MCSE) system in stationary and non-stationary environmental noisy situations with varying speech signal SNR levels. The experiments examined the performance of the existing and the proposed MCSE systems for environmental noises in filtering low to high SNRs environmental noises (−10 dB to 20 dB). The experiments were conducted using the AURORA and LibriSpeech datasets, which consist of different types of environmental noises. The existing MCSE (BAV-MCSE) makes use of beamforming, adaptive noise reduction and voice activity detection algorithms (BAV) to filter the noises from speech signals. The proposed MCSE (DWT-CNN-MCSE) system was developed based on discrete wavelet transform (DWT) preprocessing and convolution neural network (CNN) for denoising the input noisy speech signals to improve the performance accuracy. The performance of the existing BAV-MCSE and the proposed DWT-CNN-MCSE were measured using spectrogram analysis and word recognition rate (WRR). It was identified that the existing BAV-MCSE reported the highest WRR at 93.77% for a high SNR (at 20 dB) and 5.64% on average for a low SNR (at −10 dB) for different noises. The proposed DWT-CNN-MCSE system has proven to perform well at a low SNR with WRR of 70.55% and the highest improvement (64.91% WRR) at −10 dB SNR.
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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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ACEVEDO MOSQUEDA, M. E., M. A. ACEVEDO MOSQUEDA, R. CARREÑO AGUILERA, F. MARTINEZ ZUÑIGA, D. PACHECO BAUTISTA, M. PATIÑO ORTIZ, and WEN YU. "COMPUTATIONAL INTELLIGENCE FOR SHOEPRINT RECOGNITION." Fractals 27, no. 04 (June 2019): 1950080. http://dx.doi.org/10.1142/s0218348x19500804.
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Shoeprint marks present valuable information for forensic investigators to resolve a crime. These marks can be helpful to find the brand of the shoe and can make the investigation easier. In this paper, we present an associative model-based algorithm to match noisy shoeprint patterns with a brand of shoe. The shoeprints are corrupted with additive, subtractive and mixed noises. A particular case of subtractive noise are partial shoeprints such as toe, heel, left-half and right-half prints. The Morphological Associative Memories (MAMs) were applied. Both memories, max and min, recognize noisy shoeprints corrupted with 98% additive and subtractive noise, respectively, with an effectiveness of 100%. The images corrupted with mixed noise were recognized when the additive or subtractive noise applied was greater than the mixed noise; in this case, the recalling was around 70%, otherwise, both memories failed to recognize the shoeprints.
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Zhong, Dongzhou, Wanan Deng, Peng Hou, Jinbo Zhang, Yujun Chen, Qingfan Wu, and Tiankai Wang. "Recognition of Noisy Digital Images Using the Asymmetric Coupling Semiconductor Chaotic Lasers Network." Photonics 10, no. 11 (October 26, 2023): 1191. http://dx.doi.org/10.3390/photonics10111191.
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In this work, we construct a model of an asymmetrically coupled network of semiconductor chaotic lasers in order to recognize noisy digital images of digits 0–9, derived from different samples in the digital image sets 0–9 found within the MNIST dataset. Here, the lasers network consists of eight asymmetrically coupled semiconductor lasers. The chaotic lasers network is driven by the external inputs, which encode one noise digital image to be recognized. The outputs of the chaotic lasers network driven by a total of 40 samples from the digital image sets 0–9 are utilized as ten sets of reference signals. The output of the chaotic lasers network induced by one noisy digital image is used as a test signal. By judging the maximum of the correlations of the test signal with the ten sets of reference signals, all noisy digital images 0–9 can be recognized well under different noises. Moreover, we further explore the recognition rate for each noisy digital image under different noises and a fixed injection strength. It is found that all noisy digital images can be recognized well under a certain low injection strength. The recognition-rates of all noisy digital images can further decrease to a certain extent under higher noise and a fixed the injection strength. The injection strength has little influence on the recognition rate of one noise digital image target with lower noise. The recognition rate under higher noise maintains a higher value (more than 0.9) when the injection strength is smaller than a certain value, but for the larger injection strength, the recognition rate exhibits further decrease. The modeled chaotic lasers network can play the role of photonic accelerators for the recognition of the noisy digital images.
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Anam, Choirul, Ariij Naufal, Kosuke Matsubara, Tosgioh Fujibuchi, and Geoff Dougherty. "A method for quantification of noise non-uniformity in computed tomography images: A computational study." Journal of Physics and Its Applications 5, no. 2 (May 22, 2023): 48–57. http://dx.doi.org/10.14710/jpa.v5i2.17615.
