Paper Info
Title
Segregating event streams and noise with a Markov renewal process model
Abstract
We describe an inference task in which a set of timestamped event observations must be clustered into an unknown number of temporal sequences with independent and varying rates of observations. Various existing approaches to multi-object tracking assume a fixed number of sources and/or a fixed observation rate; we develop an approach to inferring structure in timestamped data produced by a mixture of an unknown and varying number of similar Markov renewal processes, plus independent clutter noise. The inference simultaneously distinguishes signal from noise as well as clustering signal observations into separate source streams. We illustrate the technique via synthetic experiments as well as an experiment to track a mixture of singing birds. Source code is available.
Year
DOI
Venue
2013
10.5555/2567709.2567732
Journal of Machine Learning Research
Keywords
DocType
Volume
unknown number,segregating event stream,fixed observation rate,varying number,inference task,independent clutter noise,clustering signal observation,distinguishes signal,markov renewal process model,source code,separate source stream,fixed number,flow network,point processes,clustering,sound
Journal
14
Issue
ISSN
Citations 
Issue-in-Progress
Journal of Machine Learning Research, 14(Aug):2213-2238, 2013
10
PageRank 
References 
Authors
0.79
12
2
Name
Order
Citations
PageRank
Dan Stowell120921.84
M. D. Plumbley21915202.38