%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{paiement:rr08-33,
         author = {Paiement, Jean-Fran{\c c}ois and Grandvalet, Yves and Bengio, Samy and Eck, Douglas},
       projects = {Idiap},
          title = {A Distance Model for Rhythms},
           type = {Idiap-RR},
         number = {Idiap-RR-33-2008},
           year = {2008},
    institution = {IDIAP},
           note = {Published in J.-F. Paiement, Y. Grandvalet, S. Bengio, and D. Eck. A Distance Model for Rhythms. The 25th International Conference on Machine Learning (ICML 2008).},
       abstract = {Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of distances between subsequences. A specific implementation of the model when considering Hamming distances over a simple rhythm representation is described. The proposed model consistently outperforms a standard Hidden Markov Model in terms of conditional prediction accuracy on two different music databases.},
            pdf = {https://publications.idiap.ch/attachments/reports/2008/paiement-idiap-rr-08-33.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2008/paiement-idiap-rr-08-33.ps.gz},
ipdmembership={learning},
}