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In computed tomography (CT), the noise is sometimes non-uniform, i.e. the noise magnitude may vary with the gradient level within the image. The purpose of this study was to quantify the noise non-uniformity in CT images using appropriate 1D and 2D computational phantoms, and to validate the effectiveness of the proposed concept in images filtered by the bilateral filter (BF), as an example of a non-linear filter. We first developed 1D and 2D computational phantoms, and Gaussian noises with several noise levels were then added to the phantoms. In addition, to simulate the real form of noise from images obtained in a real CT scanner, a homogeneous water phantom image was used. These noise levels were referred to as ground truth noise (σG). The phantoms were then filtered by the bilateral filter with various pixel value spreads (σ) to produce non-uniform noise. The original gradient phantoms (G) were subtracted from both the noisy phantoms (IN) and the filtered noisy phantoms (IBF), and the magnitudes of the resulting noise for each gradient were computed. The noise-gradient dependency (NGD) curve was used to display the dependency of noise magnitude on image gradient in the non-uniform noise. It is found that for uniform noise, the magnitude of noise was constant for all gradients. However, for non-uniform noise, the measured noise was dependent on the gradient levels and on the strength of the BF for every ground truth noise (σG). It was found that the noise magnitude was large for the large gradients and decreased with the magnitude of the image gradient.
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Yang, Jie. "Combining Speech Enhancement and Cepstral Mean Normalization for LPC Cepstral Coefficients." Key Engineering Materials 474-476 (April 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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Weis, J., and J. Haaber. "Reducing Noise Through Awareness in the NICU." Developmental Observer 12, no. 1 (September 20, 2019): 13. http://dx.doi.org/10.14434/do.v12i1.27843.
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In the NICU, environment sounds and noise can be challenging for the preterm and/or sick newborn baby. Reducing noise to provide an environment with appropriate and meaningful auditory experiences such as parents’ voices is important. Elimination of loud noises will furthermore benefit the families and staff. In 2016, the NICU at Rigshospitalet - Copenhagen University Hospital, collaborated with SoundEar™, a company that develops noise-meters for indicating and collecting noise levels, to develop a software program that was appropriate and easy to use. The aim was to support reduction in noise levels at the NICU through different layers of nudging: The noise meters with dis-plays should help staff and families become aware of their own noise levels and change their noisy behavior. The software helps staff become aware of when and where noise levels are critical and something should be done differently. The software sends out noise reports on a weekly basis via email to key staff members, who use these reports as a basis for further discussion about noise at staff meetings.
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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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Gao, Zhen Qiang, Zhi Guang Tian, and Yi Zhong Song. "Analyzing the Anti-Noise Performance of NAIRT Used in Deflection Tomography with Noised Projections." Applied Mechanics and Materials 373-375 (August 2013): 704–11. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.704.
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A new iteration reconstruction technique is suggested, which is named nonlinear auto-adjusting iterative reconstruction technique (NAIRT). Its anti-noise performance used in deflection tomography was tested with its projections added noises. A complicated air flow field, called model, was simulated, and was projected according to deflection tomographic algorithm. Thereupon, the real projections were obtained. A Series of random noises at different strength level were produced using randG function. Then, the noises were added to the real projections linearly. So, a series of noised projections were acquired. According to deflection tomographic algorithm, the noised projections were inversely projected to reconstruct the model using NAIRT. The reconstructive effect at the end of each cycle iteration was recorded with mean square error (MSE) index. The iteration stopped after one hundred and three cycles. As the results: First, at the noise level of 60dB S/N, NAIRT could reconstruct the model by a decent accuracy. The MSE declined to 6.49×10-5 at the end of 103 iteration cycles. Second, at the noise level of 30dB S/N, NAIRT could yet reconstruct the model by the level of its profile. The MSE stabilized at 2.67×10-4 at the end of 103 cycles. Last, at the noise level of 100dB S/N, NAIRT could accurately reconstruct the model. The MSE declined to 3.49×10-5 at the end of 103 iteration cycles. Both the reconstructed images and MSE analyses demonstrated that NAIRT had wonderful anti-noise performance when it was used in deflection tomography.
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Nisha, Bernad, and M. Victor Jose. "DTMF: Decision Based Trimmed Multimode Approach Filter for Denoising MRI Images." IT Journal Research and Development 7, no. 2 (February 7, 2023): 152–72. http://dx.doi.org/10.25299/itjrd.2023.9463.
