Learn More
A wireless acoustic sensor network is envisaged that relies on a collection of spatially distributed microphones, which observe a speech signal together with additive background noise. The microphone signals are sent to a fusion center where they are filtered and combined to produce an estimate of the speech signal. In order to save energy and extend(More)
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 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 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 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)
A general binaural noise reduction system is considered that employs the multichannel Wiener filter with partial noise estimation (MWF<sub><i>&eta;</i></sub>) allowing for an explicit tradeoff between noise reduction and binaural noise cue preservation. In this paper, it is assumed that along with the general binaural system, a remote microphone signal with(More)