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Academic literature on the topic 'Blind source separation'

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Published: 4 June 2021
Last updated: 20 July 2024

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Journal articles on the topic "Blind source separation"

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Frikel, Miloud, Victor Barroso, and Joao Xavier. "Blind source separation." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 1101–2. http://dx.doi.org/10.1121/1.425160.

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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 independent components. The proposed algorithms are tested by real Electroencephalogram (EEG) data, the experimental results indicate that the algorithms can quickly and effectively get optimum solution to linear blind source separation compared to classical BSS techniques, the proposed works are described by high accuracy and robustness.
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Behr, Merle, Chris Holmes, and Axel Munk. "Multiscale blind source separation." Annals of Statistics 46, no. 2 (April 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 (February 17, 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 (March 28, 2017): 4670–87. http://dx.doi.org/10.1007/s00034-017-0539-0.

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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 (March 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 (March 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 sources locations, their surroundings and boundary conditions are unspecified. Consequently, it is not possible to obtain an exact separation of signals. To circumvent this difficulty, we extend the point source separation method in this paper to an inhomogeneous fluid medium, and further combine it with iteration schemes to search for approximate source locations and signal propagation speed. Once these are accomplished, the signals emitted from individual sources are separated by deconvoluting mixed signals with respect to the identified sources. Both numerical simulation example and experiment have demonstrated that this approach can provide satisfactory source separation results.
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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 number estimation algorithm could be quite effective in estimating the dynamic number of fault sources, even in the underdetermined condition. This adaptive blind source separation algorithm could then effectively achieve fault diagnosis in respect to the positive-determined, overdetermined and underdetermined blind source separation.
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Amari, S. "Superefficiency in blind source separation." IEEE Transactions on Signal Processing 47, no. 4 (April 1999): 936–44. http://dx.doi.org/10.1109/78.752592.

