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Relevant bibliographies by topics / Feature Noise

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Academic literature on the topic 'Feature Noise'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "Feature Noise"

1

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

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Alabbasi, Hesham A., Ali M. Jalil, and Fadhil S. Hasan. "Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5208. http://dx.doi.org/10.11591/ijece.v10i5.pp5208-5216.

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The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a

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Li, Zhao Fei, and Jiang Qing Wang. "Image Background Removal by Contour Feature." Advanced Materials Research 926-930 (May 2014): 3050–53. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3050.

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In the task of the image processing and analysis, the background noise removal is a important step. In the image background noise removal, there are many methods which is popular for the researchers. For example, the gray threshold methods are commonly taken to remove the noises which have large contrast to the interest objects. However, there are many noises with no variance with the interest objects in the gray level. For these noises, the gray level based noise removal method is totally futile, while the contour feature has its super performance for reducing this sort of noise. For the cont

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McKenzie, Shannon. "Feature: Noise Annoys." Journal of the Royal Society for the Promotion of Health 128, no. 2 (2008): 58–59. http://dx.doi.org/10.1177/14664240081280020802.

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Chen, Zhe, Yaan Li, Renjie Cao, Wasiq Ali, Jing Yu, and Hongtao Liang. "A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy." Entropy 21, no. 6 (2019): 624. http://dx.doi.org/10.3390/e21060624.

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Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, the existing entropy feature extraction methods for ship-radiated noise are less reliable under noisy conditions because they lack noise reduction procedures or are single-scale based. In order to simultaneously solve these problems, a new feature extraction method is proposed based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), normalized mutua

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Youssef, Khalid, Nanette N. Jarenwattananon, and Louis-S. Bouchard. "Feature-Preserving Noise Removal." IEEE Transactions on Medical Imaging 34, no. 9 (2015): 1822–29. http://dx.doi.org/10.1109/tmi.2015.2409265.

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FAROOQ, O., S. DATTA, and M. C. SHROTRIYA. "WAVELET SUB-BAND BASED TEMPORAL FEATURES FOR ROBUST HINDI PHONEME RECOGNITION." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 06 (2010): 847–59. http://dx.doi.org/10.1142/s0219691310003845.

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This paper proposes the use of wavelet transform-based feature extraction technique for Hindi speech recognition application. The new proposed features take into account temporal as well as frequency band energy variations for the task of Hindi phoneme recognition. The recognition performance achieved by the proposed features is compared with the standard MFCC and 24-band admissible wavelet packet-based features using a linear discriminant function based classifier. To evaluate robustness of these features, the NOISEX database is used to add different types of noise into phonemes to achieve si

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Upadhyaya, Prashant, Omar Farooq, M. R. Abidi, and Priyanka Varshney. "Comparative Study of Visual Feature for Bimodal Hindi Speech Recognition." Archives of Acoustics 40, no. 4 (2015): 609–19. http://dx.doi.org/10.1515/aoa-2015-0061.

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Abstract In building speech recognition based applications, robustness to different noisy background condition is an important challenge. In this paper bimodal approach is proposed to improve the robustness of Hindi speech recognition system. Also an importance of different types of visual features is studied for audio visual automatic speech recognition (AVASR) system under diverse noisy audio conditions. Four sets of visual feature based on Two-Dimensional Discrete Cosine Transform feature (2D-DCT), Principal Component Analysis (PCA), Two-Dimensional Discrete Wavelet Transform followed by DC

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Petety, Aditya, Sandhya Tripathi, and N. Hemachandra. "Attribute Noise Robust Binary Classification (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13897–98. http://dx.doi.org/10.1609/aaai.v34i10.7221.