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The brain MRI image denoising is a challenging and attracting field for young researchers because it enhances the quality of medical images. Salt and pepper noise is the most dangerous noise which reduces the accuracy of brain diagnosis, and it damages the brain medical images severely, that leads to neurologists to fix incorrect treatments or surgery. The pitfalls raised in the existing denoising methods are less Peak signal to noise ratio, high time consumption and incapable for enormous level of noise range. Hence, this research proposes a novel denoising filter which is entitled as ‘Decision based Trimmed Multimode approach oriented Filter (DTMF)’ for salt and pepper noise removal. Herein, the noise removal section is branched into six steps which efficiently reduce noises based on multimode of majority strength. The main concepts used in this research are viz. decision based approach, trimming process, majority of intensity, median, mean, dynamic windows and Square shaped Exemplar Modeled Patch Mechanism (SEMPM). The essential contributions of this approach are i) designing rule set for majority strength structured multimode denoising, ii) computation of majority property oriented parameters like majority-instance, majority strength and majority value, iii) novel SEMPM mechanism to predict noise-free data. The novel SEMPM mechanism grants a solution for the prediction of noise-free pixel in account of the noisy pixels whose surrounding window is completely packed by noisy data. The proposed decision-based approach removes the salt and pepper noise with high peak signal to noise ratio even for huge noise range, with reasonable time consumption.
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Kang, Min Jeong. "The Meaning of Noise in The Wind in the Willows." British and American Language and Literature Association of Korea 148 (March 30, 2023): 1–21. http://dx.doi.org/10.21297/ballak.2023.148.1.
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The purpose of this study is to explore the meaning of noise in Kenneth Grahame’s The Wind in the Willows, a representative work of the Edwardian era. From the perspective of the social context, noise in this novel can be divided into two aspects: collision noise caused by class conflict and mechanical noise created by technological civilization. During the Edwardian period, the conflict between the upper class and the working class was deepening while the advent of automobiles was ushering in a new era of technological civilization. At times, the class conflicts would erupt into noisy public demonstrations, even rebellion. Simultaneously, new technologies such as automobiles created noises never heard before in rural environments. All of these new noises disrupted the peace and contributed to a break - down in the stable class hierarchy and the idyllic tranquility of nature. Symbolically, these noises reflect Grahame’s fear of the destruction of the established ideology and the terror of industrialization. Grahame maintains a critical stance on society by depicting the issues of class conflict and industrialization as a cacophony that he wishes to eliminate.
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Ataeyan, Mahdieh, and Negin Daneshpour. "Automated Noise Detection in a Database Based on a Combined Method." Statistics, Optimization & Information Computing 9, no. 3 (June 9, 2021): 665–80. http://dx.doi.org/10.19139/soic-2310-5070-879.
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Data quality has diverse dimensions, from which accuracy is the most important one. Data cleaning is one of the preprocessing steps in data mining which consists of detecting errors and repairing them. Noise is a common type of error, that occur in database. This paper proposes an automated method based on the k-means clustering for noise detection. At first, each attribute (Aj) is temporarily removed from data and the k-means clustering is applied to other attributes. Thereafter, the k-nearest neighbors is used in each cluster. After that a value is predicted for Aj in each record by the nearest neighbors. The proposed method detects noisy attributes using predicted values. Our method is able to identify several noises in a record. In addition, this method can detect noise in fields with different data types, too. Experiments show that this method can averagely detect 92% of the noises existing in the data. The proposed method is compared with a noise detection method using association rules. The results indicate that the proposed method have improved noise detection averagely by 13%.
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Josephine, S., and S. Murugan. "Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1818–25. http://dx.doi.org/10.1166/jctn.2020.8446.
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In MR machine, surface coils, especially phased-arrays are used extensively for acquiring MR images with high spatial resolution. The signal intensities on images acquired using these coils have a non-uniform map due to coil sensitivity profile. Although these smooth intensity variations have little impact on visual diagnosis, they become critical issues when quantitative information is needed from the images. Sometimes, medical images are captured by low signal to noise ratio (SNR). The low SNR makes it difficult to detect anatomical structures because tissue characterization fails on those images. Hence, denoising are essential processes before further processing or analysis will be conducted. They found that the noise in MR image is of Rician distribution. Hence, general filters cannot be used to remove these types of noises. The linear spatial filtering technique blurs the object boundaries and degrades the sharp details. The existing works proved that Wavelet based works eliminates the noise coefficient that called wavelet thresholding. Wavelet thresholding estimates the noise level from high frequency content and estimates the threshold value by comparing the estimated noisy wavelet coefficient with other wavelet coefficients and eliminate the noisy pixel intensity value. Bayesian Shrinkage rule is one of the widely used methods. It uses for Gaussian type of noise, the proposed method introduced some adaptive technique in Bayesian Shrinkage method to remove Rician type of noises from MRI images. The results were verified using quantitative parameters such as Peak Signal to Noise Ratio (PSNR). The proposed Adaptive Bayesian Shrinkage Method (ABSM) outperformed existing methods.
