Добірка наукової літератури з теми "Separation estimation"
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Статті в журналах з теми "Separation estimation":
Pishravian, Arash, and Masoud Reza Aghabozorgi Sahaf. "Application of independent component analysis for speech–music separation using an efficient score function estimation." Journal of Electrical Engineering 63, no. 6 (December 1, 2012): 380–85. http://dx.doi.org/10.2478/v10187-012-0056-0.
Xiao, Yingchun, Yang Yang, and Feng Zhu. "A Separation Method for Electromagnetic Radiation Sources of the Same Frequency." Journal of Electromagnetic Engineering and Science 23, no. 6 (November 30, 2023): 521–29. http://dx.doi.org/10.26866/jees.2023.6.r.197.
Asadi, Haniyeh, Mohammad T. Dastorani, Roy C. Sidle, and Kaka Shahedi. "Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation." Water 13, no. 24 (December 19, 2021): 3650. http://dx.doi.org/10.3390/w13243650.
Todorovic-Zarkula, Slavica, Branimir Todorovic, and Miomir Stankovic. "On-line blind separation of non-stationary signals." Yugoslav Journal of Operations Research 15, no. 1 (2005): 79–95. http://dx.doi.org/10.2298/yjor0501079t.
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.
Hasegawa, Yasuhisa, Mayumi Natsui, Chie Abe, Ayumi Ikeda, and Sean-Thomas B. Lundin. "Estimation of CO2 Separation Performances through CHA-Type Zeolite Membranes Using Molecular Simulation." Membranes 13, no. 1 (January 3, 2023): 60. http://dx.doi.org/10.3390/membranes13010060.
Lui, Hoi-Shun, and Hon Tat Hui. "Direction-of-Arrival Estimation of Closely Spaced Emitters Using Compact Arrays." International Journal of Antennas and Propagation 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/104848.
Yokoi, Masayuki, and Takao Tashiro. "Separating Prescription From Dispensation Medicines: Economic Effect Estimation in Japan." Global Journal of Health Science 10, no. 5 (April 11, 2018): 88. http://dx.doi.org/10.5539/gjhs.v10n5p88.
Reddy, Priyanka J., Vandana Pulhani, Sanjay D. Dhole, Shailesh S. Dahiwale, Sonali P. D. Bhade, and Devendra D. Rao. "Sequential analysis of uranium and plutonium in environmental matrices by extractive liquid scintillation spectrometry." Radiochimica Acta 106, no. 10 (October 25, 2018): 809–18. http://dx.doi.org/10.1515/ract-2017-2916.
Yang, ZuYuan, BeiHai Tan, GuoXu Zhou, and JinLong Zhang. "Source number estimation and separation algorithms of underdetermined blind separation." Science in China Series F: Information Sciences 51, no. 10 (September 14, 2008): 1623–32. http://dx.doi.org/10.1007/s11432-008-0138-6.
Дисертації з теми "Separation estimation":
Gunawan, David Oon Tao Electrical Engineering & Telecommunications Faculty of Engineering UNSW. "Musical instrument sound source separation." Awarded By:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/41751.
Parfitt, Maxwell. "Estimation of magnet separation for magnetic suspension applications." Thesis, University of Reading, 2013. http://centaur.reading.ac.uk/36656/.
Che, Viet Nhat Anh. "Cyclostationary analysis : cycle frequency estimation and source separation." Thesis, Saint-Etienne, 2011. http://www.theses.fr/2011STET4035.
Blind source separation problem aims to recover a set of statistically independent source signals from a set of sensor observations. These observations can be modeled as an instantaneous or convolutive mixture of the same sources. In this dissertation, the source signals are assumed to be cyclostationary where their cycle frequencies may be known or unknown a priori. First, we establish relations between the spectrum, power spectrum of a source signal and its component, then we propose two novel algorithms to estimate its cycle frequencies. Next, for blind separation of instantaneous mixtures of sources, we present four algorithms based on orthogonal (or non-orthogonal) approximate diagonalization of the multiple cyclic temporal moment matrices, and the matrix pencil approach to extract the source signal. We also introduce and prove a new identifiability condition to show which kind of input cyclostationary sources can be separated based on second-order cyclostationarity statistics. For blind separation of convolutive mixtures of sources signal or blind deconvolution of FIR MIMO systems, we present a two-steps algorithm based on time domain approach for recovering the source signals. Numerical simulations are used throughout this thesis to demonstrate the effectiveness of our proposed approaches, and compare theirs performances with previous methods
Meseguer, Brocal Gabriel. "Multimodal analysis : informed content estimation and audio source separation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS111.
