Talks and Poster Presentations (with Proceedings-Entry):
G. Kail, F. Hlawatsch, C. Novak:
"Efficient Bayesian detection of multiple events with a minimum-distance constraint";
Talk: IEEE-SP Workshop on Statistical Signal Processing (SSP),
- 09-03-2009; in: "IEEE/SP 15th Workshop on Statistical Signal Processing, 2009. SSP '09.",
We propose a Bayesian method for detecting multiple events in signals under the practically relevant assumption that successive events may not be arbitrarily close and distant events are effectively independent. Our detector has low complexity since it involves only the (Monte Carlo approximation to the) one-dimensional marginal posteriors. However, its performance is good since the metric it minimizes
depends on the entire event sequence. We also describe an efficient sequential implementation of our detector that is based on a tree representation and a recursive metric computation.
event detection, pulse detection, Bayesian analysis, Monte Carlo method
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.