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Hegarty, Paul. "Noise threshold: Merzbow and the end of natural sound." Organised Sound 6, no. 3 (December 2001): 193–200. http://dx.doi.org/10.1017/s1355771801003053.
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When we ask what noise is, we would do well to remember that no single definition can function timelessly - this may well be the case with many terms, but one of the arguments of this essay is that noise is that which always fails to come into definition. Generally speaking, noise is taken to be a problem: unwanted sound, unorganised sound, excessively loud sound. Metaphorically, when we hear of noise being generated, we understand it to be something extraneous. Historically, though, noise has just as often signalled music, or pleasing sound, as its opposite. In the twentieth century, the notion of a clear line between elements suitable for compositional use (i.e. notes, created on instruments) and the world of noises was broken down. Russolo's ‘noisy machines’, Varèse and Satie's use of ostensibly non-musical machines to generate sounds, musique concrète, Cage's rethinking of sound, noise, music, silence . . .
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Guo, Hui, Jin-Ming Liu, Cheng-Jie Zhang, and C. H. Oh. "Quantum discord of a three-qubit W-class state in noisy environments." Quantum Information and Computation 12, no. 7&8 (July 2012): 677–92. http://dx.doi.org/10.26421/qic12.7-8-12.
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We study the dynamics of the pairwise quantum discord (QD), classical correlation (CC), and entanglement of formation (EOF) for the three-qubit W-class state |W>_{123}=\frac 12(|100>_{123}+|010>_{123}+\sqrt{2}|001>_{123}) under the influence of various Markovian noises by analytically solving the master equation in the Lindblad form. Through numerical analysis, we find that EOF decreases asymptotically to zero with time for the dephasing noise, but it undergoes sudden death for the bit-flip noise, the isotropic noise, as well as the dissipative and noisy environments. Moreover, QD decays to zero in an asymptotical way for all the noises we investigated. Thus, when the W-class state |W>_{123} is subject to the above Markovian noises, QD is more robust than EOF against decoherence excluding the phase-flip noise, implying that QD is more useful than entanglement to characterize the quantum correlation. We also find a remarkable character for the CC in the presence of the phase-flip noise, i.e., CC displays the behavior of sudden transition and then keeps constant permanently, but the corresponding QD just exhibits a very small sudden change. Furthermore, we verify the monogamic relation between the pairwise QD and EOF of the W-class state.
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Jiang, Gaoxia, Jia Zhang, Xuefei Bai, Wenjian Wang, and Deyu Meng. "Which Is More Effective in Label Noise Cleaning, Correction or Filtering?" Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12866–73. http://dx.doi.org/10.1609/aaai.v38i11.29183.
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Most noise cleaning methods adopt one of the correction and filtering modes to build robust models. However, their effectiveness, applicability, and hyper-parameter insensitivity have not been carefully studied. We compare the two cleaning modes via a rebuilt error bound in noisy environments. At the dataset level, Theorem 5 implies that correction is more effective than filtering when the cleaned datasets have close noise rates. At the sample level, Theorem 6 indicates that confident label noises (large noise probabilities) are more suitable to be corrected, and unconfident noises (medium noise probabilities) should be filtered. Besides, an imperfect hyper-parameter may have fewer negative impacts on filtering than correction. Unlike existing methods with a single cleaning mode, the proposed Fusion cleaning framework of Correction and Filtering (FCF) combines the advantages of different modes to deal with diverse suspicious labels. Experimental results demonstrate that our FCF method can achieve state-of-the-art performance on benchmark datasets.
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Yu, Boya, Yuying Chai, and Chao Wang. "Effect of the Exterior Traffic Noises on the Sound Environment Evaluation in Office Spaces with Different Interior Noise Conditions." Applied Sciences 14, no. 7 (April 3, 2024): 3017. http://dx.doi.org/10.3390/app14073017.
