Distributed robust change point detection for autoregressive processes with an application to distributed voice activity detection

The detection of abrupt changes in signals that are observed by wireless sensor networks (WSN), is an important research area with potential applications, e.g., in fault detection, prediction of natural catastrophic events, and speech segmentation. We consider the distributed robust detection of changes in the parameters of autoregressive (AR) models. Our method is robust on a single sensor level by suppressing the effect of outliers and impulsive noise via a robustified distance metric between a long-term and a short-term AR model. The new distributed change detector works without a fusion center and incorporates a weighting based on signal-to-noise-ratio (SNR) information, to ensure that every node will, at least, maintain its single node performance. A Monte-Carlo simulation study is provided which compares the proposed detector to a centralized version, in terms achievable detection rates and mean detection delay. Furthermore, an application example of distributed voice activity detection for a noisy speech signal is given.