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Dissertations / Theses on the topic "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 the sparsity parameters of the matrix factorization. To further improve the previous work, this research proposes a new method based on decomposing the mixture into a series of oscillatory components termed as the intrinsic mode functions (IMF). It is shown that IMFs have several desirable properties unique to SCBSS problem and how these properties can be advantaged to relax the constraints posed by the problem. In addition, this research develops a novel method for feature extraction using psycho-acoustic model. The monaural mixed signal is transformed to a cochleagram using the gammatone filterbank, whose bandwidths increase incrementally as the center frequency increases; thus resulting to non-uniform time-frequency (TF) resolution in the analysis of audio signal. Within this domain, a family of Itakura-Saito (IS) divergence based novel two-dimensional matrix factorization has been developed. The proposed matrix factorizations have the property of scale invariant which enables lower energy components in the cochleagram to be treated with equal importance as the high energy ones. Results show that all the developed algorithms presented in this thesis have outperformed conventional methods.
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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 (BSS) of the EEG sources to yield three different approaches for source localization. In the first method the sources are localized over the scalp pattern using BSS in various subbands, and by investigating the number of components which are likely to be the true sources. In the second method, the sources are separated and their corresponding topographical information is used within a least-squares algorithm to localize the sources within the brain region. The locations of the known sources, such as some normal brain rhythms, are also utilized to help in determining the unknown sources. The final approach is an effective BSS algorithm partially constrained by information related to the known sources. In addition, some investigation have been undertaken to incorporate non-homogeneity of the head layers in terms of the changes in electrical and magnetic characteristics and also with respect to the noise level within the processing methods. Experimental studies with real and synthetic data sets are undertaken using MATLAB and the efficacy of each method discussed.
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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 blind source separation of audio sources are therefore proposed in this work as a step towards realizing such a machine. The geometry of the room environment is initially exploited to improve the separation performance of a FDCBSS algorithm. The positions of the human speakers are monitored by video cameras and this information is incorporated within the FDCBSS algorithm in the form of constraints added to the underlying cross-power spectral density matrix-based cost function which measures separation performance.
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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 cost function in separating signals with and without the presence of noise. A set of statistical tests is further developed to measure the performance in terms of speed, accuracy and convergence of the tested BSS algorithms. This work further explores the limitations of conventional assumptions of the noiseless and square mixing model which are often violated in practice and result in poor performance in signal separation. The separation of underdetermined mixing models as well as the assumptions of the source signals and noise are also addressed. This thesis presents the development of a Bayesian framework for underdetermined mixtures that produce accurate results in the estimation of mixing matrix and signals corrupted by noise. The proposed algorithm for underdetermined mixtures is capable of modelling a wide variety of signals ranging from unimodal to multimodal and symmetric to nonsymmetric signals. An integrated noise reduction procedure provides robustness against Gaussian noise and the commonly neglected non-Gaussian noise. Results justify the customisation of an algorithm for underdetermined mixtures and demonstrate the efficacy of the proposed algorithm which is three to five times better than existing algorithms. Finally, the work investigates another model misfit in the form of nonlinearly mixed signals and the difficulty of the problem. An algorithm that accurately separates nonlinear mixtures in the presence of noise is proposed. This algorithm features a system that maintains efficient convergence rate while minimising the risk of divergence regardless of the initialised parameters. There is also a mechanism that ameliorates global convergence. Results show that the proposed algorithm outperforms existing algorithms by at least three times with its features that simultaneously address the two crucial issues in the blind separation of nonlinear mixtures.
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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 learning ability. The problem of locating and extracting a target source signal is first investigated. By applying a time-frequency masking method, it is then shown that the performance can be improved. Finally, the problem of underdetermined BSS is investigated and solved by combining a MVDR beamformer with a determined BSS system.
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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 scheme. Our motivation is the competitive results of unequal-passbands scheme applications, in terms of the convergence speed. The objective of this research is to improve unequal-passbands schemes by improving the speed of convergence and reducing the computational cost. The first proposed work is a novel maximally decimated unequal-passbands scheme. This scheme uses multiple bands that make it work at a reduced sampling rate, and low computational cost. An adaptation approach is derived with an adaptation step that improved the convergence speed. The performance of the proposed scheme was measured in different ways. First, the mean square errors of various bands are measured and the results are compared to a maximally decimated equal-passbands scheme, which is currently the best performing method. The results show that the proposed scheme has a faster convergence rate than the maximally decimated equal-passbands scheme. Second, when the scheme is tested for white and coloured inputs using a low number of bands, it does not yield good results; but when the number of bands is increased, the speed of convergence is enhanced. Third, the scheme is tested for quick changes. It is shown that the performance of the proposed scheme is similar to that of the equal-passbands scheme. Fourth, the scheme is also tested in a stationary state. The experimental results confirm the theoretical work. For more challenging scenarios, an unequal-passbands scheme with over-sampled decimation is proposed; the greater number of bands, the more efficient the separation. The results are compared to the currently best performing method. Second, an experimental comparison is made between the proposed multiband scheme and the conventional scheme. The results show that the convergence speed and the signal-to-interference ratio of the proposed scheme are higher than that of the conventional scheme, and the computation cost is lower than that of the conventional scheme.
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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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Books on the topic "Blind source separation"

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Xiang, Yong, Dezhong Peng, and Zuyuan Yang. Blind Source Separation. Singapore: 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. Berlin, Heidelberg: 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. Singapore: 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. Cham: 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. Boston, MA: 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: Shanghai Jiao Tong University Press, 2011.

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

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

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Mang xin hao chu li ji chu ji qi ying yong: Blind signal processing foundation and it's applications. Beijing Shi: Guo fang gong ye chu ban she, 2010.

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C, Loizou Philipos, ed. Advances in modern blind signal separation algorithms: Theory and applications. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2010.

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Book chapters on the topic "Blind source separation"

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Jang, Gil-Jin, and Te-Won Lee. "Monaural Source Separation." In Blind Speech Separation, 339–64. Dordrecht: 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, 3–37. Berlin, Heidelberg: 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, 291–322. Berlin, Heidelberg: 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, 323–48. Berlin, Heidelberg: 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, 349–68. Berlin, Heidelberg: 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, 369–93. Berlin, Heidelberg: 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, 395–411. Berlin, Heidelberg: 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, 413–34. Berlin, Heidelberg: 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, 435–63. Berlin, Heidelberg: 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, 465–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_17.

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Conference papers on the topic "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. New York, New York, USA: 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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Reports on the topic "Blind source separation"

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Zokay, Mustapha, and Hicham Saylani. Removing specular reflection in multispectral dermatological images using blind source separation. Peeref, June 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, November 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, January 2000. http://dx.doi.org/10.15760/etd.5258.

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