publications.bib

@INPROCEEDINGS{leonard+sikora+defrancisco+eck:2010,
  AUTHOR = {B. Leonard and G. Sikora and M. De Francisco and D. Eck},
  TITLE = {Acoustic Space Sampling and the Grand Piano in a
		  Non-Anechoic Environment: a recordist-centric approach to
		  to musical acoustic study},
  BOOKTITLE = {129th Audio Engineering Society (AES) Convention},
  YEAR = {2010},
  ADDRESS = {London},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference}
}

@INPROCEEDINGS{courville+eck+bengio:nips2009,
  AUTHOR = {A. Courville and D. Eck and Y. Bengio},
  TITLE = {An Infinite Factor Model Hierarchy Via a Noisy-Or
		  Mechanism},
  YEAR = {2010},
  BOOKTITLE = {Neural Information Processing Systems Conference 22
		  (NIPS'09)},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  EDITOR = {},
  PUBLISHER = {},
  PDF = {},
  NOTE = {Accepted.}
}

@INPROCEEDINGS{davies+plumbley+eck:waspaa2009,
  AUTHOR = {M. Davies and M. Plumbley and D. Eck},
  TITLE = {Towards a musical beat emphasis function},
  BOOKTITLE = {Proceedings of IEEE WASPAA},
  YEAR = {2009},
  ADDRESS = {New Paltz, NY},
  ORGANIZATION = {IEEE Workshop on Applications of Signal Processing to
		  Audio and Acoustics},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference}
}

@INPROCEEDINGS{hamel+wood+eck:ismir2009,
  AUTHOR = {P. Hamel and S. Wood and D. Eck},
  TITLE = {Automatic identification of instrument classes in
		  polyphonic and poly-instrument audio},
  YEAR = {2009},
  BOOKTITLE = {{Proceedings of the 10th International Conference on Music
		  Information Retrieval ({ISMIR} 2009)}},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  EDITOR = {},
  PUBLISHER = {}
}

@INPROCEEDINGS{maillet+eck+desjardins+lamere:ismir2009,
  AUTHOR = {F. Maillet and D. Eck and G. Desjardins and P. Lamere},
  TITLE = {Steerable Playlist Generation by Learning Song Similarity
		  from Radio Station Playlists},
  YEAR = {2009},
  BOOKTITLE = {{Proceedings of the 10th International Conference on Music
		  Information Retrieval ({ISMIR} 2009)}},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  EDITOR = {},
  PUBLISHER = {}
}

@ARTICLE{paiement+bengio+eck:aij,
  AUTHOR = {{J.-F.} Paiement and S. Bengio and D. Eck},
  TITLE = {Probabilistic Models for Melodic Prediction},
  JOURNAL = {Artificial Intelligence Journal},
  YEAR = {2009},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal},
  VOLUME = {173},
  PAGES = {1266-1274}
}

@ARTICLE{bertinmahieux+eck+maillet+lamere:jnmr2008,
  AUTHOR = {T. Bertin-Mahieux and D. Eck and F. Maillet and P.
		  Lamere},
  TITLE = {Autotagger: A Model For Predicting Social Tags from
		  Acoustic Features on Large Music Databases},
  JOURNAL = {Journal of New Music Research},
  YEAR = {2008},
  VOLUME = {37},
  NUMBER = {2},
  PAGES = {115--135},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2008_jnmr.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal}
}

@TECHREPORT{eck+lapalme:2008,
  AUTHOR = {D. Eck and J. Lapalme},
  TITLE = {Learning Musical Structure Directly from Sequences of
		  Music},
  INSTITUTION = {Universit\'e de Montr\'eal DIRO},
  YEAR = {2008},
  NUMBER = {1300},
  ADDRESS = {http://www.iro.umontreal.ca/\-\~{}eckdoug/papers/tr1300.pdf},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/tr1300.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport}
}

@INPROCEEDINGS{manzagol+bertinmahieux+eck:ismir2008,
  AUTHOR = {P-A. Manzagol and T. Bertin-Mahieux and D. Eck},
  TITLE = {On the use of Sparse Time Relative Auditory Codes for
		  Music},
  YEAR = {2008},
  BOOKTITLE = {{Proceedings of the 9th International Conference on Music
		  Information Retrieval ({ISMIR} 2008)}},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  EDITOR = {},
  PUBLISHER = {},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2008_ismir.pdf}
}

@UNPUBLISHED{kegl+bertinmahieux+eck:mml2008,
  AUTHOR = {B. K{\'e}gl and T. Bertin-Mahieux and D. Eck},
  TITLE = {{Metropolis-Hastings} Sampling in a {FilterBoost} Music
		  Classifier},
  YEAR = {2008},
  NOTE = {International Workshop on Machine Learning and Music
		  (ICML/COLT/UAI 2008), Helsinki, Finland},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2008_mml.pdf}
}

@INPROCEEDINGS{paiement+grandvalet+bengio+eck:icml2008,
  AUTHOR = {{J.-F.} Paiement and Y. Grandvalet and S. Bengio and D.
		  Eck},
  TITLE = {A generative model for rhythms},
  BOOKTITLE = {ICML '08: Proceedings of the 25th International Conference
		  on Machine Learning},
  YEAR = {2008},
  PAGES = {},
  LOCATION = {Helsinki, Finland},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference}
}

@UNPUBLISHED{eck:nipsworkshop2007,
  AUTHOR = {D. Eck},
  TITLE = {Measuring and modeling musical expression},
  NOTE = {NIPS 2007 Workshop on Music, Brain and Cognition},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  OPTKEY = {},
  OPTMONTH = {},
  YEAR = {2007},
  OPTANNOTE = {}
}

@UNPUBLISHED{paiement+grandvalet+bengio+eck:nipsworkshop2007,
  AUTHOR = {{J.-F.} Paiement and Y. Grandvalet and S. Bengio and D.
		  Eck},
  TITLE = {A generative model for rhythms},
  NOTE = {NIPS 2007 Workshop on Music, Brain and Cognition},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  OPTKEY = {},
  OPTMONTH = {},
  YEAR = {2007},
  OPTANNOTE = {}
}

@UNPUBLISHED{pugin+burgoyne+eck+fujinaga:nipsworkshop2007,
  AUTHOR = {L. Pugin and J.A. Burgoyne and D. Eck and I. Fujinaga},
  TITLE = {Book-adaptive and book-dependant models to accelerate
		  digitalization of early music},
  NOTE = {NIPS 2007 Workshop on Music, Brain and Cognition},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  OPTKEY = {},
  OPTMONTH = {},
  YEAR = {2007},
  OPTANNOTE = {}
}

@INPROCEEDINGS{eck+lamere+bertinmahieux+green:nips2007,
  AUTHOR = {D. Eck and P. Lamere and T. Bertin-Mahieux and S. Green},
  TITLE = {Automatic generation of social tags for music
		  recommendation},
  YEAR = {2008},
  BOOKTITLE = {Neural Information Processing Systems Conference 20
		  (NIPS'07)},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  EDITOR = {},
  PUBLISHER = {},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2007_nips.pdf}
}

@INPROCEEDINGS{eck+bertinmahieux+lamere:ismir2007,
  AUTHOR = {D. Eck and T. Bertin-Mahieux and P. Lamere},
  TITLE = {Autotagging music using supervised machine learning},
  YEAR = {2007},
  BOOKTITLE = {{Proceedings of the 8th International Conference on Music
		  Information Retrieval ({ISMIR} 2007)}},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  EDITOR = {},
  PUBLISHER = {},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2007_ismir.pdf}
}

@INPROCEEDINGS{lamere+eck:ismir2007,
  AUTHOR = {P. Lamere and D. Eck},
  TITLE = {Using 3D Visualizations to Explore and Discover Music},
  YEAR = {2007},
  BOOKTITLE = {{Proceedings of the 8th International Conference on Music
		  Information Retrieval ({ISMIR} 2007)}},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  EDITOR = {},
  PUBLISHER = {}
}