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We consider the problem of learning linear classifiers when both features and labels are binary. In addition, the features are noisy, i.e., they could be flipped with an unknown probability. In Sy-De attribute noise model, where all features could be noisy together with same probability, we show that 0-1 loss (l0−1) need not be robust but a popular surrogate, squared loss (lsq) is. In Asy-In attribute noise model, we prove that l0−1 is robust for any distribution over 2 dimensional feature space. However, due to computational intractability of l0−1, we resort to lsq and observe that it need no

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Wang, Kun-Ching. "Robust Audio Content Classification Using Hybrid-Based SMD and Entropy-Based VAD." Entropy 22, no. 2 (2020): 183. http://dx.doi.org/10.3390/e22020183.

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A robust approach for the application of audio content classification (ACC) is proposed in this paper, especially in variable noise-level conditions. We know that speech, music, and background noise (also called silence) are usually mixed in the noisy audio signal. Based on the findings, we propose a hierarchical ACC approach consisting of three parts: voice activity detection (VAD), speech/music discrimination (SMD), and post-processing. First, entropy-based VAD is successfully used to segment input signal into noisy audio and noise even if variable-noise level is happening. The determination

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Dissertations / Theses on the topic "Feature Noise"

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Fairhurst, Harry. "Robust feature extraction for the recognition of noisy speech." Thesis, University of Liverpool, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327705.

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Javadi, Ailar. "Bio-inspired noise robust auditory features." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44801.

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The purpose of this work is to investigate a series of biologically inspired modifications to state-of-the-art Mel- frequency cepstral coefficients (MFCCs) that may improve automatic speech recognition results. We have provided recommendations to improve speech recognition results de- pending on signal-to-noise ratio levels of input signals. This work has been motivated by noise-robust auditory features (NRAF). In the feature extraction technique, after a signal is filtered using bandpass filters, a spatial derivative step is used to sharpen the results, followed by an envelope detector (recti

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Jedari, Fathi Elnaz. "NOISE IMPACT REDUCTION IN CLASSIFICATION APPROACH PREDICTING SOCIAL NETWORKS CHECK-IN LOCATIONS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2110.

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Since August 2010, Facebook has entered the self-reported positioning world by providing the check-in service to its users. This service allows users to share their physical location using the GPS receiver in their mobile devices such as a smart-phone, tablet, or smart-watch. Over the years, big datasets of recorded check-ins have been collected with increasing popularity of social networks. Analyzing the check-in datasets reveals valuable information and patterns in users’ check-in behavior as well as places check-in history. The analysis results can be used in several areas including busines

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Davis, Joseph M. "Second-order Cyclostationary Feature Based Detection of WiMAX Signals in Pulsed Noise Environments." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/47105.

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Spectral coexistence and cooperative spectrum sharing techniques are vital to the continued development and proliferation of wireless communications systems. Government directives indicate that certain frequency bands which once were reserved for radar-only applications must now support wireless broadband systems. The effect of co-site interference upon detection techniques for wireless broadband systems is evaluated. Cyclostationary feature based detection methods are evaluated against gaussian noise and interfering radar signals. Alternative decision algorithms utilizing support vector mach

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Hu, Xin. "An Improved 2D Adaptive Smoothing Algorithm in Image Noise Removal and Feature Preservation." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1235722460.

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Gajic, Bojana. "Feature Extraction for Automatic Speech Recognition in Noisy Acoustic Environments." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-441.

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<p>This thesis presents a study of alternative speech feature extraction methods aimed at increasing robustness of automatic speech recognition (ASR) against additive background noise. </p><p>Spectral peak positions of speech signals remain practically unchanged in presence of additive background noise. Thus, it was expected that emphasizing spectral peak positions in speech feature extraction would result in improved noise robustness of ASR systems. If frequency subbands are properly chosen, dominant subband frequencies can serve as reasonable estimates of spectral peak positions. Thus, diffe

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Schuster, Frank [Verfasser], and Cristóbal [Akademischer Betreuer] Curio. "Feature Based SLAM using High-Noise Low-Cost Automotive Sensors / Frank Schuster ; Betreuer: Cristóbal Curio." Tübingen : Universitätsbibliothek Tübingen, 2020. http://d-nb.info/1206173114/34.

