TITLE: Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks
AUTHORS: Shmulik Markovich-Golan, Alexander Bertrand, Marc Moonen and Sharon Gannot
ABSTRACT: In multiple speaker scenarios, the so-called 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 WASNs, which are a next-generation technology for audio acquisition and processing. We review three optimal distributed LCMV-based algorithms, which compute a network-wide 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.
STATUS: Published in Signal Processing, vol 107, pp. 4-20, Feb. 2015.(BibTeX entry)