@INCOLLECTION{jaeger+eck:2007,
  AUTHOR = {H. Jaeger and D. Eck},
  TITLE = {Can't get you out of my head: {A} connectionist model of
		  cyclic rehearsal},
  BOOKTITLE = {{Modeling Communications with Robots and Virtual Humans}},
  SERIES = {{LNCS}},
  PUBLISHER = {Springer-Verlag},
  YEAR = {2007},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Chapter},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2007_jaeger_eck.pdf}
}

@INPROCEEDINGS{eck:icassp2007,
  AUTHOR = {D. Eck},
  TITLE = {Beat Tracking Using an Autocorrelation Phase Matrix},
  YEAR = {2007},
  BOOKTITLE = {{Proceedings of the 2007 International Conference on
		  Acoustics, Speech and Signal Processing (ICASSP)}},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  EDITOR = {},
  PAGES = {1313--1316},
  PUBLISHER = {IEEE Signal Processing Society},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2007_icassp.pdf}
}

@INPROCEEDINGS{bergstra+lacoste+eck:ismir2006,
  AUTHOR = {J. Bergstra and A. Lacoste and D. Eck},
  TITLE = {Predicting genre labels for artists using FreeDB},
  BOOKTITLE = {{Proceedings of the 7th International Conference on Music
		  Information Retrieval ({ISMIR} 2006)}},
  YEAR = {2006},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  PAGES = {85-88},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2006_ismir_freedb.pdf}
}

@ARTICLE{eck:mp2006,
  AUTHOR = {D. Eck},
  TITLE = {Finding Long-Timescale Musical Structure with an
		  Autocorrelation Phase Matrix},
  YEAR = {2006},
  JOURNAL = {Music Perception},
  VOLUME = {24},
  NUMBER = {2},
  PAGES = {167--176},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2006_rppw.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal}
}

@ARTICLE{lacoste+eck:eurasip,
  AUTHOR = {A. Lacoste and D. Eck},
  TITLE = {A Supervised Classification Algorithm For Note Onset
		  Detection},
  JOURNAL = {EURASIP Journal on Applied Signal Processing},
  YEAR = {2007},
  VOLUME = {2007},
  NUMBER = {ID 43745},
  PAGES = {1--13},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2006_eurasip_draft.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal}
}

@ARTICLE{bergstra+casagrande+erhan+eck+kegl:ml,
  AUTHOR = {J. Bergstra and N. Casagrande and D. Erhan and D. Eck and
		  B. K{\'e}gl},
  TITLE = {Aggregate Features and {AdaBoost} for Music
		  Classification},
  JOURNAL = {Machine Learning},
  YEAR = {2006},
  VOLUME = {65},
  NUMBER = {2-3},
  PAGES = {473-484},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2006_ml_draft.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal}
}

@INPROCEEDINGS{eck:icmpc2006,
  AUTHOR = {D. Eck},
  TITLE = {Beat Induction Using an Autocorrelation Phase Matrix},
  YEAR = {2006},
  BOOKTITLE = {The Proceedings of the 9th International Conference on
		  Music Perception and Cognition ({ICMPC9})},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  PAGES = {931-932},
  EDITOR = {M. Baroni and A. R. Addessi and R. Caterina and M. Costa},
  PUBLISHER = {Causal Productions}
}

@INPROCEEDINGS{paiement+eck+bengio:ccai2006,
  AUTHOR = {{J.-F.} Paiement and D. Eck and S. Bengio},
  TITLE = {Probabilistic Melodic Harmonization},
  BOOKTITLE = {Canadian Conference on AI},
  YEAR = {2006},
  PAGES = {218-229},
  EDITOR = {Luc Lamontagne and Mario Marchand},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  PUBLISHER = {Springer},
  SERIES = {Lecture Notes in Computer Science},
  VOLUME = {4013}
}

@MISC{eck+scott:editor2005,
  AUTHOR = {D. Eck and S. K. Scott},
  TITLE = {Music Perception},
  YEAR = 2005,
  NOTE = {Guest Editor, Special Issue on Rhythm Perception and
		  Production, 22(3)},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Other}
}

@ARTICLE{eck+scott:2005,
  AUTHOR = {D. Eck and S. K. Scott},
  JOURNAL = {Music Perception},
  TITLE = {Editorial: New Research in Rhythm Perception and
		  Production},
  YEAR = 2005,
  VOLUME = {22},
  NUMBER = {3},
  PAGES = {371-388},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Other}
}

@INPROCEEDINGS{paiement+eck+bengio+barber:icml2005,
  AUTHOR = {{J.-F.} Paiement and D. Eck and S. Bengio and D. Barber},
  TITLE = {A graphical model for chord progressions embedded in a
		  psychoacoustic space},
  BOOKTITLE = {ICML '05: Proceedings of the 22nd international conference
		  on Machine learning},
  YEAR = {2005},
  PAGES = {641--648},
  LOCATION = {Bonn, Germany},
  PUBLISHER = {ACM Press},
  ADDRESS = {New York, NY, USA},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference}
}

@INPROCEEDINGS{casagrande+eck+kegl:icmc2005,
  AUTHOR = {N. Casagrande and D. Eck and B. Kegl},
  TITLE = {Geometry in Sound: A Speech/Music Audio Classifier
		  Inspired by an Image Classifier},
  BOOKTITLE = {{Proceedings of the International Computer Music
		  Conference (ICMC)}},
  YEAR = {2005},
  PAGES = {207--210},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2005_icmc_casagrande.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference}
}

@UNPUBLISHED{eck:rppw2005,
  AUTHOR = {D. Eck},
  TITLE = {Meter and Autocorrelation},
  NOTE = {{10th Rhythm Perception and Production Workshop (RPPW),
		  Alden Biesen, Belgium}},
  YEAR = {2005},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop}
}

@INPROCEEDINGS{paiement+eck+bengio:ismir2005,
  AUTHOR = {{J.-F.} Paiement and D. Eck and S. Bengio},
  TITLE = {A Probabilistic Model for Chord Progressions},
  BOOKTITLE = {{Proceedings of the 6th International Conference on Music
		  Information Retrieval ({ISMIR} 2005)}},
  YEAR = {2005},
  PAGES = {312-319},
  ADDRESS = {London: University of London},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference}
}

@INPROCEEDINGS{eck+casagrande:ismir2005,
  AUTHOR = {D. Eck and N. Casagrande},
  TITLE = {Finding Meter in Music Using an Autocorrelation Phase
		  Matrix and Shannon Entropy},
  BOOKTITLE = {{Proceedings of the 6th International Conference on Music
		  Information Retrieval ({ISMIR} 2005)}},
  YEAR = {2005},
  PAGES = {504--509},
  ADDRESS = {London: University of London},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2005_ismir.pdf}
}

@UNPUBLISHED{eck:nipsworkshop2006,
  AUTHOR = {D. Eck},
  TITLE = {Generating music sequences with an echo state network},
  NOTE = {NIPS 2006 Workshop on Echo State Networks and Liquid State
		  Machines},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  OPTKEY = {},
  OPTMONTH = {},
  YEAR = {2006},
  ABSTRACT = {Slides and musical examples available on request.},
  OPTANNOTE = {}
}

@INPROCEEDINGS{casagrande+eck+kegl:ismir2005,
  AUTHOR = {N. Casagrande and D. Eck and B. K\'{e}gl},
  TITLE = {Frame-Level Audio Feature Extraction using {A}da{B}oost},
  BOOKTITLE = {{Proceedings of the 6th International Conference on Music
		  Information Retrieval ({ISMIR} 2005)}},
  YEAR = {2005},
  PAGES = {345--350},
  ADDRESS = {London: University of London},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2005_ismir_casagrande.pdf}
}

@UNPUBLISHED{mirex2005genre,
  AUTHOR = {J. Bergstra and N. Casagrande and D. Eck},
  TITLE = {Genre Classification: Timbre- and Rhythm-Based
		  Multiresolution Audio Classification},
  NOTE = {{MIREX} genre classification contest},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Other},
  ADDRESS = {{ISMIR} Conference, London},
  YEAR = {2005}
}

