%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{paiement:mbc:2007,
                      author = {Paiement, Jean-Fran{\c c}ois and Grandvalet, Yves and Bengio, Samy and Eck, Douglas},
                    projects = {Idiap},
                       title = {A Generative Model for Rhythms},
                   booktitle = {{NIPS} Workshop on Brain, Music and Cognition},
                        year = {2007},
                        note = {IDIAP-RR 07-70},
                    crossref = {paiement:rr07-70},
                    abstract = {Modeling music involves capturing long-term dependencies in time series, which has proved very difficult to achieve with traditional statistical methods. The same problem occurs when only considering rhythms. In this paper, we introduce a generative 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/papers/2007/paiement-mbc-2007.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/papers/2007/paiement-mbc-2007.ps.gz},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{paiement:rr07-70,
                      author = {Paiement, Jean-Fran{\c c}ois and Grandvalet, Yves and Bengio, Samy and Eck, Douglas},
                    projects = {Idiap},
                       title = {A Generative Model for Rhythms},
                        type = {Idiap-RR},
                      number = {Idiap-RR-70-2007},
                        year = {2007},
                 institution = {IDIAP},
                        note = {Published in Music, Brain, and Cognition workshop, NIPS 2007.},
                    abstract = {Modeling music involves capturing long-term dependencies in time series, which has proved very difficult to achieve with traditional statistical methods. The same problem occurs when only considering rhythms. In this paper, we introduce a generative 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/2007/paiement-idiap-rr-07-70.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2007/paiement-idiap-rr-07-70.ps.gz},
ipdmembership={learning},
}