227 research outputs found
A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks with Arbitrary Topology
We propose a new robust distributed linearly constrained beamformer which
utilizes a set of linear equality constraints to reduce the cross power
spectral density matrix to a block-diagonal form. The proposed beamformer has a
convenient objective function for use in arbitrary distributed network
topologies while having identical performance to a centralized implementation.
Moreover, the new optimization problem is robust to relative acoustic transfer
function (RATF) estimation errors and to target activity detection (TAD)
errors. Two variants of the proposed beamformer are presented and evaluated in
the context of multi-microphone speech enhancement in a wireless acoustic
sensor network, and are compared with other state-of-the-art distributed
beamformers in terms of communication costs and robustness to RATF estimation
errors and TAD errors
Radio Astronomical Image Formation using Constrained Least Squares and Krylov Subspaces
Image formation for radio astronomy can be defined as estimating the spatial
power distribution of celestial sources over the sky, given an array of
antennas. One of the challenges with image formation is that the problem
becomes ill-posed as the number of pixels becomes large. The introduction of
constraints that incorporate a-priori knowledge is crucial. In this paper we
show that in addition to non-negativity, the magnitude of each pixel in an
image is also bounded from above. Indeed, the classical "dirty image" is an
upper bound, but a much tighter upper bound can be formed from the data using
array processing techniques. This formulates image formation as a least squares
optimization problem with inequality constraints. We propose to solve this
constrained least squares problem using active set techniques, and the steps
needed to implement it are described. It is shown that the least squares part
of the problem can be efficiently implemented with Krylov subspace based
techniques, where the structure of the problem allows massive parallelism and
reduced storage needs. The performance of the algorithm is evaluated using
simulations
Efficient calculation of sensor utility and sensor removal in wireless sensor networks for adaptive signal estimation and beamforming
Wireless sensor networks are often deployed over a large area of interest and therefore the quality of the sensor signals may vary significantly across the different sensors. In this case, it is useful to have a measure for the importance or the so-called "utility" of each sensor, e.g., for sensor subset selection, resource allocation or topology selection. In this paper, we consider the efficient calculation of sensor utility measures for four different signal estimation or beamforming algorithms in an adaptive context. We use the definition of sensor utility as the increase in cost (e.g., mean-squared error) when the sensor is removed from the estimation procedure. Since each possible sensor removal corresponds to a new estimation problem (involving less sensors), calculating the sensor utilities would require a continuous updating of different signal estimators (where is the number of sensors), increasing computational complexity and memory usage by a factor. However, we derive formulas to efficiently calculate all sensor utilities with hardly any increase in memory usage and computational complexity compared to the signal estimation algorithm already in place. When applied in adaptive signal estimation algorithms, this allows for on-line tracking of all the sensor utilities at almost no additional cost. Furthermore, we derive efficient formulas for sensor removal, i.e., for updating the signal estimator coefficients when a sensor is removed, e.g., due to a failure in the wireless link or when its utility is too low. We provide a complexity evaluation of the derived formulas, and demonstrate the significant reduction in computational complexity compared to straightforward implementations
A Phoneme-Scale Assessment of Multichannel Speech Enhancement Algorithms
In the intricate acoustic landscapes where speech intelligibility is
challenged by noise and reverberation, multichannel speech enhancement emerges
as a promising solution for individuals with hearing loss. Such algorithms are
commonly evaluated at the utterance level. However, this approach overlooks the
granular acoustic nuances revealed by phoneme-specific analysis, potentially
obscuring key insights into their performance. This paper presents an in-depth
phoneme-scale evaluation of 3 state-of-the-art multichannel speech enhancement
algorithms. These algorithms -- FasNet, MVDR, and Tango -- are extensively
evaluated across different noise conditions and spatial setups, employing
realistic acoustic simulations with measured room impulse responses, and
leveraging diversity offered by multiple microphones in a binaural hearing
setup. The study emphasizes the fine-grained phoneme-level analysis, revealing
that while some phonemes like plosives are heavily impacted by environmental
acoustics and challenging to deal with by the algorithms, others like nasals
and sibilants see substantial improvements after enhancement. These
investigations demonstrate important improvements in phoneme clarity in noisy
conditions, with insights that could drive the development of more personalized
and phoneme-aware hearing aid technologies.Comment: This is the preprint of the paper that we submitted to the Trends in
Hearing Journa
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