Gotowe bibliografie tematyczne / Blind source separation

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Gotowa bibliografia na temat „Blind source separation”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Blind source separation"

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x, Ekta, and Shelej Khera. "Acoustic Echo Canceller with Blind Source Separation." International Journal of Scientific Engineering and Research 3, no. 6 (2015): 83–85. https://doi.org/10.70729/ijser15265.

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Zhang, Chao Zhu, Ahmed Kareem Abdullah, and Ali Abdullabs Abdullah. "Electroencephalogram-Artifact Extraction Enhancement Based on Artificial Intelligence Technique." Journal of Biomimetics, Biomaterials and Biomedical Engineering 27 (May 2016): 77–91. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.27.77.

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Blind source separation (BSS) is an important technique used to recover isolated independent sources signals from mixtures. This paper proposes two blind artificial intelligent separation algorithms based on hybridization between artificial intelligent techniques with classical blind source separation algorithms to enhance the separation process. Speedy genetic algorithm SGA directly guesses the optimal coefficients of the separating matrix based on candidate initial from classical BSS algorithms also the separation criteria based on minimization of mutual information between the separating in
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Frikel, Miloud, Victor Barroso, and Joao Xavier. "Blind source separation." Journal of the Acoustical Society of America 105, no. 2 (1999): 1101–2. http://dx.doi.org/10.1121/1.425160.

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Behr, Merle, Chris Holmes, and Axel Munk. "Multiscale blind source separation." Annals of Statistics 46, no. 2 (2018): 711–44. http://dx.doi.org/10.1214/17-aos1565.

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Bachoc, François, Marc G. Genton, Klaus Nordhausen, Anne Ruiz-Gazen, and Joni Virta. "Spatial blind source separation." Biometrika 107, no. 3 (2020): 627–46. http://dx.doi.org/10.1093/biomet/asz079.

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Summary Recently a blind source separation model was suggested for spatial data, along with an estimator based on the simultaneous diagonalization of two scatter matrices. The asymptotic properties of this estimator are derived here, and a new estimator based on the joint diagonalization of more than two scatter matrices is proposed. The asymptotic properties and merits of the novel estimator are verified in simulation studies. A real-data example illustrates application of the method.
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Kemiha, Mina, and Abdellah Kacha. "Complex Blind Source Separation." Circuits, Systems, and Signal Processing 36, no. 11 (2017): 4670–87. http://dx.doi.org/10.1007/s00034-017-0539-0.

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Yang, Xiao Yan, Xiong Zhou, and Yi Ke Tang. "A New Method for Adaptive Blind Source Separation Based on the Estimated Number of Dynamic Fault Sources." Applied Mechanics and Materials 233 (November 2012): 211–17. http://dx.doi.org/10.4028/www.scientific.net/amm.233.211.

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In fault diagnosis of large rotating machinery, the number of fault sources may be subject to dynamic changes, which often lead to the failure in accurate estimation of the number of sources and the effective isolation of the fault source. This paper introduced the expansion of the fourth-order cumulant matrices in estimating the dynamic fault source number, plus the relationship between the source signal number and the number of sensors being utilized in the selection of the blind source separation algorithm to achieve adaptive blind source separation. Experiments showed that the source numbe
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Xu, Jiarui. "Application of blind source separation in sound source separation." Journal of Physics: Conference Series 1345 (November 2019): 032006. http://dx.doi.org/10.1088/1742-6596/1345/3/032006.

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

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Chen, Lingguang, Sean F. Wu, Yong Xu, William D. Lyman, and Gaurav Kapur. "Blind Separation of Heart Sounds." Journal of Theoretical and Computational Acoustics 26, no. 01 (2018): 1750035. http://dx.doi.org/10.1142/s2591728517500359.

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This paper presents a theoretical foundation for the newly developed methodology that enables the prediction of blood pressures based on the heart sounds measured directly on the chest of a patient. The key to this methodology is the separation of heart sounds into first heart sound and second heart sound, from which components attributable to four heart valves, i.e.: mitral; tricuspid; aortic; and pulmonary valve-closure sounds are separated. Since human physiology and anatomy can vary among people and are unknown a priori, such separation is called blind source separation. Moreover, the sour
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Rozprawy doktorskie na temat "Blind source separation"

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Gao, Bin. "Single channel blind source separation." Thesis, University of Newcastle Upon Tyne, 2011. http://hdl.handle.net/10443/1300.

