@unpublished{murphy02, title = {{Hidden Semi-Markov Models (HSMMs)}}, author = {Murphy, Kevin P.}, note = {{Unpublished notes}}, year = {2002}, crossref = {murphy14}, url = {http://www.cs.ubc.ca/~murphyk/mypapers.html}, pdf = {http://www.cs.ubc.ca/~murphyk/Papers/segment.pdf}, annote = {Papier théorique, aucune application, mais les algorithmes sont expliqués et prouvés (notamment, discussion complexité du Forward-Backward).} }
@unpublished{murphy14, title = {{Hidden Semi-Markov Models (HSMMs)}}, author = {Murphy, Kevin P.}, note = {Tutorial slides}, year = {2014}, urldoi = {http://www.cs.ubc.ca/~murphyk/mypapers.html}, pdf = {http://colinlea.com/docs/JournalTalks/2014_HSMM.pdf}, annote = {Slides incompréhensibles de son papier murphy02.} }
@incollection{barbu2008, title = {{Hidden Semi-Markov Model and Estimation}}, author = {Barbu, Vlad and Limnios, Nikolaos}, booktitle = {{Semi-Markov Chains and Hidden Semi-Markov Models toward Applications}}, pages = {1--48}, year = {2008}, publisher = {Springer}, annote = {A voir.} }
@article{yu10, title = {{Hidden semi-Markov Models}}, author = {Yu, Shun-Zheng}, journal = {Artificial Intelligence}, volume = {174}, number = {2}, pages = {215--243}, year = {2010}, publisher = {Elsevier}, urldoi = {https://dx.doi.org/10.1016/j.artint.2009.11.011}, pdf = {http://sist.sysu.edu.cn/~syu/Publications/HSMMs_AIJ.pdf}, doi = {10.1016/j.artint.2009.11.011}, urldoi = {https://dx.doi.org/10.1016/j.artint.2009.11.011}, annote = {Eqs (13) and (14) give the alpha/beta recursion for HSMM (with logs).} }
@article{mhsmm, title = {{Hidden semi markov models for multiple observation sequences: The mhsmm package for R}}, author = {O'Connell, Jared and H{\o}jsgaard, S{\o}ren and others}, journal = {Journal of Statistical Software}, volume = {39}, number = {4}, pages = {1--22}, year = {2011}, publisher = {American Statistical Association}, pdf = {https://cran.r-project.org/web/packages/mhsmm/mhsmm.pdf}, url = {https://cran.r-project.org/web/packages/mhsmm/}, urldoi = {http://www.jstatsoft.org/htaccess.php?volume=39&type=i&issue=04}, annote = {p.8-9 give E-M steps for HSMM.} }
@article{rabiner89, title = {{A tutorial on Hidden Markov Models and selected applications in speech recognition}}, author = {Rabiner, Lawrence R}, journal = {Proceedings of the IEEE}, volume = {77}, number = {2}, pages = {257--286}, year = {1989}, publisher = {IEEE}, pdf = {http://www.robots.ox.ac.uk:5000/~vgg/rg/papers/hmm.pdf}, doi = {10.1109/5.18626}, urldoi = {https://dx.doi.org/10.1109/5.18626}, annote = {EM-steps for HSMM} }
@article{guedon03, title = {{Estimating Hidden Semi-markov chains from discrete sequences}}, author = {Gu{\'e}don, Yann}, journal = {Journal of Computational and Graphical Statistics}, volume = {12}, number = {3}, pages = {604--639}, year = {2003}, publisher = {Taylor \& Francis}, urldoi = {http://publications.cirad.fr/une_notice.php?dk=529866}, doi = {10.1198/1061860032030}, urldoi = {https://dx.doi.org/10.1198/1061860032030}, pdf = {http://hal.cirad.fr/file/index/docid/826992/filename/JCGSguedon2003.pdf}, annote = {cited in mhsmm, provides a version of the forward-backward algorithm for estimating the E-step of durations (eq. 8 in mhsmm)} }
@inproceedings{cuvillier14, title = {{Coherent time modeling of Semi-Markov Models with application to real-time audio-to-score alignment}}, author = {Cuvillier, Philippe and Cont, Arshia}, booktitle = {Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop}, pages = {1--6}, year = {2014}, organization = {IEEE}, urldoi = {https://hal.inria.fr/hal-01058366}, pdf = {https://hal.inria.fr/hal-01058366/document}, annote = {Court papier, application des HSMM à l'alignement musique-partition (piano). TODO: read it more in details!} }
@inproceedings{bach15, title = {{An online EM algorithm in Hidden (Semi-)Markov Models for audio segmentation and clustering}}, author = {Bietti, Alberto and Bach, Francis and Cont, Arshia}, booktitle = {Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference}, pages = {1881--1885}, year = {2015}, doi = {10.1109/ICASSP.2015.7178297}, urldoi = {https://dx.doi.org/10.1109/ICASSP.2015.7178297}, urldoi = {https://hal.inria.fr/hal-01115826}, pdf = {https://hal.inria.fr/hal-01115826/document}, annote = {Court papier, deux applications à la segmentation et classification audio. TODO: read it more in details!} }