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Mizaku, Alda. "Biomolecular feature selection of colorectal cancer microarray data using GA-SVM hybrid and noise perturbation to address overfitting." Diss., Online access via UMI:, 2009.

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Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Bioengineering, Biomedical Engineering, 2009.<br>Includes bibliographical references.

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Sorsa, A. (Aki). "Prediction of material properties based on non-destructive Barkhausen noise measurement." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526200682.

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Abstract Barkhausen noise measurement is an intriguing non-destructive testing method suitable for ferromagnetic materials. It is based on the stochastic movements of magnetic domain walls when the tested sample is placed in an external varying magnetic field. Barkhausen noise is typically utilised so that some features are calculated from the signal and then compared with the studied material properties. Typical features are, for example, the root-mean-square value (RMS), peak height, width and position. Better utilisation of the method, however, requires quantitative predictions of material

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Kumar, Prashant. "Online 3D Reconstruction and Ground Segmentation using Drone based Long Baseline Stereo Vision System." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/98009.

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This thesis presents online 3D reconstruction and ground segmentation using unmanned aerial vehicle (UAV) based stereo vision. For this purpose, a long baseline stereo vision system has been designed and built. Application of this system is to work as part of an air and ground based multi-robot autonomous terrain surveying project at Unmanned Systems Lab (USL), Virginia Tech, to act as a first responder robotic system in disaster situations. Areas covered by this thesis are design of long baseline stereo vision system, study of stereo vision raw output, techniques to filter out outliers from r

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Books on the topic "Feature Noise"

1

Nathanson, Michael, and Todd Graff. Joyful noise. Distributed by Warner Home Video, 2012.

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Iqbal, R. Noise analysis in tracking the features of a spatially distributed field. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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3

Borg, Erik. Noise-induced hearing loss: Literature review and experiments in rabbits : morphological and electrophysiological features, exposure parameters and temporal factors, variability and interactions. Scandinavian University Press, 1995.

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4

An Investigation of Preliminary Feature Screening Using Signal-To-Noise Ratios. Storming Media, 1996.

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Mansell, James G. Creating the Sonically Rational. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040672.003.0004.

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This chapter examines rationalizing attempts to intervene in the everyday sounds of early twentieth-century Britain, focussing on two spatial case studies – the industrial workplace and the home – where reformers targeted their attention in the inter-war period. It traces the influence of state-sponsored industrial psychologists who situated their dispassionate expertise about noise in opposition to the noise abatement movement led by the Anti-Noise League. The chapter also examines documentary filmmaking as a third practice where state-sponsored activity sought to shape everyday attitudes to

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6

Whittaker, Nicola. Noses (Creature Features). Gareth Stevens Publishing, 2002.

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Noses (Creature Features). Franklin Watts Ltd, 2001.

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Noses (Creature Features). Franklin Watts Ltd, 2003.

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Burke, Billie. With a Feather on My Nose. Kessinger Publishing, LLC, 2005.

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Burke, Billie. With a Feather on My Nose. Kessinger Publishing, LLC, 2005.

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Book chapters on the topic "Feature Noise"

1

Droppo, Jasha. "Feature Compensation." In Techniques for Noise Robustness in Automatic Speech Recognition. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118392683.ch9.

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Tam, Roger, and Wolfgang Heidrich. "Feature-Preserving Medial Axis Noise Removal." In Computer Vision — ECCV 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47967-8_45.

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Teng, Choh Man. "Combining Noise Correction with Feature Selection." In Data Warehousing and Knowledge Discovery. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45228-7_34.

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Gemmeke, Jort Florent, and Ulpu Remes. "Missing-Data Techniques: Feature Reconstruction." In Techniques for Noise Robustness in Automatic Speech Recognition. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118392683.ch15.

