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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.
This work addresses this problem in the short-time Fourier
transform (STFT) domain. We divide the general problem into five
basic categories depending on the number of microphones being used
and whether the interframe or interband correlation is considered.
The first category deals with the single-channel problem where STFT
coefficients at different frames and frequency bands are assumed to
be independent. In this case, the noise reduction filter in each
frequency band is basically a real gain. Since a gain does not
improve the signal-to-noise ratio (SNR) for any given subband and
frame, the noise reduction is basically achieved by liftering the
subbands and frames that are less noisy while weighing down on
those that are more noisy. The second category also concerns the
single-channel problem. The difference is that now the interframe
correlation is taken into account and a filter is applied in each
subband instead of just a gain. The advantage of using the
interframe correlation is that we can improve not only the
long-time fullband SNR, but the frame-wise subband SNR as well. The
third and fourth classes discuss the problem of multichannel noise
reduction in the STFT domain with and without interframe
correlation, respectively. In the last category, we consider the
interband correlation in the design of the noise reduction filters.
We illustrate the basic principle for the single-channel case as an
example, while this concept can be generalized to other scenarios.
In all categories, we propose different optimization cost functions
from which we derive the optimal filters and we also define the
performance measures that help analyzing them.
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