Rate-Distributed Spatial Filtering Based Noise Reduction in Wireless Acoustic Sensor Networks

TL;DR

This paper proposes a rate-distributed spatial filtering method for noise reduction in wireless acoustic sensor networks that optimizes energy consumption by allocating bit rates based on sensor importance and distance, outperforming sensor selection approaches.

Contribution

It introduces a semi-definite programming approach for rate allocation in WASNs, generalizing sensor selection and improving energy efficiency in noise reduction tasks.

Findings

Rate allocation depends on sensor signal statistics and distance.

The method outperforms sensor subset selection in energy efficiency.

Sensors near the fusion center and sources receive higher bit rates.

Abstract

In wireless acoustic sensor networks (WASNs), sensors typically have a limited energy budget as they are often battery driven. Energy efficiency is therefore essential to the design of algorithms in WASNs. One way to reduce energy costs is to only select the sensors which are most informative, a problem known as {\it sensor selection}. In this way, only sensors that significantly contribute to the task at hand will be involved. In this work, we consider a more general approach, which is based on rate-distributed spatial filtering. Together with the distance over which transmission takes place, bit rate directly influences the energy consumption. We try to minimize the battery usage due to transmission, while constraining the noise reduction performance. This results in an efficient rate allocation strategy, which depends on the underlying signal statistics, as well as the distance from sensors to a fusion center (FC). Under the utilization of a linearly constrained minimum variance (LCMV) beamformer, the problem is derived as a semi-definite program. Furthermore, we show that rate allocation is more general than sensor selection, and sensor selection can be seen as a special case of the presented rate-allocation solution, e.g., the best microphone subset can be determined by thresholding the rates. Finally, numerical simulations for the application of estimating several target sources in a WASN demonstrate that the proposed method outperforms the microphone subset selection based approaches in the sense of energy usage, and we find that the sensors close to the FC and close to point sources are allocated with higher rates.