@UNPUBLISHED{mirex2005artist,
  AUTHOR = {J. Bergstra and N. Casagrande and D. Eck},
  TITLE = {Artist Recognition: A Timbre- and Rhythm-Based
		  Multiresolution Approach},
  NOTE = {{MIREX} artist recognition contest},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Other},
  ADDRESS = {{ISMIR} Conference, London},
  YEAR = {2005}
}

@UNPUBLISHED{mirex2005note,
  AUTHOR = {A. Lacoste and D. Eck},
  TITLE = {Onset Detection with Artificial Neural Networks},
  NOTE = {{MIREX} note onset detection contest},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Other},
  ADDRESS = {{ISMIR} Conference, London},
  YEAR = {2005}
}

@UNPUBLISHED{mirex2005tempo,
  AUTHOR = {D. Eck and N. Casagrande},
  TITLE = {A Tempo-Extraction Algorithm Using an Autocorrelation
		  Phase Matrix and Shannon Entropy},
  NOTE = {{MIREX} tempo extraction contest
		  (www.music-ir.org/\-evaluation/\-mirex-results)},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Other},
  ADDRESS = {{ISMIR} Conference, London},
  YEAR = {2005}
}

@UNPUBLISHED{eck:mipsworkshop2004,
  AUTHOR = {D. Eck},
  TITLE = {Bridging Long Timelags in Music},
  NOTE = {NIPS 2004 Workshop on Music and Machine Learning (MIPS),
		  Whistler, British Columbia},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  OPTKEY = {},
  OPTMONTH = {},
  YEAR = {2004},
  ABSTRACT = {Slides and musical examples available on request.},
  OPTANNOTE = {}
}

@UNPUBLISHED{eck:bramsworkshop2004,
  AUTHOR = {D. Eck},
  TITLE = {Challenges for Machine Learning in the Domain of Music},
  NOTE = {BRAMS Workshop on Brain and Music, Montreal Neurological
		  Institute},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  OPTKEY = {},
  OPTMONTH = {},
  YEAR = {2004},
  ABSTRACT = {Slides and musical examples available on request.},
  OPTANNOTE = {}
}

@INPROCEEDINGS{eck:icmpc2004,
  AUTHOR = {D. Eck},
  TITLE = {A Machine-Learning Approach to Musical Sequence Induction
		  That Uses Autocorrelation to Bridge Long Timelags},
  YEAR = {2004},
  ADDRESS = {Adelaide},
  BOOKTITLE = {{The Proceedings of the Eighth International Conference on
		  Music Perception and Cognition ({ICMPC}8)}},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  EDITOR = {S.D. Lipscomb and R. Ashley and R.O. Gjerdingen and P.
		  Webster},
  PUBLISHER = {Causal Productions},
  PAGES = {542-543},
  ABSTRACT = { One major challenge in using statistical sequence
		  learning methods in the domain of music lies in bridging
		  the long timelags that separate important musical events.
		  Consider, for example, the chord changes that convey the
		  basic structure of a pop song. A sequence learner that
		  cannot predict chord changes will almost certainly not be
		  able to generate new examples in a musical style or to
		  categorize songs by style. Yet, it is surprisingly
		  difficult for a sequence learner to bridge the long
		  timelags necessary to identify when a chord change will
		  occur and what its new value will be. This is the case
		  because chord changes can be separated by dozens or
		  hundreds of intervening notes. One could solve this problem
		  by treating chords as being special (as did Mozer, NIPS
		  1991). But this is impractical---it requires chords to be
		  labeled specially in the dataset, limiting the
		  applicability of the model to non-labeled examples---and
		  furthermore does not address the general issue of nested
		  temporal structure in music. I will briefly describe this
		  temporal structure (known commonly as "meter") and present
		  a model that uses to its advantage an assumption that
		  sequences are metrical. The model consists of an
		  autocorrelation-based filtration that estimates online the
		  most likely metrical tree (i.e. the frequency and phase of
		  beat, measure, phrase &etc.) and uses that to generate a
		  series of sequences varying at different rates. These
		  sequences correspond to each level in the hierarchy.
		  Multiple learners can be used to treat each series
		  separately and their predictions can be combined to perform
		  composition and categorization. I will present preliminary
		  results that demonstrate the usefulness of this approach.
		  Time permitting I will also compare the model to alternate
		  approaches. }
}

@INPROCEEDINGS{graves+eck+schmidhuber:bio-adit2004,
  AUTHOR = {A. Graves and D. Eck and N. Beringer and J. Schmidhuber},
  TITLE = {Biologically Plausible Speech Recognition with {LSTM}
		  Neural Nets},
  YEAR = {2004},
  BOOKTITLE = {Proceedings of the First Int'l Workshop on Biologically
		  Inspired Approaches to Advanced Information Technology
		  (Bio-ADIT)},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2004_bioadit.pdf},
  PAGES = {127-136},
  ABSTRACT = { Long Short-Term Memory (LSTM) recurrent neural networks
		  (RNNs) are local in space and time and closely related to a
		  biological model of memory in the prefrontal cortex. Not
		  only are they more biologically plausible than previous
		  artificial RNNs, they also outperformed them on many
		  artificially generated sequential processing tasks. This
		  encouraged us to apply LSTM to more realistic problems,
		  such as the recognition of spoken digits. Without any
		  modification of the underlying algorithm, we achieved
		  results comparable to state-of-the-art Hidden Markov Model
		  (HMM) based recognisers on both the TIDIGITS and TI46
		  speech corpora. We conclude that LSTM should be further
		  investigated as a biologically plausible basis for a
		  bottom-up, neural net-based approach to speech recognition.
		  }
}

@ARTICLE{perez+gers+schmidhuber+eck:2003,
  AUTHOR = {J.A. P\'{e}rez-Ortiz and F. A. Gers and D. Eck and J.
		  Schmidhuber},
  TITLE = {{K}alman filters improve {LSTM} network performance in
		  problems unsolvable by traditional recurrent nets},
  JOURNAL = {Neural Networks},
  NUMBER = 2,
  VOLUME = 16,
  PAGES = {241--250},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2003_nn.pdf},
  YEAR = {2003},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal},
  ABSTRACT = {The Long Short-Term Memory (LSTM) network trained by
		  gradient descent solves difficult problems which
		  traditional recurrent neural networks in general cannot. We
		  have recently observed that the decoupled extended Kalman
		  filter training algorithm allows for even better
		  performance, reducing significantly the number of training
		  steps when compared to the original gradient descent
		  training algorithm. In this paper we present a set of
		  experiments which are unsolvable by classical recurrent
		  networks but which are solved elegantly and robustly and
		  quickly by LSTM combined with Kalman filters.}
}

@UNPUBLISHED{eck:nipsworkshop2003,
  AUTHOR = {D. Eck},
  TITLE = {Time-warped hierarchical structure in music and speech: A
		  sequence prediction challenge},
  NOTE = {NIPS 2003 Workshop on Recurrent Neural Networks, Whistler,
		  British Columbia},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  OPTKEY = {},
  OPTMONTH = {},
  YEAR = {2003},
  ABSTRACT = {Slides and musical examples available on request.},
  OPTANNOTE = {}
}

@UNPUBLISHED{eck:irisworkshop2004,
  AUTHOR = {D. Eck},
  TITLE = {Using Autocorrelation to Bridge Long Timelags when
		  Learning Sequences of Music},
  NOTE = {IRIS 2004 Machine Learning Workshop, Ottawa, Canada},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  OPTKEY = {},
  OPTMONTH = {},
  YEAR = {2004},
  ABSTRACT = {Slides and musical examples available on request.},
  OPTANNOTE = {}
}

