Analysis of rate constraints for MWF-based noise reduction in acoustic sensor networks

Lawin-Ore, Toby Christian; Doclo, Simon

doi:10.1109/icassp.2011.5946392

Cited by 13 publications

(11 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we confine ourselves to the review of optimal distributed minimum-variance BF algorithms where nodes share (compressed) signals and parameters, and where the general aim is to achieve the same speech enhancement performance as obtained with a centralized minimum-variance BF. We mainly focus on the BF algorithm design challenges, and we disregard several other (but equally important) challenges, such as synchronization [29]- [32], node subset selection [33], [34], topology selection, distortion due to audio compression [22], [35], [36], packet loss, input-output delay management [37], etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks

et al. 2015

View full text Add to dashboard Cite

In multiple speaker scenarios, the so-called linearly constrained minimum variance (LCMV) beamformer is a popular microphone array-based speech enhancement technique, as it allows minimizing the noise power while maintaining a set of desired responses towards the different speakers. In this paper, we address the algorithmic challenges arising in the application of the LCMV beamformer in so-called wireless acoustic sensor networks (WASNs), which are a next-generation technology for audio acquisition and processing. We review three optimal distributed LCMV-based algorithms, which compute a networkwide LCMV beamformer output at each node without centralizing the microphone signals. Optimality here refers to the fact that the algorithms theoretically generate the same beamformer outputs as in a centralized realization where a single processor would have access to all the signals. We derive and motivate the algorithms in an accessible top-down framework that reveals the underlying relations between them, as well as their differences. We explain how these differences result from their different design criterion (node-specific versus common constraints sets), as well as their different priorities with respect to communication bandwidth, computational power, and adaptivity. Furthermore, although the three algorithms were originally proposed for a fully-connected WASN, we also explain how they can be extended to the case of a partially-connected WASN, which is assumed to be pruned to a tree topology. Finally, we discuss the advantages and disadvantages of the various algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Here, signal enhancement and coding can be viewed as cascaded techniques, but there also exist approaches where both are jointly tackled in a WASN context [30], [31]. It is obvious that such an integrated approach can yield better performance, since (lossy) compression inevitably has a negative effect on the signal enhancement algorithm (see, e.g., [32], which analyzes the effect of compression on DANSE).…”

Section: Distributed Compressionmentioning

confidence: 99%

Applications and trends in wireless acoustic sensor networks: A signal processing perspective

Bertrand

2011

2011 18th IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT)

214

153

View full text Add to dashboard Cite

Wireless microphone networks or so-called wireless acoustic sensor networks (WASNs) are a next-generation technology for audio acquisition and processing. As opposed to traditional microphone arrays that sample a sound field only locally, often at large distances from the relevant sound sources, WASNs allow to use many more microphones to cover a large area of interest. However, the design of such WASNs is very challenging, especially for real-time audio acquisition and signal enhancement due to the significant data traffic in the network. There is a need for scalable solutions, both on the signal processing level and on the network-communication level. In this paper, we give an overview of applications and trends in the field of WASNs, and we address the core challenges that need to be tackled. We mainly focus on the signal processing level, and we explain how advances in the area of signal processing can relax the high-demanding constraints on the network layer design. Furthermore, we address the interaction between the application layer and the network layer, and we explain why cross-layer design can be important to improve the performance of WASN applications.

show abstract

“…In particular, we revisit the so-called distributed adaptive nodespecific signal estimation (DANSE) algorithm, which operates in fully connected wireless sensor networks (WSNs) [1]- [4] or WSNs with a tree topology [5]. Each node acts as a data sink and fuses its local sensor signal observations with (compressed) signal observations obtained from other nodes, based on a local linear minimum mean squared error (MMSE) estimator.…”

Section: Introductionmentioning

confidence: 99%

Distributed signal estimation in sensor networks where nodes have different interests

Bertrand

Moonen

2012

Signal Processing

View full text Add to dashboard Cite

In this paper, we consider distributed signal estimation in sensor networks where the nodes exchange compressed sensor signal observations to estimate different node-specific signals. In particular, we revisit the so-called distributed adaptive node-specific signal estimation (DANSE) algorithm, which applies to the case where the nodes share a so-called 'common interest', and cast it in the more general setting where the nodes have 'different interests'. We prove existence of an equilibrium state for such a setting by using a result from fixed point theory. By establishing a link between the DANSE algorithm and game theory, we point out that any equilibrium of the DANSE algorithm is a Nash equilibrium of the corresponding game. This provides an intuitive interpretation to the resulting signal estimators. The equilibrium state existence proof also reveals a problem with discontinuities in the DANSE update function, which may result in non-convergence of the algorithm. However, since these discontinuities are identifiable, they can easily be avoided by applying a minor heuristic modification to the algorithm. We demonstrate the effectiveness of this modification by means of numerical examples.

show abstract

Analysis of rate constraints for MWF-based noise reduction in acoustic sensor networks

Cited by 13 publications

References 8 publications

Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks

Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks

Applications and trends in wireless acoustic sensor networks: A signal processing perspective

Distributed signal estimation in sensor networks where nodes have different interests

Contact Info

Product

Resources

About