@inproceedings{nakamura15, title = {{Autoregressive Hidden Semi-markov Model of symbolic music performance for score following}}, author = {Nakamura, Eita and Cuvillier, Philippe and Cont, Arshia and Ono, Nobutaka and Sagayama, Shigeki}, booktitle = {16th International Society for Music Information Retrieval Conference (ISMIR)}, address = {Malaga, Spain}, year = {2015}, keywords = {Score Following; MIDI; Semi-Markov; Computer Music}, urldoi = {https://hal.inria.fr/hal-01183820}, pdf = {https://hal.inria.fr/hal-01183820/file/draft_SemiMarkovMIDIScofo_ISMIR2015_v17.pdf}, annote = {TODO: read it more in details! Video https://www.youtube.com/watch?v=gasgH0A-m00.} }
@misc{hmmlearn, title = {{Unsupervised learning and inference of Hidden Markov Model in Python [hmmlearn] (GitHub repository)}}, author = {Lebedev, Sergei and GitHub contributors}, year = {2015}, month = {October}, editor = {{GitHub}}, url = {https://github.com/hmmlearn/hmmlearn/}, lastchecked = {28.12.2015}, originalyear = {15.10.2015}, note = {Online, accessed 28.12.2015}, annote = {For more details, see its online documentation at \url{https://hmmlearn.readthedocs.org/en/latest/}.} }
@misc{pyhsmm, title = {{Bayesian inference in HSMMs and HMMs in Python [pyhsmm] (GitHub repository)}}, author = {Johnson, Matthew J. and GitHub contributors}, year = {2015}, month = {December}, editor = {{GitHub}}, url = {https://github.com/mattjj/pyhsmm}, lastchecked = {28.12.2015}, originalyear = {26.12.2015}, note = {Online, accessed 28.12.2015}, annote = {{This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Non-Parametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.}} }
@article{johnson13, title = {{Bayesian non-parametric Hidden semi-Markov Models (HDP-HSMM)}}, author = {Johnson, Matthew J. and Willsky, Alan S.}, journal = {{Journal of Machine Learning Research}}, pages = {673--701}, volume = {14}, month = {February}, year = {2013}, urldoi = {https://github.com/mattjj/pyhsmm}, pdf = {http://www.jmlr.org/papers/volume14/johnson13a/johnson13a.pdf}, annote = {Excellent papier et code (pyhsmm). PhD Thesis at MIT by Matthew J. Johnson (2013).} }
@unpublished{cappe01, title = {{Ten years of HMMs}}, author = {Capp{\'e}, Olivier}, note = {{Unpublished notes on-line}}, year = {2001}, url = {http://perso.telecom-paristech.fr/~cappe/Research/Bibliographies/hmmbib/}, annote = {Une bibliographie de plus de 300 references concernant les HMMs (et les HSMMs).} }
@book{cappe06, title = {{Inference in Hidden Markov Models}}, author = {Capp{\'e}, Olivier and Moulines, Eric and Ryd{\'e}n, Tobias}, year = {2006}, publisher = {Springer Science \& Business Media}, annote = {Un gros livre. Utile pour alpha/beta (§3.2 p63) generalise au cas continu, et EM dans les HMM (§10.2 p361).} }
@book{koller09, title = {{Probabilistic Graphical Models}}, author = {Koller, Daphne and Friedman, Nir}, publisher = {MIT Press}, year = {2009}, series = {{Adaptive Computation And Machine Learning}}, urldoi = {https://catalogue.ens-cachan.fr/cgi-bin/koha/opac-detail.pl?biblionumber=57541}, annote = {Un autre gros livre. Bonne description de l'utilisation d'un HMM pour le speech recognition, Box 6.B p209. Bonne description d'un HMM (§6.2.3.1 p208). Propagation based inference (§11.3 p391).} }
@article{baumpetrie, title = {{Statistical inference for probabilistic functions of finite state Markov chains}}, author = {Baum, Leonard E and Petrie, Ted}, journal = {The Annals of Mathematical Statistics}, pages = {1554--1563}, year = {1966}, publisher = {JSTOR}, doi = {10.1214/aoms/1177699147}, urldoi = {https://dx.doi.org/10.1214/aoms/1177699147}, urldoi = {http://www.jstor.org/stable/2238772}, pdf = {http://projecteuclid.org/download/pdf_1/euclid.aoms/1177699147}, annote = {Article fondateur} }
@book{bishop06, title = {{Pattern Recognition And Machine Learning}}, author = {Bishop, Christopher M}, year = {2006}, publisher = {Springer}, annote = {Explications tres claires, chapitre entier sur les HMM (§13.1 a §13.5).} }
@book{jordan98learningpgm, title = {{Learning in Graphical Models}}, author = {Jordan, Michael I.}, volume = {89}, year = {1998}, publisher = {Springer Science \& Business Media}, note = {Proceedings of the NATO Advanced Study Institute (Ettore Mairona Center, Erice, Italy)}, url = {http://cognet.mit.edu/book/learning-graphical-models}, annote = {Livre (1/2) dont est tire le poly du cours PGM.} }
@article{jordan98graphical, title = {{Graphical Models}}, author = {Jordan, Michael I.}, journal = {Statistical Science}, publisher = {JSTOR}, number = {1}, volume = {19}, pages = {140-155}, year = {2004}, issn = {08834237}, urldoi = {http://www.jstor.org/stable/4144379}, annote = {Livre (2/2) dont est tire le poly du cours PGM.} }
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