@TECHREPORT{eck+graves+schmidhuber:tr-speech2003,
  AUTHOR = {D. Eck and A. Graves and J. Schmidhuber},
  TITLE = {A New Approach to Continuous Speech Recognition Using
		  {LSTM} Recurrent Neural Networks},
  INSTITUTION = {IDSIA},
  YEAR = {2003},
  NUMBER = {IDSIA-14-03},
  ADDRESS = {www.idsia.ch/\-techrep.html},
  MONTH = {May},
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport},
  ABSTRACT = { This paper presents an algorithm for continuous speech
		  recognition built from two Long Short-Term Memory (LSTM)
		  recurrent neural networks. A first LSTM network performs
		  frame-level phone probability estimation. A second network
		  maps these phone predictions onto words. In contrast to
		  HMMs, this allows greater exploitation of long-timescale
		  correlations. Simulation results are presented for a
		  hand-segmented subset of the "Numbers-95" database. These
		  results include isolated phone prediction, continuous
		  frame-level phone prediction and continuous word
		  prediction. We conclude that despite its early stage of
		  development, our new model is already competitive with
		  existing approaches on certain aspects of speech
		  recognition and promising on others, warranting further
		  research. }
}

@TECHREPORT{graves+eck+schmidhuber:tr-digits2003,
  AUTHOR = {A. Graves and D. Eck and J. Schmidhuber},
  TITLE = {Comparing {LSTM} Recurrent Networks and Spiking Recurrent
		  Networks on the Recognition of Spoken Digits},
  INSTITUTION = {IDSIA},
  YEAR = {2003},
  NUMBER = {IDSIA-13-03},
  ADDRESS = {www.idsia.ch/\-techrep.html},
  MONTH = {May},
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport},
  ABSTRACT = { One advantage of spiking recurrent neural networks (SNNs)
		  is an ability to categorise data using a synchrony-based
		  latching mechnanism. This is particularly useful in
		  problems where timewarping is encountered, such as speech
		  recognition. Differentiable recurrent neural networks
		  (RNNs) by contrast fail at tasks involving difficult
		  timewarping, despite having sequence learning capabilities
		  superior to SNNs. In this paper we demonstrate that Long
		  Short-Term Memory (LSTM) is an RNN capable of robustly
		  categorizing timewarped speech data, thus combining the
		  most useful features of both paradigms. We compare its
		  performance to SNNs on two variants of a spoken digit
		  identification task, using data from an international
		  competition. The first task (described in Nature (Nadis
		  2003)) required the categorisation of spoken digits with
		  only a single training exemplar, and was specifically
		  designed to test robustness to timewarping. Here LSTM
		  performed better than all the SNNs in the competition. The
		  second task was to predict spoken digits using a larger
		  training set. Here LSTM greatly outperformed an SNN-like
		  model found in the literature. These results suggest that
		  LSTM has a place in domains that require the learning of
		  large timewarped datasets, such as automatic speech
		  recognition. }
}

@ARTICLE{eck:psyres2002,
  AUTHOR = {D. Eck},
  TITLE = {Finding Downbeats with a Relaxation Oscillator},
  JOURNAL = {Psychol. Research},
  YEAR = {2002},
  VOLUME = {66},
  NUMBER = {1},
  PAGES = {18--25},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2002_psyres.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal},
  ABSTRACT = {A relaxation oscillator model of neural spiking dynamics
		  is applied to the task of finding downbeats in rhythmical
		  patterns. The importance of downbeat discovery or {\em beat
		  induction} is discussed, and the relaxation oscillator
		  model is compared to other oscillator models. In a set of
		  computer simulations the model is tested on 35 rhythmical
		  patterns from Povel \& Essens (1985). The model performs
		  well, making good predictions in 34 of 35 cases. In an
		  analysis we identify some shortcomings of the model and
		  relate model behavior to dynamical properties of relaxation
		  oscillators. }
}

@ARTICLE{schmidhuber+gers+eck:2002,
  AUTHOR = {J. Schmidhuber and F.A. Gers and D. Eck},
  TITLE = {Learning Nonregular Languages: A Comparison of Simple
		  Recurrent Networks and {LSTM}},
  JOURNAL = {Neural Computation},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2002_nc.pdf},
  YEAR = {2002},
  VOLUME = {14},
  NUMBER = {9},
  PAGES = {2039--2041},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal},
  ABSTRACT = {In response to Rodriguez' recent article (Rodriguez 2001)
		  we compare the performance of simple recurrent nets and
		  {\em ``Long Short-Term Memory''} (LSTM) recurrent nets on
		  context-free and context-sensitive languages.}
}

@INPROCEEDINGS{eck+schmidhuber:ieee2002,
  AUTHOR = {D. Eck and J. Schmidhuber},
  TITLE = {Finding Temporal Structure in Music: Blues Improvisation
		  with {LSTM} Recurrent Networks},
  BOOKTITLE = {Neural Networks for Signal Processing XII, Proceedings of
		  the 2002 IEEE Workshop},
  EDITOR = {H. Bourlard},
  PAGES = {747--756},
  PUBLISHER = {IEEE},
  ADDRESS = {New York},
  YEAR = 2002,
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2002_ieee.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = { Few types of signal streams are as ubiquitous as music.
		  Here we consider the problem of extracting essential
		  ingredients of music signals, such as well-defined global
		  temporal structure in the form of nested periodicities (or
		  {\em meter}). Can we construct an adaptive signal
		  processing device that learns by example how to generate
		  new instances of a given musical style? Because recurrent
		  neural networks can in principle learn the temporal
		  structure of a signal, they are good candidates for such a
		  task. Unfortunately, music composed by standard recurrent
		  neural networks (RNNs) often lacks global coherence. The
		  reason for this failure seems to be that RNNs cannot keep
		  track of temporally distant events that indicate global
		  music structure. Long Short-Term Memory (LSTM) has
		  succeeded in similar domains where other RNNs have failed,
		  such as timing \& counting and learning of context
		  sensitive languages. In the current study we show that LSTM
		  is also a good mechanism for learning to compose music. We
		  present experimental results showing that LSTM successfully
		  learns a form of blues music and is able to compose novel
		  (and we believe pleasing) melodies in that style.
		  Remarkably, once the network has found the relevant
		  structure it does not drift from it: LSTM is able to play
		  the blues with good timing and proper structure as long as
		  one is willing to listen. }
}

@UNPUBLISHED{eck:verita2002,
  AUTHOR = {D. Eck},
  TITLE = {Real Time Beat Induction with Spiking Neurons},
  NOTE = {{Music, Motor Control and the Mind: Symposium at Monte
		  Verita, May}},
  YEAR = {2002},
  ADDRESS = {},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Workshop},
  ABSTRACT = { Beat induction is best described by analogy to the
		  activites of hand clapping or foot tapping, and involves
		  finding important metrical components in an auditory
		  signal, usually music. Though beat induction is intuitively
		  easy to understand it is difficult to define and still more
		  difficult to model. I will discuss an approach to beat
		  induction that uses a network of spiking neurons to
		  synchronize with periodic components in a signal at many
		  timescales. Through a competitive process, groups of
		  oscillators embodying a particular metrical interpretation
		  (e.g. \"4/4\") are selected from the network and used to
		  track the pattern. I will compare this model to other
		  approaches including a traditional symbolic AI system
		  (Dixon 2001), and one based on Bayesian statistics (Cemgil
		  et al, 2001). Finally I will present performance results of
		  the network on a set of MIDI-recorded piano performances of
		  Beatles songs collected by the Music, Mind, Machine Group,
		  NICI, University of Nijmegen (see Cemgil et al, 2001 for
		  more details or http://www.nici.kun.nl/mmm). }
}

@INPROCEEDINGS{eck+schmidhuber:icann2002,
  AUTHOR = {D. Eck and J. Schmidhuber},
  TITLE = {Learning The Long-Term Structure of the Blues},
  BOOKTITLE = {{Artificial Neural Networks -- ICANN 2002 (Proceedings)}},
  EDITOR = {J. Dorronsoro},
  VOLUME = {},
  PAGES = {284--289},
  PUBLISHER = {Springer},
  ADDRESS = {Berlin},
  YEAR = 2002,
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2002_icannMusic.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = { In general music composed by recurrent neural networks
		  (RNNs) suffers from a lack of global structure. Though
		  networks can learn note-by-note transition probabilities
		  and even reproduce phrases, they have been unable to learn
		  an entire musical form and use that knowledge to guide
		  composition. In this study, we describe model details and
		  present experimental results showing that LSTM successfully
		  learns a form of blues music and is able to compose novel
		  (and some listeners believe pleasing) melodies in that
		  style. Remarkably, once the network has found the relevant
		  structure it does not drift from it: LSTM is able to play
		  the blues with good timing and proper structure as long as
		  one is willing to listen. }
}

