Paper Info
Title
Distributed Robust Change Point Detection For Autoregressive Processes With An Application To Distributed Voice Activity Detection
Abstract
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.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP)
Change Point, Distributed Detection, Robust, Voice Activity, Autoregressive Process
Field
DocType
ISSN
Autoregressive model,Change detection,Computer science,Fault detection and isolation,Voice activity detection,Signal-to-noise ratio,Robustness (computer science),Real-time computing,Detector,Wireless sensor network
Conference
1520-6149
Citations 
PageRank 
References 
0
0.34
7
Authors
3
Name
Order
Citations
PageRank
Daniel Kalus100.34
Michael Muma214419.51
Abdelhak M. Zoubir31036148.03