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The present study focuses on the impact of exterior traffic noises on sound environment evaluation in office spaces, considering their interaction with interior noises. There were three interior noise conditions: silence, air-conditioner noise, and irrelevant speech noise. Six exterior traffic noises (road, maglev, tram, metro, conventional inter-city train, and high-speed train) were merged with interior noise clips to create the combined noise stimuli. Forty subjects participated in the experiment to assess the acoustic environment in office spaces exposed to multiple noises. The results showed that both interior and exterior noise significantly affected acoustic comfort and noise disturbance. As for the exterior traffic noise, both the traffic noise source and the noise level were found to be influential on both attributes. More temporally fluctuating traffic noises, such as high-speed train noise, were found to have a greater negative effect on subjective evaluations. Meanwhile, the interior noise source was also found to influence evaluations of the sound environment. Compared to the single traffic noise condition, irrelevant speech noise significantly increased the negative impact of traffic noises, while the air-conditioner noise had a neutral effect. In addition, participants in offices with speech noise were less sensitive to the traffic noise level.
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Wang, Guodong, Qian Dong, Zhenkuan Pan, Ximei Zhao, Jinbao Yang, and Cunliang Liu. "Active Contour Model for Ultrasound Images with Rayleigh Distribution." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/295320.
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Ultrasound images are often corrupted by multiplicative noises with Rayleigh distribution. The noises are strong and often called speckle noise, so segmentation is a hard work with this kind of noises. In this paper, we incorporate multiplicative noise removing model into active contour model for ultrasound images segmentation. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. Our model can segment the noisy ultrasound images very well. Finally, a fast method called Split-Bregman method is used for the easy implementation of segmentation. Experiments on a variety of synthetic and real ultrasound images validate the performance of our method.
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Balachandran, G., and Praveen Kumar Gupta. "FPGA – Based Electrocardiography Signal Analysis System using (FIR) Filter." International Journal of Advance Research and Innovation 8, no. 1 (2020): 44–48. http://dx.doi.org/10.51976/ijari.812008.
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The cardiovascular attack is a more dangerous than other diseases and it is measured by ECG (Electro cardiograph) signals which is like a noisy signal in real time, especially in the field of telemedicine environment. The noisy ECG signals have more motion artifacts, electrical interference, etc. An adaptive filtering approach based on Discrete Wavelet Transform and an artificial neural network is proposed to reduce the noise in ECG signal. The quality of de-noised signal is improved by SVM algorithm. This suggested approach can successfully take out a broad scope of noise and our method achieve up to almost 82% improvement on the SNR of de-noised signals. The MATLAB simulation results shown clearly about the improvement of ECG signal with SNR value.
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EL MELLALI, TARIK, and YOUSSEF OUKNINE. "WEAK CONVERGENCE FOR QUASILINEAR STOCHASTIC HEAT EQUATION DRIVEN BY A FRACTIONAL NOISE WITH HURST PARAMETER H ∈ (½, 1)." Stochastics and Dynamics 13, no. 03 (May 27, 2013): 1250024. http://dx.doi.org/10.1142/s0219493712500244.
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In this paper, we consider a quasi-linear stochastic heat equation in one dimension on [0, 1], with Dirichlet boundary conditions driven by an additive fractional white noise. We formally replace the random perturbation by a family of noisy inputs depending on a parameter n ∈ ℕ which can approximate the fractional noise in some sense. Then, we provide sufficient conditions ensuring that the real-valued mild solution of the SPDE perturbed by this family of noises converges in law, in the space [Formula: see text] of continuous functions, to the solution of the fractional noise driven SPDE.
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Rouis, Mohamed, Salim Sbaa, and Nasser Edinne Benhassine. "The effectiveness of the choice of criteria on the stationary and non-stationary noise removal in the phonocardiogram (PCG) signal using discrete wavelet transform." Biomedical Engineering / Biomedizinische Technik 65, no. 3 (May 26, 2020): 353–66. http://dx.doi.org/10.1515/bmt-2019-0197.
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AbstractThe greatest problem with recording heart sounds is parasitic noise effects. A reasonable solution to reduce noise can be carried out by minimization of extraneous noises in the vicinity of the patient during recording, in addition to the methods of signal processing that must be effective in noisy environments. Wavelet transform has become an essential tool for many applications, but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) are important key factors to demonstrate the advantages of wavelet denoising. So, selection of optimal mother wavelet with DL is a main challenge to current algorithms. The principal aim of this study was the choice of an appropriate criterion for finding the optimal DL and the optimal mother wavelet function according to four criteria which are: signal-to-noise ratio (SNR), mean square error (MSE), percentage root-mean-square difference (PRD) and the structure similarity index measure (SSIM) for testing the robustness of the proposed algorithm. The proposed method is applied to the PCG signal contaminated with four colored noise types, in addition to the Gaussian noise. The obtained results show the effectiveness of the proposed method in reducing noise from the noisy PCG signals, especially at a low SNR.