@INPROCEEDINGS{gers+perez+eck+schmidhuber:esann2002,
  AUTHOR = {F.A. Gers and J.A. Perez-Ortiz and D. Eck and J.
		  Schmidhuber},
  TITLE = {{DEKF-LSTM}},
  BOOKTITLE = {Proceedings of the 10th European Symposium on Artificial
		  Neural Networks, ESANN 2002},
  ADDRESS = {},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  YEAR = {2002}
}

@INPROCEEDINGS{gers+perez+eck+schmidhuber:icanna2002,
  AUTHOR = {F.A. Gers and J.A. P\'{e}rez-Ortiz and D. Eck and J.
		  Schmidhuber},
  TITLE = {Learning Context Sensitive Languages with {LSTM} Trained
		  with {Kalman} Filters},
  BOOKTITLE = {{Artificial Neural Networks -- ICANN 2002 (Proceedings)}},
  EDITOR = {J. Dorronsoro},
  PAGES = {655--660},
  PUBLISHER = {Springer},
  ADDRESS = {Berlin},
  YEAR = 2002,
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = { Unlike traditional recurrent neural networks, the Long
		  Short-Term Memory (LSTM) model generalizes well when
		  presented with training sequences derived from regular and
		  also simple nonregular languages. Our novel combination of
		  LSTM and the decoupled extended Kalman filter, however,
		  learns even faster and generalizes even better, requiring
		  only the 10 shortest exemplars n <= 10 of the context
		  sensitive language a^nb^nc^n to deal correctly with values
		  of n up to 1000 and more. Even when we consider the
		  relatively high update complexity per timestep, in many
		  cases the hybrid offers faster learning than LSTM by
		  itself. }
}

@INPROCEEDINGS{perez+schmidhuber+gers+eck:icannb2002,
  AUTHOR = {J.A. P\'{e}rez-Ortiz and J. Schmidhuber and F.A. Gers and
		  D. Eck},
  TITLE = {Improving Long-Term Online Prediction with {Decoupled
		  Extended Kalman Filters}},
  BOOKTITLE = {{Artificial Neural Networks -- ICANN 2002 (Proceedings)}},
  EDITOR = {J. Dorronsoro},
  PAGES = {1055--1060},
  PUBLISHER = {Springer},
  ADDRESS = {Berlin},
  YEAR = 2002,
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = { Long Short-Term Memory (LSTM) recurrent neural networks
		  (RNNs) outperform traditional RNNs when dealing with
		  sequences involving not only short-term but also long-term
		  dependencies. The decoupled extended Kalman filter learning
		  algorithm (DEKF) works well in online environments and
		  reduces significantly the number of training steps when
		  compared to the standard gradient-descent algorithms.
		  Previous work on LSTM, however, has always used a form of
		  gradient descent and has not focused on true online
		  situations. Here we combine LSTM with DEKF and show that
		  this new hybrid improves upon the original learning
		  algorithm when applied to online processing. }
}

@TECHREPORT{eck:tr-tracking2002,
  AUTHOR = {D. Eck},
  TITLE = {Real-Time Musical Beat Induction with Spiking Neural
		  Networks},
  INSTITUTION = {IDSIA},
  YEAR = {2002},
  NUMBER = {IDSIA-22-02},
  ADDRESS = {www.idsia.ch/\-techrep.html},
  MONTH = {October},
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport},
  ABSTRACT = { Beat induction is best described by analogy to the
		  activities of hand clapping or foot tapping, and involves
		  finding important metrical components in an auditory
		  signal, usually music. Though beat induction is intuitively
		  easy to understand it is difficult to define and still more
		  difficult to perform automatically. We will present a model
		  of beat induction that uses a spiking neural network as the
		  underlying synchronization mechanism. This approach has
		  some advantages over existing methods; it runs online,
		  responds at many levels in the metrical hierarchy, and
		  produces good results on performed music (Beatles piano
		  performances encoded as MIDI). In this paper the model is
		  described in some detail and simulation results are
		  discussed. }
}

@TECHREPORT{eck:tr-music2002,
  AUTHOR = {D. Eck and J. Schmidhuber},
  TITLE = {A First Look at Music Composition using {LSTM} Recurrent
		  Neural Networks},
  INSTITUTION = {IDSIA},
  YEAR = {2002},
  NUMBER = {IDSIA-07-02},
  ADDRESS = {www.idsia.ch/\-techrep.html},
  MONTH = {March},
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport},
  ABSTRACT = { In general music composed by recurrent neural networks
		  (RNNs) suffers from a lack of global structure. Though
		  networks can learn note-by-note transition probabilities
		  and even reproduce phrases, attempts at learning an entire
		  musical form and using that knowledge to guide composition
		  have been unsuccessful. The reason for this failure seems
		  to be that RNNs cannot keep track of temporally distant
		  events that indicate global music structure. Long
		  Short-Term Memory (LSTM) has succeeded in similar domains
		  where other RNNs have failed, such as timing \& counting
		  and CSL learning. In the current study I show that LSTM is
		  also a good mechanism for learning to compose music. I
		  compare this approach to previous attempts, with particular
		  focus on issues of data representation. I present
		  experimental results showing that LSTM successfully learns
		  a form of blues music and is able to compose novel (and I
		  believe pleasing) melodies in that style. Remarkably, once
		  the network has found the relevant structure it does not
		  drift from it: LSTM is able to play the blues with good
		  timing and proper structure as long as one is willing to
		  listen.
		  
		  {\em Note: This is a more complete version of the 2002
		  ICANN submission Learning the Long-Term Structure of the
		  Blues.} }
}

@ARTICLE{eck:jnmr2001,
  AUTHOR = {D. Eck},
  TITLE = {A Positive-Evidence Model for Rhythmical Beat Induction},
  JOURNAL = {Journal of New Music Research},
  YEAR = {2001},
  VOLUME = {30},
  NUMBER = {2},
  PAGES = {187--200},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2001_jnmr.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal},
  ABSTRACT = {The Normalized Positive (NPOS) model is a rule-based model
		  that predicts downbeat location and pattern complexity in
		  rhythmical patterns. Though derived from several existing
		  models, the NPOS model is particularly effective at making
		  correct predictions while at the same time having low
		  complexity. In this paper, the details of the model are
		  explored and a comparison is made to existing models.
		  Several datasets are used to examine the complexity
		  predictions of the model. Special attention is paid to the
		  model's ability to account for the effects of musical
		  experience on beat induction.}
}

@INPROCEEDINGS{eck:icann2001,
  AUTHOR = {D. Eck},
  TITLE = {A Network of Relaxation Oscillators that Finds Downbeats
		  in Rhythms},
  BOOKTITLE = {{Artificial Neural Networks -- ICANN 2001 (Proceedings)}},
  EDITOR = {Georg Dorffner},
  VOLUME = {},
  PAGES = {1239--1247},
  PUBLISHER = {Springer},
  ADDRESS = {Berlin},
  YEAR = 2001,
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2001_icann.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = {A network of relaxation oscillators is used to find
		  downbeats in rhythmical patterns. In this study, a novel
		  model is described in detail. Its behavior is tested by
		  exposing it to patterns having various levels of rhythmic
		  complexity. We analyze the performance of the model and
		  relate its success to previous work dealing with fast
		  synchrony in coupled oscillators. }
}

@INPROCEEDINGS{gers+eck+schmidhuber:icann2001,
  AUTHOR = {F. A. Gers and D. Eck and J. Schmidhuber},
  TITLE = {Applying {LSTM} to Time Series Predictable Through
		  Time-Window Approaches},
  BOOKTITLE = {{Artificial Neural Networks -- ICANN 2001 (Proceedings)}},
  EDITOR = {Georg Dorffner},
  PAGES = {669--676},
  PUBLISHER = {Springer},
  ADDRESS = {Berlin},
  YEAR = 2001,
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2001_gers_icann.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = {Long Short-Term Memory (LSTM) is able to solve many time
		  series tasks unsolvable by feed-forward networks using
		  fixed size time windows. Here we find that LSTM's
		  superiority does {\em not} carry over to certain simpler
		  time series tasks solvable by time window approaches: the
		  Mackey-Glass series and the Santa Fe FIR laser emission
		  series (Set A). This suggests t use LSTM only when simpler
		  traditional approaches fail.}
}