This dissertation proposes the study of multimodal learning in the context of musical signals. Throughout, we focus on the interaction between audio signals and text information. Among the many text sources related to music that can be used (e.g. reviews, metadata, or social network feedback), we concentrate on lyrics. The singing voice directly connects the audio signal and the text information in a unique way, combining melody and lyrics where a linguistic dimension complements the abstraction of musical instruments. Our study focuses on the audio and lyrics interaction for targeting source separation and informed content estimation. Real-world stimuli are produced by complex phenomena and their constant interaction in various domains. Our understanding learns useful abstractions that fuse different modalities into a joint representation. Multimodal learning describes methods that analyse phenomena from different modalities and their interaction in order to tackle complex tasks. This results in better and richer representations that improve the performance of the current machine learning methods. To develop our multimodal analysis, we need first to address the lack of data containing singing voice with aligned lyrics. This data is mandatory to develop our ideas. Therefore, we investigate how to create such a dataset automatically leveraging resources from the World Wide Web. Creating this type of dataset is a challenge in itself that raises many research questions. We are constantly working with the classic ``chicken or the egg'' problem: acquiring and cleaning this data requires accurate models, but it is difficult to train models without data. We propose to use the teacher-student paradigm to develop a method where dataset creation and model learning are not seen as independent tasks but rather as complementary efforts. In this process, non-expert karaoke time-aligned lyrics and notes describe the lyrics as a sequence of time-aligned notes with their associated textual information. We then link each annotation to the correct audio and globally align the annotations to it. For this purpose, we use the normalized cross-correlation between the voice annotation sequence and the singing voice probability vector automatically, which is obtained using a deep convolutional neural network. Using the collected data we progressively improve that model. Every time we have an improved version, we can in turn correct and enhance the data
Lahlou, Mouncef. "Color-Based Surface Reflectance Separation for Scene Illumination Estimation and Rendering." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/381.
Becker, Saskia. "The Propagation-Separation Approach." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2014. http://dx.doi.org/10.18452/16960.
In statistics, nonparametric estimation is often based on local parametric modeling. For pointwise estimation of the target function, the parametric neighborhoods can be described by weights that depend on design points or on observations. As it turned out, the comparison of noisy observations at single points suffers from a lack of robustness. The Propagation-Separation Approach by Polzehl and Spokoiny [2006] overcomes this problem by using a multiscale approach with iteratively updated weights. The method has been successfully applied to a large variety of statistical problems. Here, we present a theoretical study and numerical results, which provide a better understanding of this versatile procedure. For this purpose, we introduce and analyse a novel strategy for the choice of the crucial parameter of the algorithm, namely the adaptation bandwidth. In particular, we study its variability with respect to the unknown target function. This justifies a choice independent of the data at hand. For piecewise constant and piecewise bounded functions, this choice enables theoretical proofs of the main heuristic properties of the algorithm. Additionally, we consider the case of a misspecified model. Here, we introduce a specific step function, and we establish a pointwise error bound between this function and the corresponding estimates of the Propagation-Separation Approach. Finally, we develop a method for the denoising of diffusion-weighted magnetic resonance data, which is based on the Propagation-Separation Approach. Our new procedure, called (ms)POAS, relies on a specific description of the data, which enables simultaneous smoothing in the measured positions and with respect to the directions of the applied diffusion-weighting magnetic field gradients. We define and justify two distance functions on the combined measurement space, where we follow a differential geometric approach. We demonstrate the capability of (ms)POAS on simulated and experimental data.
Han, Kun. "Supervised Speech Separation And Processing." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1407865723.
Liu, Yuzhou. "Deep CASA for Robust Pitch Tracking and Speaker Separation." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1566179636974186.
