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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.
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.
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