Joseph Szurley

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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(More)
A wireless acoustic sensor network is envisaged that is composed of distributed nodes each with several microphones. The goal of each node is to perform signal enhancement, by means of a multi-channel Wiener filter (MWF), in particular to produce an estimate of a desired speech signal. In order to reduce the number of broadcast signals between the nodes,(More)
Wireless microphone networks or so-called wireless acoustic sensor networks (WASNs) consist of physically distributed microphone nodes that exchange data over wireless links. In this paper, we propose a novel distributed distortionless signal estimation algorithm for noise reduction in WASNs. The most important feature of the proposed algorithm is that the(More)
A wireless acoustic sensor network is considered with spatially distributed microphones which observe a desired speech signal that has been corrupted by noise. In order to reduce the noise the signals are sent to a fusion center where they are processed with a centralized rank-1 multi-channel Wiener filter (R1-MWF). The goal of this work is to efficiently(More)
A topology-independent distributed adaptive node-specific signal estimation (<inline-formula> <tex-math notation="LaTeX">$\text{TI-DANSE}$</tex-math></inline-formula>) algorithm is presented where each node of a wireless sensor network (WSN) is tasked with estimating a node-specific desired signal. To reduce the amount of data exchange, each node applies a(More)
A wireless acoustic sensor network is considered that is used to estimate a desired speech signal that has been corrupted by noise. The application layer of the WASN derives an optimal filter in a linear MMSE sense. A utility function is then used in conjunction with the MMSE estimate in order to evaluate the most significant signal components from each(More)
Widely linear (WL) filtering has been shown to improve performance compared to linear filtering due to its ability to incorporate the non-circularity of the signal statistics. However there has been some inconsistency in its application, specifically when constructing complex signals from real signals, which has recently been considered in the context of(More)
We envisage a wireless sensor network (WSN) where each node is tasked with estimating a set of node-specific desired signals that has been corrupted by additive noise. The nodes accomplish this estimation by means of the distributed adaptive node-specific estimation (DANSE) algorithm in a tree topology (T-DANSE). In this paper, we consider a network where(More)