@TECHREPORT{eck:tr-oscnet2001,
  AUTHOR = {D. Eck},
  TITLE = {A Network of Relaxation Oscillators that Finds Downbeats
		  in Rhythms},
  INSTITUTION = {IDSIA},
  YEAR = {2001},
  NUMBER = {IDSIA-06-01},
  ADDRESS = {www.idsia.ch/\-techrep.html},
  MONTH = {February},
  PS = {ftp://ftp.idsia.ch/pub/techrep/IDSIA-06-01.ps.gz},
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport},
  ABSTRACT = {A network of relaxation oscillators is used to find
		  downbeats in rhythmical patterns. In this study, a novel
		  model is described in detail. Its behavior is tested by
		  exposing it to patterns having various levels of rhythmic
		  complexity. We analyze the performance of the model and
		  relate its success to previous work dealing with fast
		  synchrony in coupled oscillators. \\
		  
		  {\em Note: See the 2001 ICANN conference proceeding by the
		  same title for a newer version of this paper.}}
}

@INCOLLECTION{eck+gasser+port:2000,
  AUTHOR = {D. Eck and M. Gasser and Robert Port},
  TITLE = {Dynamics and Embodiment in Beat Induction},
  BOOKTITLE = {{Rhythm Perception and Production}},
  EDITOR = {P. Desain and L. Windsor},
  PUBLISHER = {Swets and Zeitlinger},
  ADDRESS = {Lisse, The Netherlands},
  YEAR = 2000,
  PAGES = {157--170},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/2000_rppw.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Chapter},
  ABSTRACT = {We provide an argument for using dynamical systems theory
		  in the domain of beat induction. We motivate the study of
		  beat induction and to relate beat induction to the more
		  general study of human rhythm cognition. In doing so we
		  compare a dynamical, embodied approach to a symbolic
		  (traditional AI) one, paying particular attention to how
		  the modeling approach brings with it tacit assumptions
		  about what is being modeled. Please note that this is a
		  philosophy paper about research that was, at the time of
		  writing, very much in progress. }
}

@TECHREPORT{gers+eck+schmidhuber:tr-2000,
  AUTHOR = {F. A. Gers and D. Eck and J. Schmidhuber},
  TITLE = {Applying {LSTM} to Time Series Predictable Through
		  Time-Window Approaches},
  INSTITUTION = {IDSIA},
  YEAR = {2000},
  NUMBER = {IDSIA-22-00},
  ADDRESS = {www.idsia.ch/\-techrep.html},
  MONTH = {December},
  PS = {ftp://ftp.idsia.ch/pub/techrep/IDSIA-22-00.ps.gz},
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport},
  ABSTRACT = {Long Short-Term Memory (LSTM) is able to solve many time
		  series tasks unsolvable by feed-forward networks using
		  fixed size time windows. Here we find that LSTM's
		  superiority does {\em not} carry over to certain simpler
		  time series tasks solvable by time window approaches: the
		  Mackey-Glass series and the Santa Fe FIR laser emission
		  series (Set A). This suggests t use LSTM only when simpler
		  traditional approaches fail.\\
		  
		  {\em Note: See the 2001 ICANN conference proceeding by the
		  same title for a newer version of this paper.}}
}

@TECHREPORT{eck:tr-tracking2000,
  AUTHOR = {D. Eck},
  TITLE = {Tracking Rhythms with a Relaxation Oscillator},
  INSTITUTION = {IDSIA},
  YEAR = {2000},
  NUMBER = {IDSIA-10-00},
  ADDRESS = {www.idsia.ch/\-techrep.html},
  MONTH = {October},
  PS = {ftp://ftp.idsia.ch/pub/techrep/IDSIA-10-00.ps.gz},
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport},
  ABSTRACT = { A number of biological and mechanical processes are
		  typified by a continued slow accrual and fast release of
		  energy. A nonlinear oscillator exhibiting this slow-fast
		  behavior is called a relaxation oscillator and is used to
		  model, for example, human heartbeat pacemaking and neural
		  action potential. Similar limit cycle oscillators are used
		  to model a wider range of behaviors including predator-prey
		  relationships and synchrony in animal populations such as
		  fireflies. Though nonlinear limit-cycle oscillators have
		  been successfully applied to beat induction, relaxation
		  oscillators have received less attention. In this work we
		  offer a novel and effective relaxation oscillator model of
		  beat induction. We outline the model in detail and provide
		  a perturbation analysis of its response to external
		  stimuli. In a series of simulations we expose the model to
		  patterns from Experiment 1 of Povel \& Essens (1985). We
		  then examine the beat assignments of the model. Although
		  the overall performance of the model is very good, there
		  are shortcomings. We believe that a network of
		  mutually-coupled oscillators will address many of these
		  shortcomings, and we suggest an appropriate course for
		  future research.\\
		  
		  {\em Note: See the 2001 {\em Psychological Research}
		  article "Finding Downbeats with a Relaxation Oscillator"
		  for a revised but less detailed version of this paper.}}
}

@TECHREPORT{eck:tr-npos2000,
  AUTHOR = {D. Eck},
  TITLE = {A Positive-Evidence Model for Classifying Rhythmical
		  Patterns},
  INSTITUTION = {IDSIA},
  YEAR = {2000},
  NUMBER = {IDSIA-09-00},
  ADDRESS = {www.idsia.ch/\-techrep.html},
  MONTH = {October},
  PS = {ftp://ftp.idsia.ch/pub/techrep/IDSIA-09-00.ps.gz},
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport},
  ABSTRACT = {The Normalized Positive (NPOS) model is a novel matching
		  model that predicts downbeat location and pattern
		  complexity in rhythmical patterns. Though similar models
		  report success, the NPOS model is particularly effective at
		  making these predictions while at the same time being
		  theoretically and mathematically simple. In this paper, the
		  details of the model are explored and a comparison is made
		  to existing models. Several datasets are used to examine
		  the complexity predictions of the model. Special attention
		  is paid to the model's ability to account for the effects
		  of musical experience on rhythm perception.\\
		  
		  {\em Note: See the 2001 Journal of New Music Research paper
		  "A Positive-Evidence Model for Rhythmical Beat Induction"
		  for a newer version of this paper.}}
}

@PHDTHESIS{eck:diss,
  AUTHOR = {D. Eck},
  TITLE = {{Meter Through Synchrony: Processing Rhythmical Patterns
		  with Relaxation Oscillators}},
  SCHOOL = {Indiana University, Bloomington, IN,
		  www.idsia.ch/\-\~{}doug/\-publications.html },
  YEAR = {2000},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/thesis.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Thesis},
  ABSTRACT = { This dissertation uses a network of relaxation
		  oscillators to beat along with temporal signals. Relaxation
		  oscillators exhibit interspersed slow-fast movement and
		  model a wide array of biological oscillations. The model is
		  built up gradually: first a single relaxation oscillator is
		  exposed to rhythms and shown to be good at finding
		  downbeats in them. Then large networks of oscillators are
		  mutually coupled in an exploration of their internal
		  synchronization behavior. It is demonstrated that
		  appropriate weights on coupling connections cause a network
		  to form multiple pools of oscillators having stable phase
		  relationships. This is a promising first step towards
		  networks that can recreate a rhythmical pattern from
		  memory. In the full model, a coupled network of relaxation
		  oscillators is exposed to rhythmical patterns. It is shown
		  that the network finds downbeats in patterns while
		  continuing to exhibit good internal stability. A novel
		  non-dynamical model of downbeat induction called the
		  Normalized Positive (NP) clock model is proposed, analyzed,
		  and used to generate comparison predictions for the
		  oscillator model. The oscillator model compares favorably
		  to other dynamical approaches to beat induction such as
		  adaptive oscillators. However, the relaxation oscillator
		  model takes advantage of intrinsic synchronization
		  stability to allow the creation of large coupled networks.
		  This research lays the groundwork for a long-term research
		  goal, a robotic arm that responds to rhythmical signals by
		  tapping along. It also opens the door to future work in
		  connectionist learning of long rhythmical patterns.}
}