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Shi, Yao-Wu, Chen Wang, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi, and De-Min Wang. "1/f spectrum estimation based on α-stable distribution in colored Gaussian noise environments." Journal of Low Frequency Noise, Vibration and Active Control 38, no. 1 (December 4, 2018): 18–35. http://dx.doi.org/10.1177/1461348418813291.
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The main goal of this paper is to suppress the effect of unavoidable colored Gaussian noise on declining accuracy of transistor 1/f spectrum estimation. Transistor noises are measured by a nondestructive cross-spectrum measurement method, which is first to amplify the voltage signals through ultra-low noise amplifiers, then input the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. Therefore, by utilizing the properties of α-stable distribution, we propose a cross-spectrum method effective in noisy environments based on samples normalized cross-correlation function. Simulation results and diodes output noise spectrum estimation results confirm the effectiveness of our method.
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Nimmagadda, Padmaja, Kondru Ayyappa Swamy, Samuda Prathima, Sushma Chintha, and Zachariah Callottu Alex. "Short-term uncleaned signal to noise threshold ratio based end-to-end time domain speech enhancement in digital hearing aids." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (July 1, 2022): 131. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp131-138.
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This paper presents the improvements in the combined solution for the noise estimation and the speech enhancement in digital hearing aids in time domain. This study focuses on the single channel statistical temporal speech enhancement using adaptive Wiener filtering. In this technique, the noise is updated based on the short-term uncleaned signal to noise threshold ratio (ST-USNTR) of the frame. It works best if and only if the back ground noise level is low compared to that of speech of interest. We considered the time domain algorithms in order to consider the time varying nature of speech signal. The performance of the proposed algorithm is evaluated for speech signal with seven ty pes of noises and three signal to noise ratios (SNR) levels in each type of noise. From the results, it is clear that the basic level of adaptive speech enhancement is obtained using statistical parameters of noisy speech without the need for reference input.
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Lv, Li, and Ping Zhou. "Effect of noise on deterministic remote preparation of an arbitrary two-qudit state by using a four-qudit χ-type state as the quantum channel." International Journal of Quantum Information 18, no. 05 (August 2020): 2050028. http://dx.doi.org/10.1142/s0219749920500288.
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We present a protocol for remote preparation of an arbitrary two-qudit state by using a four-qudit [Formula: see text]-type state as the quantum channel via positive operator-valued measurement. We first propose the protocol for remote preparation of an arbitrary two-qudit state via positive operator-valued measurement in noiseless environment and then discuss the protocol in noisy environments. Four important quantum decoherence noise models, the dephasing noise, the qudit-flip noise, the qudit-phase-flip noise and the depolarizing noise, are considered in our protocol. The output states and the fidelities of remote state preparation in four different types of quantum noises are presented. It is shown the protocol for remote state preparation via positive operator-valued measurement with [Formula: see text]-type state has the advantage of transmitting less particles for remote preparing an arbitrary two-qudit state. The fidelities of remote state preparation depend on the coefficients of original two-qudit state and the decoherence rates of the noise models.
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Al-Attabi, Ali, and Ali Al. "Spectral Graph Filtering for Noisy Signals Using the Kalman filter." ECTI Transactions on Electrical Engineering, Electronics, and Communications 21, no. 2 (June 27, 2023): 249818. http://dx.doi.org/10.37936/ecti-eec.2023212.249818.
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Noise is unwanted electrical or electromagnetic radiation that degrades the quality of the signal and the data. It can be difficult to denoise a signal that has been acquired in a noisy environment, but doing so may be necessary in a number of signal processing applications. This paper extends the issue of signal denoising from signals with regular structures, which are affected by noise, to signals with irregular structures by applying the graph signal processing (GSP) technique and a very wellknown filter, the standard Kalman filter, after adjusting it. When the modified Kalman filter is compared to the standard Kalman filter, the modified one performs better in situations where there are uncertain observations and/or processing noise and shows the best results. Also, the modified Kalman filter showed a higher efficiency when we compared it with other filters for different types of noise, which are not only standard Gaussian noises but also uniform distribution noise across two intervals for uncertain observation noise.
