Abstract
Wireless acoustic sensor network (WASN) has shown great potential for speech enhancement compared with traditional microphone arrays. As dealing with all sensor measurements demands a powerful centralized processor, a distributed framework for speech enhancement is developed in this work. We propose to suppress noises by jointly exploiting the temporal and spatial information of audio signals, based on the information weighted consensus (IWC) filtering. In order to reduce the computational cost as well as the transmission power, we activate only an informative subnetwork by maximizing the energy efficiency in the WASN, where the subnetwork connectivity is guaranteed by incorporating more constraints. To select sensors rapidly, we propose a near-optimal greedy method, which removes one sensor at each iteration until the subnetwork size equals a preset value. The proposed method can obtain the high-quality target signal in a distributed and energy-efficient way. Simulation and real-world experiment results confirm its validity.
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