Douglas Eck Publications

[1] B. Leonard, G. Sikora, M. De Francisco, and D. Eck. Acoustic space sampling and the grand piano in a non-anechoic environment: a recordist-centric approach to to musical acoustic study. In 129th Audio Engineering Society (AES) Convention, London, 2010.
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[2] A. Courville, D. Eck, and Y. Bengio. An infinite factor model hierarchy via a noisy-or mechanism. In Neural Information Processing Systems Conference 22 (NIPS'09), 2010. Accepted.
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[3] M. Davies, M. Plumbley, and D. Eck. Towards a musical beat emphasis function. In Proceedings of IEEE WASPAA, New Paltz, NY, 2009. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.
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[4] P. Hamel, S. Wood, and D. Eck. Automatic identification of instrument classes in polyphonic and poly-instrument audio. In Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR 2009), 2009.
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[5] F. Maillet, D. Eck, G. Desjardins, and P. Lamere. Steerable playlist generation by learning song similarity from radio station playlists. In Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR 2009), 2009.
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[6] J.-F. Paiement, S. Bengio, and D. Eck. Probabilistic models for melodic prediction. Artificial Intelligence Journal, 173:1266-1274, 2009.
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[7] J.-F. Paiement. A graphical model for music sequence learning. Ph.D. Dissertation. Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland (Co-supervised with Samy Bengio), 2009.
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[8] D. Eck. What can machines learn from music performances? The Canadian Higher Education IT Conference, Montreal, June, 2009.
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[9] D. Eck. Learning music at multiple timescales. Vanier College, Montreal, September, 2009.
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[10] D. Eck. Applying machine learning to music and motion. Ubisoft Inc., Montreal, January, 2009.
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[11] D. Eck. An overview of machine learning with applications in music and motion. BRAMS Scientific Day, Montreal, May, 2009.
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[12] T. Bertin-Mahieux, D. Eck, F. Maillet, and P. Lamere. Autotagger: A model for predicting social tags from acoustic features on large music databases. Journal of New Music Research, 37(2):115-135, 2008.
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[13] D. Eck and J. Lapalme. Learning musical structure directly from sequences of music. Technical Report 1300, Université de Montréal DIRO, http://www.iro.umontreal.ca/˜eckdoug/papers/tr1300.pdf, 2008.
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[14] P-A. Manzagol, T. Bertin-Mahieux, and D. Eck. On the use of sparse time relative auditory codes for music. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008), 2008.
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[15] B. Kégl, T. Bertin-Mahieux, and D. Eck. Metropolis-Hastings sampling in a FilterBoost music classifier. International Workshop on Machine Learning and Music (ICML/COLT/UAI 2008), Helsinki, Finland, 2008.
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[16] J.-F. Paiement, Y. Grandvalet, S. Bengio, and D. Eck. A generative model for rhythms. In ICML '08: Proceedings of the 25th International Conference on Machine Learning, 2008.
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[17] D. Eck, P. Lamere, T. Bertin-Mahieux, and S. Green. Automatic generation of social tags for music recommendation. In Neural Information Processing Systems Conference 20 (NIPS'07), 2008.
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[18] D. Eck. Measuring and modeling musical expression. CRA Annual Meeting for Computer Science & Computer Engineering Chairs, Snowbird, Utah, July, 2008.
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[19] D. Eck. Autotagger: an algorithm for automatically tagging music collections using large-scale data mining and supervised machine learning. Departmental Colloquium, DIRO, University of Montreal, Montreal, Canada, May, 2008.
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[20] D. Eck. Measuring and modeling musical expression. NIPS 2007 Workshop on Music, Brain and Cognition, 2007.
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[21] J.-F. Paiement, Y. Grandvalet, S. Bengio, and D. Eck. A generative model for rhythms. NIPS 2007 Workshop on Music, Brain and Cognition, 2007.
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[22] L. Pugin, J.A. Burgoyne, D. Eck, and I. Fujinaga. Book-adaptive and book-dependant models to accelerate digitalization of early music. NIPS 2007 Workshop on Music, Brain and Cognition, 2007.
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[23] D. Eck, T. Bertin-Mahieux, and P. Lamere. Autotagging music using supervised machine learning. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), 2007.
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[24] P. Lamere and D. Eck. Using 3d visualizations to explore and discover music. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), 2007.
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[25] H. Jaeger and D. Eck. Can't get you out of my head: A connectionist model of cyclic rehearsal. In Modeling Communications with Robots and Virtual Humans, LNCS. Springer-Verlag, 2007.
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[26] D. Eck. Beat tracking using an autocorrelation phase matrix. In Proceedings of the 2007 International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1313-1316. IEEE Signal Processing Society, 2007.
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[27] A. Lacoste and D. Eck. A supervised classification algorithm for note onset detection. EURASIP Journal on Applied Signal Processing, 2007(ID 43745):1-13, 2007.
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[28] D. Eck. Panelist. Pop & Policy Music Recommendation Panel, Montreal, Canada, October, 2007.
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[29] D. Eck. Automatically tagging audio files using supervised learning on acoustic features. Google Research Labs, Mountainview Calif, USA, April, 2007.
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[30] J. Bergstra, A. Lacoste, and D. Eck. Predicting genre labels for artists using freedb. In Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), pages 85-88, 2006.
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[31] D. Eck. Finding long-timescale musical structure with an autocorrelation phase matrix. Music Perception, 24(2):167-176, 2006.
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[32] J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and B. Kégl. Aggregate features and AdaBoost for music classification. Machine Learning, 65(2-3):473-484, 2006.