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Jenkinson, Mark, and Michael Brady. "Feature saliency from noise variations in invariants." In Computer Vision — ACCV'98. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63931-4_196.

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Partridge, D., W. Wang, and P. Jones. "Efficient and Effective Feature Selection in the Presence of Feature Interaction and Noise." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44732-6_20.

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Kim, Hyun Dong, Chul Hong Min, and Tae Seon Kim. "Adaptable Noise Reduction of ECG Signals for Feature Extraction." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760191_86.

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Hernández, Gabriel, José R. Calvo, Flavio J. Reyes, and Rafael Fernández. "Simple Noise Robust Feature Vector Selection Method for Speaker Recognition." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_37.

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Harizanov, Stanislav, Jaume de Dios Pont, Sebastian Ståhl, and Dennis Wenzel. "Noise Removal and Feature Extraction of 2D CT Radiographic Images." In Advanced Computing in Industrial Mathematics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65530-7_6.

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Leal, Adriana, César Teixeira, Ioanna Chouvarda, et al. "A Multi-feature Approach for Noise Detection in Lung Sounds." In International Conference on Biomedical and Health Informatics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4505-9_47.

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Conference papers on the topic "Feature Noise"

1

Guo, Yingying, and Xuezhi Zhao. "Fault Feature Extraction of Elliptically Shaped Bearing Raceway." In Noise and Vibration Conference & Exhibition. SAE International, 2019. http://dx.doi.org/10.4271/2019-01-1564.

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Pang, Guansong, Longbing Cao, Ling Chen, and Huan Liu. "Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/360.

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This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-k outlier ranking evaluation meas

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Haylett, Jim, Francesc Romagosa, and Erik Nelson. "Subjective and Objective Seat Feature Acoustic and Vibration Response." In Noise and Vibration Conference & Exhibition. SAE International, 2021. http://dx.doi.org/10.4271/2021-01-1075.

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Demuynck, Kris, Xueru Zhang, Dirk Van Compernolle, and Hugo Van hamme. "Feature versus model based noise robustness." In Interspeech 2010. ISCA, 2010. http://dx.doi.org/10.21437/interspeech.2010-269.

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Giesbrecht, James E., Russell Clarke, and Derek Abbott. "Coherence as a feature of real HF signals." In SPIE Third International Symposium on Fluctuations and Noise, edited by Costas N. Georghiades and Langford B. White. SPIE, 2005. http://dx.doi.org/10.1117/12.620312.

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Amiriparian, Shahin, Maurice Gerczuk, Sandra Ottl, Nicholas Cummins, Sergey Pugachevskiy, and Bjorn Schuller. "Bag-of-Deep-Features: Noise-Robust Deep Feature Representations for Audio Analysis." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489416.

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Zhang, Yan. "A Muti-Feature based Noise Detector for Random-Valued Impulse Noise." In 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2020. http://dx.doi.org/10.1109/icaica50127.2020.9182608.

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Jingfeng Pan, Tieyong Cao, Xiongwei Zhang, and Hui Huang. "A quantum-inspired noise reduction method based on noise feature codebook." In 2012 International Conference on Computer Vision in Remote Sensing (CVRS). IEEE, 2012. http://dx.doi.org/10.1109/cvrs.2012.6421252.

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Ayari, Emna Zoghlami, Reinhard Tielert, and Norbert Wehn. "A Noise Tolerant Method for ECG Signals Feature Extraction and Noise Reduction." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162207.

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Sharpee, Tatyana, Hiroki Sugihara, A. V. Kurgansky, S. Rebrik, M. P. Stryker, and Kenneth D. Miller. "Probing feature selectivity of neurons in primary visual cortex with natural stimuli." In Second International Symposium on Fluctuations and Noise, edited by Derek Abbott, Sergey M. Bezrukov, Andras Der, and Angel Sanchez. SPIE, 2004. http://dx.doi.org/10.1117/12.548513.

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