Umiltà, Caterina. "Development and assessment of a blind component separation method for cosmological parameter estimation." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066453/document.
The Planck satellite observed the whole sky at various frequencies in the microwave range. These data are of high value to cosmology, since they help understanding the primordial universe through the observation of the cosmic microwave background (CMB) signal. To extract the CMB information, astrophysical foreground emissions need to be removed via component separation techniques. In this work I use the blind component separation method SMICA to estimate the CMB angular power spectrum with the aim of using it for the estimation of cosmological parameters. In order to do so, small scales limitations as the residual contamination of unresolved point sources and the noise need to be addressed. In particular, the point sources are modelled as two independent populations with a flat angular power spectrum: by adding this information, the SMICA method is able to recover the joint emission law of point sources. Auto-spectra deriving from one sky map have a noise bias at small scales, while cross-spectra show no such bias. This is particularly true in the case of cross-spectra between data-splits, corresponding to sky maps with the same astrophysical content but different noise properties. I thus adapt SMICA to use data-split cross-spectra only. The obtained CMB spectra from simulations and Planck 2015 data are used to estimate cosmological parameters. Results show that this estimation can be biased if the shape of the (weak) foreground residuals in the angular power spectrum is not well known. In the end, I also present results of the study of a Modified Gravity model called Induced Gravity
Landqvist, Ronnie. "Signal processing techniques in mobile communication systems : signal separation, channel estimation and equalization /." Karlskrona : Blekinge Institute of Technology, 2005. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/98bf8bfb44d67d86c1257099003e2fc1?OpenDocument.
Книги з теми "Separation estimation":
Vergouw, Ir Jolanda. A conductivity probe for thickeners: Calibration and level estimation. [s.l: s.n.]., 1998.
National Aeronautics and Space Administration (NASA). Estimation of Supersonic Stage Separation Aerodynamics of Winged-Body Launch Vehicles Using Response Surface Methods. Independently Published, 2020.
National Aeronautics and Space Administration (NASA) Staff. Estimation of Supersonic Stage Separation Aerodynamics of Winged-Body Launch Vehicles Using Response Surface Methods. Independently Published, 2019.
Congendo, Marco, and Fernando H. Lopes da Silva. Event-Related Potentials. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0039.
Cardot, Hervé, and Pascal Sarda. Functional Linear Regression. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.
National Aeronautics and Space Administration (NASA) Staff. Separating Direct and Indirect Turbofan Engine Combustion Noise While Estimating Post-Combustion (Post-Flame) Residence Time Using the Correlation Function. Independently Published, 2019.
Kelly, Phil. Defending Classical Geopolitics. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.279.
Частини книг з теми "Separation estimation":
Zarzoso, V., and A. K. Nandi. "Blind Source Separation." In Blind Estimation Using Higher-Order Statistics, 167–252. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-2985-6_4.
King, M. B., O. J. Catchpole, and T. R. Bott. "Estimation of separation cost." In Extraction of Natural Products Using Near-Critical Solvents, 299–321. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2138-5_10.
Markovich-Golan, Shmulik, Walter Kellermann, and Sharon Gannot. "Multichannel Parameter Estimation." In Audio Source Separation and Speech Enhancement, 219–34. Chichester, UK: John Wiley & Sons Ltd, 2018. http://dx.doi.org/10.1002/9781119279860.ch11.
Chen, Jitong, and DeLiang Wang. "DNN Based Mask Estimation for Supervised Speech Separation." In Audio Source Separation, 207–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73031-8_9.
Daei, Sajad, Massoud Babaie-Zadeh, and Christian Jutten. "A MAP-Based Order Estimation Procedure for Sparse Channel Estimation." In Latent Variable Analysis and Signal Separation, 344–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22482-4_40.
Martínez-Guerra, Rafael, and Christopher Diego Cruz-Ancona. "A Separation Principle for Nonlinear Systems." In Algorithms of Estimation for Nonlinear Systems, 105–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53040-6_9.
Shaw, Jane E., and P. W. Ramwell. "Separation, Identification, and Estimation of Prostaglandins." In Methods of Biochemical Analysis, 325–71. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470110355.ch8.