@ARTICLE{gasser+eck+port:1999,
  AUTHOR = {M. Gasser and D. Eck and R. Port},
  TITLE = {Meter as Mechanism: A Neural Network Model that Learns
		  Metrical patterns},
  YEAR = {1999},
  JOURNAL = {Connection Science},
  VOLUME = {11},
  NUMBER = {2},
  PAGES = {187--216},
  OWN = {Have},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/1999_gasser.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Journal},
  ABSTRACT = {One kind of prosodic structure that apparently underlies
		  both music and some examples of speech production is meter.
		  Yet detailed measurements of the timing of both music and
		  speech show that the nested periodicities that define
		  metrical structure can be quite noisy in time. What kind of
		  system could produce or perceive such variable metrical
		  timing patterns? And what would it take to be able to store
		  and reproduce particular metrical patterns from long-term
		  memory? We have developed a network of coupled oscillators
		  that both produces and perceives patterns of pulses that
		  conform to particular meters. In addition, beginning with
		  an initial state with no biases, it can learn to prefer the
		  particular meter that it has been previously exposed to.}
}

@INPROCEEDINGS{eck:1999,
  AUTHOR = {D. Eck},
  TITLE = {Learning Simple Metrical Preferences in a Network of
		  {F}itzhugh-{N}agumo Oscillators},
  BOOKTITLE = {{The Proceedings of the Twenty-First Annual Conference of
		  the Cognitive Science Society}},
  PUBLISHER = {Lawrence Erlbaum Associates},
  YEAR = {1999},
  EDITOR = {},
  ADDRESS = {New Jersey},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = {Hebbian learning is used to train a network of oscillators
		  to prefer periodic signals of pulses over aperiodic
		  signals. Target signals consisted of metronome-like voltage
		  pulses with varying amounts of inter-onset noise injected.
		  (with 0\% noise yielding a periodic signal and more noise
		  yielding more and more aperiodic signals.) The
		  oscillators---piecewise-linear approximations (Abbott,
		  1990) to Fitzhugh-Nagumo oscillators---are trained using
		  mean phase coherence as an objective function. Before
		  training a network is shown to readily synchronize with
		  signals having wide range of noise. After training on a
		  series of noise-free signals, a network is shown to only
		  synchronize with signals having little or no noise. This
		  represents a bias towards periodicity and is explained by
		  strong positive coupling connections between oscillators
		  having harmonically-related periods. }
}

@INPROCEEDINGS{chemero+eck:1999,
  AUTHOR = {T. Chemero and D. Eck},
  TITLE = {An Exploration of Representational Complexity via Coupled
		  Oscillators},
  BOOKTITLE = {{Proceedings of the Tenth Midwest Artificial Intelligence
		  and Cognitive Science Society}},
  YEAR = {1999},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/1999_chemero.pdf},
  PUBLISHER = {MIT Press},
  ADDRESS = {Cambridge, Mass.},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = {We note some inconsistencies in a view of representation
		  which takes {\it decoupling} to be of key importance. We
		  explore these inconsistencies using examples of
		  representational vehicles taken from coupled oscillator
		  theory and suggest a new way to reconcile {\it coupling}
		  with {\it absence}. Finally, we tie these views to a
		  teleological definition of representation.}
}

@INPROCEEDINGS{eck+gasser:1996,
  AUTHOR = {D. Eck and M. Gasser},
  TITLE = {Perception of Simple Rhythmic Patterns in a Network of
		  Oscillators},
  BOOKTITLE = {{The Proceedings of the Eighteenth Annual Conference of
		  the Cognitive Science Society}},
  PUBLISHER = {Lawrence Erlbaum Associates},
  YEAR = {1996},
  EDITOR = {},
  ADDRESS = {New Jersey},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = {This paper is concerned with the complex capacity to
		  recognize and reproduce rhythmic patterns. While this
		  capacity has not been well investigated, in broad
		  qualitative terms it is clear that people can learn to
		  identify and produce recurring patterns defined in terms of
		  sequences of beats of varying intensity and rests: the
		  rhythms behind waltzes, reels, sambas, etc. Our short term
		  goal is a model which is "hard-wired" with knowledge of a
		  set of such patterns. Presented with a portion of one of
		  the patterns or a label for a pattern, the model should
		  reproduce the pattern and continue to do so when the input
		  is turned off. Our long-term goal is a model which can
		  learn to adjust the connection strengths which implement
		  particular patterns as it is exposed to input patterns.}
}

@INPROCEEDINGS{gasser+eck:1996,
  AUTHOR = {M. Gasser and D. Eck},
  TITLE = {Representing Rhythmic Patterns in a Network of
		  Oscillators},
  BOOKTITLE = {{The Proceedings of the International Conference on Music
		  Perception and Cognition}},
  PUBLISHER = {Lawrence Erlbaum Associates},
  ADDRESS = {New Jersey},
  YEAR = {1996},
  EDITOR = {},
  NUMBER = {4},
  PAGES = {361--366},
  PDF = {http://www.iro.umontreal.ca/~eckdoug/papers/1996_gasser_icmpc.pdf},
  SOURCE = {OwnPublication},
  SOURCETYPE = {Conference},
  ABSTRACT = { This paper describes an evolving computational model of
		  the perception and pro-duction of simple rhythmic patterns.
		  The model consists of a network of oscillators of different
		  resting frequencies which couple with input patterns and
		  with each other. Os-cillators whose frequencies match
		  periodicities in the input tend to become activated.
		  Metrical structure is represented explicitly in the network
		  in the form of clusters of os-cillators whose frequencies
		  and phase angles are constrained to maintain the harmonic
		  relationships that characterize meter. Rests in rhythmic
		  patterns are represented by ex-plicit rest oscillators in
		  the network, which become activated when an expected beat
		  in the pattern fails to appear. The model makes predictions
		  about the relative difficulty of patterns and the effect of
		  deviations from periodicity in the input.}
}

@TECHREPORT{gasser+eck+port:tr-1996,
  AUTHOR = {M. Gasser and D. Eck and R. Port},
  TITLE = {Meter as Mechanism A Neural Network that Learns Metrical
		  Patterns},
  INSTITUTION = {Indiana University Cognitive Science Program},
  YEAR = 1996,
  SOURCE = {OwnPublication},
  SOURCETYPE = {TechReport},
  NUMBER = {180}
}

@MISC{phd:paiement,
  AUTHOR = {{J.-F.} Paiement},
  TITLE = {A graphical model for music sequence learning},
  HOWPUBLISHED = {Ph.D. Dissertation. Ecole Polytechnique F\'{e}d\'{e}rale
		  de Lausanne (EPFL), Switzerland (Co-supervised with Samy
		  Bengio)},
  YEAR = {2009},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedDoc}
}

@MISC{phd:hamel,
  AUTHOR = {Philippe Hamel},
  TITLE = {Adaptive music generation},
  HOWPUBLISHED = {},
  YEAR = {to appear},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedDoc}
}

@MISC{phd:boulanger-lewandowski,
  AUTHOR = {Nicolas Boulanger-Lewandowski},
  TITLE = {Timing and dynamics of expressive performance},
  HOWPUBLISHED = {},
  YEAR = {to appear},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedDoc}
}

@MISC{phd:vernays,
  AUTHOR = {Michel Vernays},
  TITLE = {Computational model of piano timbre},
  HOWPUBLISHED = {},
  YEAR = {to appear},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedDoc}
}

