Energy-vs-performance trade-offs in speech enhancement in wireless acoustic sensor networks

Abstract

Distributed algorithms allow wireless acoustic sensor networks (WASNs) to divide the computational load of signal processing tasks, such as speech enhancement, among the sensor nodes. However, current algorithms focus on performance optimality, oblivious to the energy constraints that battery-powered sensor nodes usually face. To extend the lifetime of the network, nodes should be able to dynamically scale down their energy consumption when decreases in performance are tolerated. In this paper we study the relationship between energy and performance in the DANSE algorithm applied to speech enhancement. We propose two strategies that introduce flexibility to adjust the energy consumption and the desired performance. To analyze the impact of these strategies we combine an energy model with simulations. Results show that the energy consumption can be substantially reduced depending on the tolerated decrease in performance. This shows significant potential for extending the network lifetime using dynamic system reconfiguration.