Distributed Speech Presence Probability Estimator in Fully Connected Wireless Acoustic Sensor Networks

This paper presents a Gaussian-based distributed speech presence probability (DSPP) estimator which is applied in fully connected wireless acoustic sensor networks (WASNs). In WASNs, we are primarily interested in optimally utilizing all available information of recorded signals. In this work, under the Gaussian statistical assumption of signals, each node computes the DSPP using its own local signals along with the compressed signals from other nodes. We evaluate the effect of DSPP estimation on noise reduction from both the simulated and the real recorded signals. The performance of the proposed DSPP estimator is compared to that of local SPP estimation, where each node only uses its noisy signals, and to that of centralized SPP estimation, where each node uses all recorded noisy signals of the whole network. It is shown that the proposed method exhibits good performance, while the computational complexity is considerably reduced.