Internet Web servers: workload characterization and performance implications
IEEE/ACM Transactions on Networking (TON)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
On the complexity of time table and multi-commodity flow problems
SFCS '75 Proceedings of the 16th Annual Symposium on Foundations of Computer Science
Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
Non-negative hidden Markov modeling of audio with application to source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Normalized Cuts for Predominant Melodic Source Separation
IEEE Transactions on Audio, Speech, and Language Processing
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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.