196 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
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 Speech Distortion and Interference Rejection Constraint Beamformer
Signals captured by a set of microphones in a speech communication system are mixtures of desired and undesired signals and ambient noise. Existing beamformers can be divided into those that preserve or distort the desired signal. Beamformers that preserve the desired signal are, for example, the linearly constrained minimum variance (LCMV) beamformer that is supposed, ideally, to reject the undesired signal and reduce the ambient noise power, and the minimum variance distortionless response (MVDR) beamformer that reduces the interference-plus-noise power. The multichannel Wiener filter, on the other hand, reduces the interference-plus-noise power without preserving the desired signal. In this paper, a speech distortion and interference rejection constraint (SDIRC) beamformer is derived that minimizes the ambient noise power subject to specific constraints that allow a tradeoff between speech distortion and interference-plus-noise reduction on the one hand, and undesire d signal and ambient noise reductions on the other hand. Closed-form expressions for the performance measures of the SDIRC beamformer are derived and the relations to the aforementioned beamformers are derived. The performance evaluation demonstrates the tradeoffs that can be made using the SDIRC beamformer
Covariance Blocking and Whitening Method for Successive Relative Transfer Function Vector Estimation in Multi-Speaker Scenarios
This paper addresses the challenge of estimating the relative transfer
function (RTF) vectors of multiple speakers in a noisy and reverberant
environment. More specifically, we consider a scenario where two speakers
activate successively. In this scenario, the RTF vector of the first speaker
can be estimated in a straightforward way and the main challenge lies in
estimating the RTF vector of the second speaker during segments where both
speakers are simultaneously active. To estimate the RTF vector of the second
speaker the so-called blind oblique projection (BOP) method determines the
oblique projection operator that optimally blocks the second speaker. Instead
of blocking the second speaker, in this paper we propose a covariance blocking
and whitening (CBW) method, which first blocks the first speaker and applies
whitening using the estimated noise covariance matrix and then estimates the
RTF vector of the second speaker based on a singular value decomposition. When
using the estimated RTF vectors of both speakers in a linearly constrained
minimum variance beamformer, simulation results using real-world recordings for
multiple speaker positions demonstrate that the proposed CBW method outperforms
the conventional BOP and covariance whitening methods in terms of
signal-to-interferer-and-noise ratio improvement.Comment: IEEE Workshop on Applications of Signal Processing to Audio and
Acoustics (WASPAA), New Paltz, NY, USA, Oct 22-25, 202
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