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Seper, Eric, Francis Kuk, Petri Korhonen, and Christopher Slugocki. "Tracking of Noise Tolerance to Predict Hearing Aid Satisfaction in Loud Noisy Environments." Journal of the American Academy of Audiology 30, no. 04 (April 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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GUO, YANHUI, H. D. CHENG, and YINGTAO ZHANG. "A NEW NEUTROSOPHIC APPROACH TO IMAGE DENOISING." New Mathematics and Natural Computation 05, no. 03 (November 2009): 653–62. http://dx.doi.org/10.1142/s1793005709001490.
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A neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image G is transformed into NS domain, which is described using three membership sets: T, I and F. The entropy of the neutrosophic set is defined and employed to evaluate the indeterminancy. A new operation, γ-median-filtering operation, is proposed to decrease the set indeterminancy and remove noise. We have conducted experiments on a variety of noisy images using different types of noises with different levels. The experimental results demonstrate that the proposed approach can remove noise automatically and effectively. Especially, it can process not only noisy images with different levels of noise, but also images with different kinds of noise well without knowing the type of the noise, which is the most difficult task for image denoising.
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Wei, Xing, Jiahua Xiao, and Yihong Gong. "Blind Hyperspectral Image Denoising with Degradation Information Learning." Remote Sensing 15, no. 2 (January 13, 2023): 490. http://dx.doi.org/10.3390/rs15020490.
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Although existing hyperspectral image (HSI) denoising methods have exhibited promising performance in synthetic noise removal, they are seriously restricted in real-world scenarios with complicated noises. The major reason is that model-based methods largely rely on the noise type assumption and parameter setting, and learning-based methods perform poorly in generalizability due to the scarcity of real-world clean–noisy data pairs. To overcome this long-standing challenge, we propose a novel denoising method with degradation information learning (termed DIBD), which attempts to approximate the joint distribution of the clean–noisy HSI pairs in a Bayesian framework. Specifically, our framework learns the mappings of noisy-to-clean and clean-to-noisy in a priority dual regression scheme. We develop more comprehensive auxiliary information to simplify the joint distribution approximation process instead of only estimating noise intensity. Our method can leverage both labeled synthetic and unlabeled real data for learning. Extensive experiments show that the proposed DIBD achieves state-of-the-art performance on synthetic datasets and has better generalization to real-world HSIs. The source code will be available to the public.
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Viney, Mark, and Sarah E. Reece. "Adaptive noise." Proceedings of the Royal Society B: Biological Sciences 280, no. 1767 (September 22, 2013): 20131104. http://dx.doi.org/10.1098/rspb.2013.1104.
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In biology, noise implies error and disorder and is therefore something which organisms may seek to minimize and mitigate against. We argue that such noise can be adaptive. Recent studies have shown that gene expression can be noisy, noise can be genetically controlled, genes and gene networks vary in how noisy they are and noise generates phenotypic differences among genetically identical cells. Such phenotypic differences can have fitness benefits, suggesting that evolution can shape noise and that noise may be adaptive. For example, gene networks can generate bistable states resulting in phenotypic diversity and switching among individual cells of a genotype, which may be a bet hedging strategy. Here, we review the sources of noise in gene expression, the extent to which noise in biological systems may be adaptive and suggest that applying evolutionary rigour to the study of noise is necessary to fully understand organismal phenotypes.
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CHU, PETER C., LEONID M. IVANOV, and TATYANA M. MARGOLINA. "ROTATION METHOD FOR RECONSTRUCTING PROCESS AND FIELD FROM IMPERFECT DATA." International Journal of Bifurcation and Chaos 14, no. 08 (August 2004): 2991–97. http://dx.doi.org/10.1142/s0218127404010941.
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Reconstruction of processes and fields from noisy data is to solve a set of linear algebraic equations. Three factors affect the accuracy of reconstruction: (a) a large condition number of the coefficient matrix, (b) high noise-to-signal ratio in the source term, and (c) no a priori knowledge of noise statistics. To improve reconstruction accuracy, the set of linear algebraic equations is transformed into a new set with minimum condition number and noise-to-signal ratio using the rotation matrix. The procedure does not require any knowledge of low-order statistics of noises. Several examples including highly distorted Lorenz attractor illustrate the benefit of using this procedure.
