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Fundamentals of differential beamforming

Benesty, Jacob.

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Fundamentals of differential beamforming

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fundamentals of differential beamforming/ by Jacob Benesty, Jingdong Chen, Chao Pan.
作者:	Benesty, Jacob.
其他作者:	Chen, Jingdong.
出版者:	Singapore :Springer Singapore : : 2016.,
面頁冊數:	viii, 122 p. :ill. (some col.), digital ;24 cm.
內容註:	Introduction -- Problem Formulation -- Some Background -- Performance Measures Revisited -- Conventional Optimization -- Beampattern Design -- Joint Optimization.
Contained By:	Springer eBooks
標題:	Beamforming. -
電子資源:	http://dx.doi.org/10.1007/978-981-10-1046-0
ISBN:	9789811010460

Fundamentals of differential beamforming
Benesty, Jacob.

Fundamentals of differential beamforming[electronic resource] /by Jacob Benesty, Jingdong Chen, Chao Pan. - Singapore :Springer Singapore :2016. - viii, 122 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

Introduction -- Problem Formulation -- Some Background -- Performance Measures Revisited -- Conventional Optimization -- Beampattern Design -- Joint Optimization.

This book provides a systematic study of the fundamental theory and methods of beamforming with differential microphone arrays (DMAs), or differential beamforming in short. It begins with a brief overview of differential beamforming and some popularly used DMA beampatterns such as the dipole, cardioid, hypercardioid, and supercardioid, before providing essential background knowledge on orthogonal functions and orthogonal polynomials, which form the basis of differential beamforming. From a physical perspective, a DMA of a given order is defined as an array that measures the differential acoustic pressure field of that order; such an array has a beampattern in the form of a polynomial whose degree is equal to the DMA order. Therefore, the fundamental and core problem of differential beamforming boils down to the design of beampatterns with orthogonal polynomials. But certain constraints also have to be considered so that the resulting beamformer does not seriously amplify the sensors' self noise and the mismatches among sensors. Accordingly, the book subsequently revisits several performance criteria, which can be used to evaluate the performance of the derived differential beamformers. Next, differential beamforming is placed in a framework of optimization and linear system solving, and it is shown how different beampatterns can be designed with the help of this optimization framework. The book then presents several approaches to the design of differential beamformers with the maximum DMA order, with the control of the white noise gain, and with the control of both the frequency invariance of the beampattern and the white noise gain. Lastly, it elucidates a joint optimization method that can be used to derive differential beamformers that not only deliver nearly frequency-invariant beampatterns, but are also robust to sensors' self noise.

ISBN: 9789811010460

Standard No.: 10.1007/978-981-10-1046-0doiSubjects--Topical Terms:

2191580
Beamforming.

LC Class. No.: TK7871.67.A33

Dewey Class. No.: 621.3822

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520 $a This book provides a systematic study of the fundamental theory and methods of beamforming with differential microphone arrays (DMAs), or differential beamforming in short. It begins with a brief overview of differential beamforming and some popularly used DMA beampatterns such as the dipole, cardioid, hypercardioid, and supercardioid, before providing essential background knowledge on orthogonal functions and orthogonal polynomials, which form the basis of differential beamforming. From a physical perspective, a DMA of a given order is defined as an array that measures the differential acoustic pressure field of that order; such an array has a beampattern in the form of a polynomial whose degree is equal to the DMA order. Therefore, the fundamental and core problem of differential beamforming boils down to the design of beampatterns with orthogonal polynomials. But certain constraints also have to be considered so that the resulting beamformer does not seriously amplify the sensors' self noise and the mismatches among sensors. Accordingly, the book subsequently revisits several performance criteria, which can be used to evaluate the performance of the derived differential beamformers. Next, differential beamforming is placed in a framework of optimization and linear system solving, and it is shown how different beampatterns can be designed with the help of this optimization framework. The book then presents several approaches to the design of differential beamformers with the maximum DMA order, with the control of the white noise gain, and with the control of both the frequency invariance of the beampattern and the white noise gain. Lastly, it elucidates a joint optimization method that can be used to derive differential beamformers that not only deliver nearly frequency-invariant beampatterns, but are also robust to sensors' self noise.
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