Distributed parameterized topology-independent noise reduction in acoustic sensor networks

Distributed noise reduction is a challenging task in acoustic sensor networks (ASNs). To address the problem, a distributed parameterized topology-independent noise reduction method is proposed in this paper. To be specific, the parameterized topology-independent distributed adaptive node-specific signal estimation (PTI-DANSE) is first presented in fully connected ASNs, where only one node uses its local signals and the compressed signals summation from the other nodes at a time to estimate signal statistics and update parameters. Then, to make all nodes estimate signal statistics and update parameters simultaneously, the relaxed simultaneous PTI-DANSE (rsPTI-DANSE) is proposed in a synchronous update fashion. Finally, through using in-network summation techniques, the distributed parameterized topology-independent noise reduction algorithm for any topology is obtained. The proposed algorithm is applicative for any network topology, and it is adjustable in terms of speech distortion and noise reduction. Simulation results and real-world experiments indicate that the proposed algorithm has good noise reduction performance.