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010   $a3-319-99561-8
024 7 $a10.1007/978-3-319-99561-8
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035   $a(DE-He213)978-3-319-99561-8
035   $a(PPN)230540090
035   $a(EXLCZ)994100000006674792
100   $a20180927d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aFundamentals of Spherical Array Processing /$fby Boaz Rafaely
205   $a2nd ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (201 pages)
225 1 $aSpringer Topics in Signal Processing,$x1866-2609 ;$v16
311   $a3-319-99560-X 
327   $aMathematical background -- Acoustical Background.-Sampling the Sphere -- Spherical array configurations -- Spherical Array Beamforming -- Optimal beam pattern design -- Beamforming with noise minimization.
330   $aThis book provides a comprehensive introduction to the theory and practice of spherical microphone arrays, and was written for graduate students, researchers and engineers who work with spherical microphone arrays in a wide range of applications. The new edition includes additions and modifications, and references supplementary Matlab code to provide the reader with a straightforward start for own implementations. The book is also accompanied by a Matlab manual, which explains how to implement the examples and simulations presented in the book. The first two chapters provide the reader with the necessary mathematical and physical background, including an introduction to the spherical Fourier transform and the formulation of plane-wave sound fields in the spherical harmonic domain. In turn, the third chapter covers the theory of spatial sampling, employed when selecting the positions of microphones to sample sound pressure functions in space. Subsequent chapters highlight various spherical array configurations, including the popular rigid-sphere-based configuration. Beamforming (spatial filtering) in the spherical harmonics domain, including axis-symmetric beamforming, and the performance measures of directivity index and white noise gain are introduced, and a range of optimal beamformers for spherical arrays, including those that achieve maximum directivity and maximum robustness are developed, along with the Dolph?Chebyshev beamformer. The final chapter discusses more advanced beamformers, such as MVDR (minimum variance distortionless response) and LCMV (linearly constrained minimum variance) types, which are tailored to the measured sound field.
410  0$aSpringer Topics in Signal Processing,$x1866-2609 ;$v16
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aAcoustics
606   $aGeophysics
606   $aAcoustical engineering
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
606   $aAcoustics$3https://scigraph.springernature.com/ontologies/product-market-codes/P21069
606   $aGeophysics/Geodesy$3https://scigraph.springernature.com/ontologies/product-market-codes/G18009
606   $aEngineering Acoustics$3https://scigraph.springernature.com/ontologies/product-market-codes/T16000
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615  0$aAcoustics.
615  0$aGeophysics.
615  0$aAcoustical engineering.
615 14$aSignal, Image and Speech Processing.
615 24$aAcoustics.
615 24$aGeophysics/Geodesy.
615 24$aEngineering Acoustics.
676   $a621.3822
700   $aRafaely$b Boaz$f1964-$4aut$4http://id.loc.gov/vocabulary/relators/aut$0862572
906   $aBOOK
912   $a9910337603303321
996   $aFundamentals of Spherical Array Processing$91925327
997   $aUNINA