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Single channel blind source separation (SCBSS) is an intensively researched field with numerous important applications. This research sets out to investigate the separation of monaural mixed audio recordings without relying on training knowledge. This research proposes a novel method based on variable regularised sparse nonnegative matrix factorization which decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral basis and temporal code of the sources. In this work, a variational Bayesian approach has been developed for computing
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Abrar, Shafayat. "Blind channel equalization and instantaneous blind source separation." Thesis, University of Liverpool, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540044.

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Latif, Mohamed Amin. "Localization of brain signal sources using blind source separation." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/54567/.

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Reliable localization of brain signal sources by using convenient, easy, and hazardless data acquisition techniques can potentially play a key role in the understanding, analysis, and tracking of brain activities for determination of physiological, pathological, and functional abnormalities. The sources can be due to normal brain activities, mental disorders, stimulation of the brain, or movement related tasks. The focus of this thesis is therefore the development of novel source localization techniques based upon EEG measurements. Independent component analysis is used in blind separation (BS
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Naqvi, Syed Mohsen Raza. "Multimodal methods for blind source separation of audio sources." Thesis, Loughborough University, 2009. https://dspace.lboro.ac.uk/2134/36117.

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The enhancement of the performance of frequency domain convolutive blind source separation (FDCBSS) techniques when applied to the problem of separating audio sources recorded in a room environment is the focus of this thesis. This challenging application is termed the cocktail party problem and the ultimate aim would be to build a machine which matches the ability of a human being to solve this task. Human beings exploit both their eyes and their ears in solving this task and hence they adopt a multimodal approach, i.e. they exploit both audio and video modalities. New multimodal methods for
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Khor, Li Chin. "Blind source separation under model misfits." Thesis, University of Newcastle upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490154.

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Blind Signal Separation (BSS) is a statistical signal processing-based technique and has recently been developed for many potential applications. This thesis aims to investigate model misfits in BSS problems as well as identify and develop efficient solutions for enhancing the performance of signal separation. This research sets out to investigate model misfits associated with finite signal sample size, mixing model, source signal and noise models. The effects of finite signal sample size on several well-known cost functions have been studied and this thesis has identified the most optimal cos
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Klajman, Maurice. "Mixed statistics in blind source separation." Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406683.

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Zhou, Lihong. "Blind source separation systems for hearing aids." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28395.

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For many real-life situations, there is more than one speaker at a given time and people need to concentrate on a target sound signal to extract it. This process happens naturally for people with a normal hearing ability, but it is very difficult for hearing impaired persons. In this thesis, we present a system for enhancing the quality of the signal produced by a hearing aid. The proposed system combines spatial information with blind source separation (BSS) to extract the target signal. Results show that the proposed system can locate a target signal in different environments, with a good le
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Smith, Paul Carson. "Broadband analog opto-electronic blind source separation." Diss., Connect to online resource, 2005. http://wwwlib.umi.com/dissertations/fullcit/3178354.

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Badran, Salah Al-Din Ibrahim. "Efficient multiband algorithms for blind source separation." Thesis, De Montfort University, 2016. http://hdl.handle.net/2086/16089.

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The problem of blind separation refers to recovering original signals, called source signals, from the mixed signals, called observation signals, in a reverberant environment. The mixture is a function of a sequence of original speech signals mixed in a reverberant room. The objective is to separate mixed signals to obtain the original signals without degradation and without prior information of the features of the sources. The strategy used to achieve this objective is to use multiple bands that work at a lower rate, have less computational cost and a quicker convergence than the conventional
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Anemüller, Jörn. "Across-frequency processing in convolutive blind source separation." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962819247.

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Książki na temat "Blind source separation"

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Xiang, Yong, Dezhong Peng, and Zuyuan Yang. Blind Source Separation. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-227-2.

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Naik, Ganesh R., and Wenwu Wang, eds. Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4.

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Yu, Xianchuan, Dan Hu, and Jindong Xu. Blind Source Separation. John Wiley & Sons, Singapore Pte. Ltd, 2014. http://dx.doi.org/10.1002/9781118679852.

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Deville, Yannick, Leonardo Tomazeli Duarte, and Shahram Hosseini. Nonlinear Blind Source Separation and Blind Mixture Identification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64977-7.

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Bourgeois, Julien, and Wolfgang Minker, eds. Time-Domain Beamforming and Blind Source Separation. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-68836-7.

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Shi, Xizhi. Blind signal processing: Theory and practice. Shanghai Jiao Tong University Press, 2011.

