This book presents the signal processing algorithms that have been developed to process the
signals acquired by a spherical microphone array. Spherical microphone arrays can be used to
capture the sound field in three dimensions and have received significant interest from
researchers and audio engineers. Algorithms for spherical array processing are different to
corresponding algorithms already known in the literature of linear and planar arrays because
the spherical geometry can be exploited to great beneficial effect. The authors aim to advance
the field of spherical array processing by helping those new to the field to study it
efficiently and from a single source as well as by offering a way for more experienced
researchers and engineers to consolidate their understanding adding either or both of breadth
and depth. The level of the presentation corresponds to graduate studies at MSc and PhD level.
This book begins with a presentation of some of the essential mathematical and physical theory
relevant to spherical microphone arrays and of an acoustic impulse response simulation method
which can be used to comprehensively evaluate spherical array processing algorithms in
reverberant environments. The chapter on acoustic parameter estimation describes the way in
which useful descriptions of acoustic scenes can be parameterized and the signal processing
algorithms that can be used to estimate the parameter values using spherical microphone arrays.
Subsequent chapters exploit these parameters including in particular measures of
direction-of-arrival and of diffuseness of a sound field. The array processing algorithms are
then classified into two main classes each described in a separate chapter. These are
signal-dependent and signal-independent beamforming algorithms. Although signal-dependent
beamforming algorithms are in theory able to provide better performance compared to the
signal-independent algorithms they are currently rarely used in practice. The main reason for
this is that the statistical information required by these algorithms is difficult to estimate.
In a subsequent chapter it is shown how the estimated acoustic parameters can be used in the
design of signal-dependent beamforming algorithms. This final step closes at least in part
the gap between theory and practice.