@MISC{ms:bergeron,
  AUTHOR = {Arnaud Bergeron},
  TITLE = {Automatic expressive performance},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research},
  YEAR = {to appear},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:daouda,
  AUTHOR = {Tariq Daouda},
  TITLE = {Rhythm generation using reservoir computing},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research (Co-supervised
		  with Pascal Vincent)},
  YEAR = {to appear},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:bouchard,
  AUTHOR = {Lysiane Bouchard},
  TITLE = {Music and machine learning},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research (Co-supervised
		  with Pascal Vincent)},
  YEAR = {to appear},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:lemieux,
  AUTHOR = {Simon Lemieux},
  TITLE = {Methods for measuring similarity in melodic sequences},
  YEAR = {to appear},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:maillet,
  AUTHOR = {Francis Maillet},
  TITLE = {Automatic mastering of multi-track music recordings using
		  machine learning},
  YEAR = {to appear},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:bertin-mahieux,
  AUTHOR = {Thierry Bertin-Mahieux},
  TITLE = {Predicting social tags from audio features for music
		  recommendation},
  YEAR = {to appear},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research (Co-supervised
		  with Bal\'{a}zs K\'{e}gl)},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:wood,
  AUTHOR = {Sean Wood},
  TITLE = {Using matrix factorization for polyphonic pitch tracking},
  YEAR = {to appear},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:lauly,
  AUTHOR = {Stanislaus Lauly},
  TITLE = {A model of expressive performance timing for the piano},
  YEAR = {to appear},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:bergstra,
  AUTHOR = {James Bergstra},
  TITLE = {Automatic classification of recorded music using machine
		  learning},
  YEAR = {2006},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:lacoste,
  AUTHOR = {Alexandre Lacoste},
  TITLE = {Machine learning methods for identifying emergent
		  properties in complex music signals},
  YEAR = {2006},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:lapalme,
  AUTHOR = {Jasmin Lapalme},
  TITLE = {Music composition usion recurrent neural networks and
		  metrical structure},
  YEAR = {2005},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{ms:casagrande,
  AUTHOR = {N. Casagrande},
  TITLE = {A multi-class, multi-label AdaBoost algorithm that uses
		  rhythmical and visually-based features for sound and music
		  classification},
  YEAR = {2005},
  HOWPUBLISHED = {Master's Thesis. University of Montreal Department of
		  Computer Science and Operations Research (Co-supervised
		  with Bal\'{a}zs K\'{e}gl)},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnSupervisedMasters}
}

@MISC{talk:canheit2009,
  AUTHOR = {D. Eck},
  TITLE = {What can machines learn from music performances?},
  HOWPUBLISHED = {The Canadian Higher Education IT Conference, Montreal,
		  June},
  YEAR = {2009},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:vanier2009,
  AUTHOR = {D. Eck},
  TITLE = {Learning music at multiple timescales},
  HOWPUBLISHED = {Vanier College, Montreal, September},
  YEAR = {2009},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:ubisoft2009,
  AUTHOR = {D. Eck},
  TITLE = {Applying Machine Learning to Music and Motion},
  HOWPUBLISHED = {Ubisoft Inc., Montreal, January},
  YEAR = {2009},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:brams2009,
  AUTHOR = {D. Eck},
  TITLE = {An Overview of Machine Learning with Applications in Music
		  and Motion},
  HOWPUBLISHED = {BRAMS Scientific Day, Montreal, May},
  YEAR = {2009},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:cra2008,
  AUTHOR = {D. Eck},
  TITLE = {Measuring and Modeling Musical Expression},
  HOWPUBLISHED = {CRA Annual Meeting for Computer Science \& Computer
		  Engineering Chairs, Snowbird, Utah, July},
  YEAR = {2008},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:diro2008,
  AUTHOR = {D. Eck},
  TITLE = {Autotagger: an algorithm for automatically tagging music
		  collections using large-scale data mining and supervised
		  machine learning.},
  HOWPUBLISHED = {Departmental Colloquium, DIRO, University of Montreal,
		  Montreal, Canada, May},
  YEAR = {2008},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:pop2007,
  AUTHOR = {D. Eck},
  TITLE = {Panelist},
  HOWPUBLISHED = {Pop \& Policy Music Recommendation Panel, Montreal,
		  Canada, October},
  YEAR = {2007},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:google2007,
  AUTHOR = {D. Eck},
  TITLE = {Automatically Tagging Audio Files Using Supervised
		  Learning on Acoustic Features},
  HOWPUBLISHED = {Google Research Labs, Mountainview Calif, USA, April},
  YEAR = {2007},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:sun2006,
  AUTHOR = {D. Eck},
  TITLE = {An Ensemble Learning Approach to Music Classification},
  HOWPUBLISHED = {Sun labs, Sun Microsystems, Boston Mass, USA, April},
  YEAR = {2006},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:nyu2006,
  AUTHOR = {D. Eck},
  TITLE = {Learning Musical Structure},
  HOWPUBLISHED = {New York University Faculty of Music, New York, USA,
		  March},
  YEAR = {2006},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:mcgill2005,
  AUTHOR = {D. Eck},
  TITLE = {Predicting the Similarity Between Music Audio Files},
  HOWPUBLISHED = {CS Colloquium, McGill University, Montreal, Canada,
		  October},
  YEAR = {2005},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:cirmmt2005,
  AUTHOR = {D. Eck},
  TITLE = {Entropy and Autocorrelation: Using Simple Statistics to
		  Find Tempo and Metrical Structure in Unfiltered Digital
		  Audio},
  HOWPUBLISHED = {CIRMMT, McGill University, Montreal, Canada, March},
  YEAR = {2005},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:ofai2005,
  AUTHOR = {D. Eck},
  TITLE = {Using Autocorrelation to Find Tempo and Structure in
		  Music},
  HOWPUBLISHED = {Austrian Institute for Artificial Intelligence (OFAI),
		  Vienna, Austria, March},
  YEAR = {2005},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:cernec2004,
  AUTHOR = {D. Eck},
  TITLE = {Methods for Discovering Metrical Structure in Music},
  HOWPUBLISHED = {Research Center for Neuropsychology and Cognition
		  (CERNEC), U. of Montreal, Montreal, Canada, November},
  YEAR = {2004},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:ogi2003,
  AUTHOR = {D. Eck},
  TITLE = {{LSTM} Hybrid Recurrent Networks for Music and Speech},
  HOWPUBLISHED = {Oregon Graduate Institute (OGI), Portland, Oregon, USA,
		  March},
  YEAR = {2003},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:losalamos2003,
  AUTHOR = {D. Eck},
  TITLE = {Timeseries Analysis with {Long Short-Term Memory}
		  ({LSTM})},
  HOWPUBLISHED = {Los Alamos National Laboratories (LANL), Los Alamos, New
		  Mexico, USA, March},
  YEAR = {2003},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:ircam2002,
  AUTHOR = {D. Eck},
  TITLE = {Beat Induction with Spiking Neurons},
  HOWPUBLISHED = {L'Institut de Recherche et Coordination Acoustique/Musique
		  (IRCAM), Paris, France, August},
  YEAR = {2002},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:csl2002,
  AUTHOR = {D. Eck},
  TITLE = {Beat Induction with Spiking Neurons},
  HOWPUBLISHED = {Sony Computer Science Laboratory (CSL), Paris, France,
		  August},
  YEAR = {2002},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:usi2002,
  AUTHOR = {D. Eck},
  TITLE = {Time in Neural Networks},
  HOWPUBLISHED = {Università della Svizzera italiana, Lugano, Switzerland,
		  April},
  YEAR = {2002},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:nici2001,
  AUTHOR = {D. Eck},
  TITLE = {A Nonlinear Dynamical System for Rhythmic Pattern
		  Discovery},
  HOWPUBLISHED = {Nijmegen Institute for Cognition and Information (NICI),
		  Nijmegen, The Netherlands, July},
  YEAR = {2001},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}

@MISC{talk:fnn2001,
  AUTHOR = {D. Eck},
  TITLE = {Finding Temporal Structure in Music using Relaxation
		  Oscillators},
  HOWPUBLISHED = {Foundation for Neural Networks (FNN), Nijmegen, The
		  Netherlands, July},
  YEAR = {2001},
  SOURCE = {OwnPublication},
  SOURCETYPE = {OwnTalk}
}


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