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Comon, Pierre. Handbook of Blind Source Separation: Independent Component Analysis and Applications. Elsevier, 2010.

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author, Sun Jiande, and Xu Hongji author, eds. Mang xin hao chu li li lun yu ying yong. Ke xue chu ban she, 2013.

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Andrzej, Cichocki, ed. Non-negative matrix and tensor factorizations: Applications to exploratory multiway data analysis and blind source separation. John Wiley, 2009.

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author, Feng Jiuchao 1964, and Xie Zhigang 1965-, eds. Chuan gan qi wang luo zhong de mang yuan fen li yu xin hao chong gou: Blind Source Separation and Signal Reconstruction in Sensor Networks. Dian zi gong ye chu ban she, 2012.

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Części książek na temat "Blind source separation"

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Jang, Gil-Jin, and Te-Won Lee. "Monaural Source Separation." In Blind Speech Separation. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6479-1_12.

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Deville, Yannick, and Alain Deville. "Quantum-Source Independent Component Analysis and Related Statistical Blind Qubit Uncoupling Methods." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_1.

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Saruwatari, Hiroshi, and Ryoichi Miyazaki. "Statistical Analysis and Evaluation of Blind Speech Extraction Algorithms." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_10.

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Wang, Lin, Heping Ding, and Fuliang Yin. "Speech Separation and Extraction by Combining Superdirective Beamforming and Blind Source Separation." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_11.

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Hummersone, Christopher, Toby Stokes, and Tim Brookes. "On the Ideal Ratio Mask as the Goal of Computational Auditory Scene Analysis." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_12.

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Geravanchizadeh, Masoud, and Reza Ahmadnia. "Monaural Speech Enhancement Based on Multi-threshold Masking." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_13.

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Rafii, Zafar, Antoine Liutkus, and Bryan Pardo. "REPET for Background/Foreground Separation in Audio." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_14.

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Hu, Hongmei, Guoping Li, Mark E. Lutman, and Stefan Bleeck. "Nonnegative Matrix Factorization Sparse Coding Strategy for Cochlear Implants." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_15.

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Martín-Clemente, Rubén. "Exploratory Analysis of Brain with ICA." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_16.

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Teschendorff, Andrew E., Emilie Renard, and Pierre A. Absil. "Supervised Normalization of Large-Scale Omic Datasets Using Blind Source Separation." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_17.

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Streszczenia konferencji na temat "Blind source separation"

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Chambers, J., and Wenwu Wang. "Frequency domain blind source separation." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040612.

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James, C. J. "Introduction and overview of the BSS/ICA problem - specifically when applied to biomedicine." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040611.

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Shoker, L., and S. Sanei. "Artefact removal from EEGs using a hybrid BSS-SVM algorithm." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040616.

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El-Deredy, W. "EEG dipole source localisation using independent component analysis: single trial analysis of laser evoked pain." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040617.

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Hesse, C. "BSS for EEG signal pre-processing and feature extraction in seizure onset analysis." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040618.

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Girolami, M. "Statistical and probabilistic fundamentals of ICA." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040613.

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Everson, R. "Non-stationary ICA." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040614.

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Langley, P. "Extracting the atrial signal from the electrocardiogram in atrial fibrillation." In IEE Seminar on Blind Source Separation in Biomedicine. IEE, 2004. http://dx.doi.org/10.1049/ic:20040615.

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Yang, Junjie, Zuyuan Yang, Yi Guo, and Shengli Xie. "Blind Source Separation." In the 9th International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3057039.3057055.

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Kisilev, Pavel, Michael Zibulevsky, Yehoshua Y. Zeevi, and Barak A. Pearlmutter. "Multiscale blind source separation." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Harold H. Szu, David L. Donoho, Adolf W. Lohmann, William J. Campbell, and James R. Buss. SPIE, 2001. http://dx.doi.org/10.1117/12.421206.

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Raporty organizacyjne na temat "Blind source separation"

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Zokay, Mustapha, and Hicham Saylani. Removing specular reflection in multispectral dermatological images using blind source separation. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2306p8383322.

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Xu, Pengfei, and Yinjie Jia. Blind Source Separation for Chirp Signals Based on the Local Quadratic Regression Smoothing. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2020. http://dx.doi.org/10.7546/crabs.2020.11.13.

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Hoffman, Jeffrey. Using Blind Source Separation and a Compact Microphone Array to Improve the Error Rate of Speech Recognition. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.5258.

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