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[33] D. Eck. Beat induction using an autocorrelation phase matrix. In M. Baroni, A. R. Addessi, R. Caterina, and M. Costa, editors, The Proceedings of the 9th International Conference on Music Perception and Cognition (ICMPC9), pages 931-932. Causal Productions, 2006.
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[34] J.-F. Paiement, D. Eck, and S. Bengio. Probabilistic melodic harmonization. In Luc Lamontagne and Mario Marchand, editors, Canadian Conference on AI, volume 4013 of Lecture Notes in Computer Science, pages 218-229. Springer, 2006.
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[35] D. Eck. Generating music sequences with an echo state network. NIPS 2006 Workshop on Echo State Networks and Liquid State Machines, 2006.
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[36] James Bergstra. Automatic classification of recorded music using machine learning. Master's Thesis. University of Montreal Department of Computer Science and Operations Research, 2006.
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[37] Alexandre Lacoste. Machine learning methods for identifying emergent properties in complex music signals. Master's Thesis. University of Montreal Department of Computer Science and Operations Research, 2006.
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[38] D. Eck. An ensemble learning approach to music classification. Sun labs, Sun Microsystems, Boston Mass, USA, April, 2006.
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[39] D. Eck. Learning musical structure. New York University Faculty of Music, New York, USA, March, 2006.
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[40] D. Eck and S. K. Scott. Music perception, 2005. Guest Editor, Special Issue on Rhythm Perception and Production, 22(3).
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[41] D. Eck and S. K. Scott. Editorial: New research in rhythm perception and production. Music Perception, 22(3):371-388, 2005.
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[42] J.-F. Paiement, D. Eck, S. Bengio, and D. Barber. A graphical model for chord progressions embedded in a psychoacoustic space. In ICML '05: Proceedings of the 22nd international conference on Machine learning, pages 641-648, New York, NY, USA, 2005. ACM Press.
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[43] N. Casagrande, D. Eck, and B. Kegl. Geometry in sound: A speech/music audio classifier inspired by an image classifier. In Proceedings of the International Computer Music Conference (ICMC), pages 207-210, 2005.
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[44] D. Eck. Meter and autocorrelation. 10th Rhythm Perception and Production Workshop (RPPW), Alden Biesen, Belgium, 2005.
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[45] J.-F. Paiement, D. Eck, and S. Bengio. A probabilistic model for chord progressions. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), pages 312-319, London: University of London, 2005.
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[46] D. Eck and N. Casagrande. Finding meter in music using an autocorrelation phase matrix and shannon entropy. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), pages 504-509, London: University of London, 2005.
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[47] N. Casagrande, D. Eck, and B. Kégl. Frame-level audio feature extraction using AdaBoost. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), pages 345-350, London: University of London, 2005.
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[48] J. Bergstra, N. Casagrande, and D. Eck. Genre classification: Timbre- and rhythm-based multiresolution audio classification. MIREX genre classification contest, 2005.
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[49] J. Bergstra, N. Casagrande, and D. Eck. Artist recognition: A timbre- and rhythm-based multiresolution approach. MIREX artist recognition contest, 2005.
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[50] A. Lacoste and D. Eck. Onset detection with artificial neural networks. MIREX note onset detection contest, 2005.
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[51] D. Eck and N. Casagrande. A tempo-extraction algorithm using an autocorrelation phase matrix and shannon entropy. MIREX tempo extraction contest (www.music-ir.org/evaluation/mirex-results), 2005.
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[52] Jasmin Lapalme. Music composition usion recurrent neural networks and metrical structure. Master's Thesis. University of Montreal Department of Computer Science and Operations Research, 2005.
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[53] N. Casagrande. A multi-class, multi-label adaboost algorithm that uses rhythmical and visually-based features for sound and music classification. Master's Thesis. University of Montreal Department of Computer Science and Operations Research (Co-supervised with Balázs Kégl), 2005.
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[54] D. Eck. Predicting the similarity between music audio files. CS Colloquium, McGill University, Montreal, Canada, October, 2005.
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[55] D. Eck. Entropy and autocorrelation: Using simple statistics to find tempo and metrical structure in unfiltered digital audio. CIRMMT, McGill University, Montreal, Canada, March, 2005.
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[56] D. Eck. Using autocorrelation to find tempo and structure in music. Austrian Institute for Artificial Intelligence (OFAI), Vienna, Austria, March, 2005.
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[57] D. Eck. Bridging long timelags in music. NIPS 2004 Workshop on Music and Machine Learning (MIPS), Whistler, British Columbia, 2004.
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[58] D. Eck. Challenges for machine learning in the domain of music. BRAMS Workshop on Brain and Music, Montreal Neurological Institute, 2004.
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[59] D. Eck. A machine-learning approach to musical sequence induction that uses autocorrelation to bridge long timelags. In S.D. Lipscomb, R. Ashley, R.O. Gjerdingen, and P. Webster, editors, The Proceedings of the Eighth International Conference on Music Perception and Cognition (ICMPC8), pages 542-543, Adelaide, 2004. Causal Productions.
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[60] A. Graves, D. Eck, N. Beringer, and J. Schmidhuber. Biologically plausible speech recognition with LSTM neural nets. In Proceedings of the First Int'l Workshop on Biologically Inspired Approaches to Advanced Information Technology (Bio-ADIT), pages 127-136, 2004.
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[61] D. Eck. Using autocorrelation to bridge long timelags when learning sequences of music. IRIS 2004 Machine Learning Workshop, Ottawa, Canada, 2004.
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[62] D. Eck. Methods for discovering metrical structure in music. Research Center for Neuropsychology and Cognition (CERNEC), U. of Montreal, Montreal, Canada, November, 2004.
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[63] D. Eck, A. Graves, and J. