Overview
- Presents cutting-edge theory and algorithms for spherical microphone arrays, including implementation examples and simulations with Matlab
- Relevant for a broad range of applications in audio and acoustics, including speech communication, music recording, room acoustics, acoustic holography, virtual acoustics, and binaural sound reproduction
- Is also relevant for other areas such as image processing and geodesy
Part of the book series: Springer Topics in Signal Processing (STSP, volume 16)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (7 chapters)
Keywords
- Linearly Constrained Minimum Variance (LCMV)
- Microphone Arrays
- Minimum Variance Distortionless Response (MVDR)
- Optimal Beamforming
- Plane-Wave Decomposition
- Rigid Sphere Array
- Spatial Sampling
- Spherical Fourier Transform
- Spherical Harmonics
- Spherical Array Beamforming
- Spherical Array Configurations
- Spherical Arrays
- Spherical Microphone Arrays
- Matlab Spherical Algorithm
About this book
This 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.
Mathworks kindly distributes the Matlab sources for this book on https://www.mathworks.com/matlabcentral/fileexchange/68655-fundamentals-of-spherical-array-processing.
Reviews
Authors and Affiliations
Bibliographic Information
Book Title: Fundamentals of Spherical Array Processing
Authors: Boaz Rafaely
Series Title: Springer Topics in Signal Processing
DOI: https://doi.org/10.1007/978-3-319-99561-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-319-99560-1Published: 11 October 2018
Softcover ISBN: 978-3-030-07611-5Published: 01 February 2019
eBook ISBN: 978-3-319-99561-8Published: 27 September 2018
Series ISSN: 1866-2609
Series E-ISSN: 1866-2617
Edition Number: 2
Number of Pages: XII, 193
Number of Illustrations: 49 b/w illustrations, 27 illustrations in colour
Topics: Signal, Image and Speech Processing, Acoustics, Geophysics/Geodesy, Engineering Acoustics