Robust Distributed Noise Suppression in Acoustic Sensor Networks

Abstract

In acoustic sensor networks (ASNs), speech-related applications often perform poorly due to serious noise interference. To address this problem, a robust distributed noise suppression method for ASNs is proposed in this article. Specifically, by using the maximum input signal-to-noise ratio (SNR) subarray as the reference array, the robust distributed parameterized multichannel Wiener filter (RD-PMWF) is proposed for a tradeoff between the desired signal distortion and the noise suppression in the fully connected ASNs, where each subarray fuses its multiple speech signals into a single-channel signal by a compression vector. Then, to seek out the maximum input SNR subarray in any topology, an in-network compare method that takes place on the tree topology is presented based on a data-driven signal flow. Finally, the RD-PMWF without topology constraints is obtained, relying on the in-network comparison of input SNR, and the communication cost and computational complexity are analyzed. The proposed method is robust to the change of network topology and speaker positions even at low input SNR situations and successfully suppresses noise by using only the subarray with the maximum input SNR and its neighbors in any topology. Simulation and real-world experimental results illustrate that the proposed method achieves good noise suppression performance in noisy and reverberant ASNs while exhibiting an even lower computational complexity.