Moreau, Eric, and Tülay Adali. "Estimation by Joint Diagonalization." In Blind Identification and Separation of Complex-Valued Signals, 27–45. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118579749.ch2.
Corey, Ryan M., and Andrew C. Singer. "Relative Transfer Function Estimation from Speech Keywords." In Latent Variable Analysis and Signal Separation, 238–47. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93764-9_23.
El Badawy, Dalia, Ivan Dokmanić, and Martin Vetterli. "Acoustic DoA Estimation by One Unsophisticated Sensor." In Latent Variable Analysis and Signal Separation, 89–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53547-0_9.
Тези доповідей конференцій з теми "Separation estimation":
Lacoume, J. L., and M. Gaeta. "The general source separation problem." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205565.
Feige, Uriel, and Shlomo Jozeph. "Separation between Estimation and Approximation." In ITCS'15: Innovations in Theoretical Computer Science. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2688073.2688101.
Cheng, Wei, Seungchul Lee, Zhousuo Zhang, and Zhengjia He. "Dissimilarity Measures for ICA-Based Source Number Estimation." In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7340.
Dunham, Darin T., and Scott E. August. "Kinematic separation point estimation using PMHT." In 2010 IEEE Aerospace Conference. IEEE, 2010. http://dx.doi.org/10.1109/aero.2010.5446683.
Jiang, Xiaoyun, and Noboru Ohta. "Maximum color separation in illuminant estimation." In Electronic Imaging 2004, edited by Reiner Eschbach and Gabriel G. Marcu. SPIE, 2003. http://dx.doi.org/10.1117/12.527445.
Dunham, Darin T., and Scott E. August. "Using PMHT for separation point estimation." In SPIE Defense, Security, and Sensing, edited by Oliver E. Drummond. SPIE, 2010. http://dx.doi.org/10.1117/12.851468.
Li Jiang, Lin Li, and Guo-qing Zhao. "Pulse-compression radar signal sorting using the blind source separation algrithms." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280204.
Dorfan, Yuval, Ofer Schwartz, Boaz Schwartz, Emanuel A. P. Habets, and Sharon Gannot. "Multiple DOA estimation and blind source separation using estimation-maximization." In 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE). IEEE, 2016. http://dx.doi.org/10.1109/icsee.2016.7806066.
Stephenson, Cory, Patrick Callier, Abhinav Ganesh, and Karl Ni. "Monaural speaker separation using source-contrastive estimation." In 2017 IEEE International Workshop on Signal Processing Systems (SiPS). IEEE, 2017. http://dx.doi.org/10.1109/sips.2017.8110005.
Karbancioglu, Ibrahim Murat, Gökmen Mahmutyazicioglu, and Kemal Ozgoren. "Estimation of Separation Aerodynamics Using Photogrammetric Data." In AIAA Atmospheric Flight Mechanics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-7952.
Звіти організацій з теми "Separation estimation":
Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.
Ferguson, Thomas, and Servaas Storm. Trump versus Biden: The Macroeconomics of the Second Coming. Institute for New Economic Thinking Working Paper Series, May 2024. http://dx.doi.org/10.36687/inetwp221.
Equipment Design and Cost Estimation for Small Modular Biomass Systems, Synthesis Gas Cleanup, and Oxygen Separation Equipment; Task 2.3: Sulfur Primer. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/882502.
Equipment Design and Cost Estimation for Small Modular Biomass Systems, Synthesis Gas Cleanup, and Oxygen Separation Equipment; Task 1: Cost Estimates of Small Modular Systems. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/882499.
Equipment Design and Cost Estimation for Small Modular Biomass Systems, Synthesis Gas Cleanup, and Oxygen Separation Equipment; Task 9: Mixed Alcohols From Syngas -- State of Technology. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/882503.
Equipment Design and Cost Estimation for Small Modular Biomass Systems, Synthesis Gas Cleanup, and Oxygen Separation Equipment; Task 2: Gas Cleanup Design and Cost Estimates -- Wood Feedstock. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/882500.
Equipment Design and Cost Estimation for Small Modular Biomass Systems, Synthesis Gas Cleanup, and Oxygen Separation Equipment; Task 2: Gas Cleanup Design and Cost Estimates -- Black Liquor Gasification. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/882504.