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CHAPEAU-BLONDEAU, FRANÇOIS, and JULIO ROJAS-VARELA. "NONLINEAR SIGNAL PROPAGATION ENHANCED BY NOISE VIA STOCHASTIC RESONANCE." International Journal of Bifurcation and Chaos 10, no. 08 (August 2000): 1951–59. http://dx.doi.org/10.1142/s0218127400001249.
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A model is developed for a nonlinear line of coupled noisy threshold elements. The propagation on the line of various information-carrying signals, periodic, aperiodic or random, is analyzed. Different measures quantifying the efficacy of the propagation are calculated, including signal-to-noise ratio, cross-correlation measures, information-theoretic measures and propagation length. These measures are shown to be improvable by the addition of noise. These results establish a new instance of the nonlinear phenomenon of stochastic resonance under the form of a noise-enhanced propagation applying to a broad variety of signals and noises. The results also contain significance for the propagation of neuronal signals.
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Selvaraj, Poovarasan, and E. Chandra. "Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 1869–83. http://dx.doi.org/10.3233/jifs-211236.
Full textAbstract:
The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.
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Shen, Kenan, and Dongbiao Zhao. "An EMD-LSTM Deep Learning Method for Aircraft Hydraulic System Fault Diagnosis under Different Environmental Noises." Aerospace 10, no. 1 (January 5, 2023): 55. http://dx.doi.org/10.3390/aerospace10010055.
Full textAbstract:
Aircraft hydraulic fault diagnosis is an important technique in aircraft systems, as the hydraulic system is one of the key components of an aircraft. In aircraft hydraulic system fault diagnosis, complex environmental noises will lead to inaccurate results. To address the above problem, hydraulic system fault detection methods should be capable of noise resistance. Previous research has mainly focused on noise-free conditions and many effective approaches have been proposed; however, in real-world aircraft flying conditions, the aircraft hydraulic system often has strong and complex noises. The methods proposed may not have good fault detection results in such a noisy environment. According to the situation, this work focuses on aircraft hydraulic system fault classification under the influence of a hydraulic working environment with Gaussian white noise. In order to eliminate the noise interference and adapt to the actual noisy environment, a new aircraft hydraulic fault diagnostic method based on empirical mode deposition (EMD) and long short-term memory (LSTM) is presented. First, the hydraulic system is constructed by AMESIM. One normal state and five fault states are considered in this paper. Eight-channel signals of different states are collected for network training and testing. Second, the EMD method is used to obtain the different intrinsic mode functions (IMFs) of the signals. Third, principal component analysis (PCA) is used to obtain the main component of the IMFs. Fourth, three different LSTM methods are chosen to compare and the best structure that is chosen is the gate recurrent unit (GRU). After that, the network parameters are optimized. The results under different noise environments are given. Then, a comparison between the EMD-GRU with several different machine learning methods is considered, and the result shows that the method in this paper has a better anti-noise effect. Therefore, the proposed method is demonstrated to have a strong ability of fault diagnosis and classification under the working noises based on the simulation results.
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Nogales, Alberto, Javier Caracuel-Cayuela, and Álvaro J. García-Tejedor. "Analyzing the Influence of Diverse Background Noises on Voice Transmission: A Deep Learning Approach to Noise Suppression." Applied Sciences 14, no. 2 (January 15, 2024): 740. http://dx.doi.org/10.3390/app14020740.
Full textAbstract:
This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. The training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combined with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise suppression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. The results have been assessed with objective (mathematical metrics) and subjective (listening to a set of audios by humans) methods. Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work should involve refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical applications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality.
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Achtenberg, Krzysztof, Janusz Mikołajczyk, and Zbigniew Bielecki. "Two-Channel Detecting Sensor with Signal Cross-Correlation for FTIR Instruments." Sensors 22, no. 22 (November 18, 2022): 8919. http://dx.doi.org/10.3390/s22228919.
Full textAbstract:
This paper’s purpose was to demonstrate a performance of a novel approach in a low-noise optical sensor for an FTIR spectrometer. Methods: Compared to the standard FTIR detection setup, our sensor ensures a higher signal-to-noise ratio (SNR) and lower signal standard deviation by reducing the uncorrelated noise components (e.g., thermal and 1/f noises of the detection module). Its construction is based on two-channel detection modules and a processing unit with implemented cross-correlation signal analyses. Each module was built of LWIR HgCdTe photodiodes and low-noise transimpedance amplifiers. Results: the experiments demonstrated a decrease in a signal standard deviation of about 1.7 times with a 10 dB-improvement in the SNR. Conclusion: this result indicates our sensor’s main benefit, especially in registered “weak” and noisy interferograms.