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.
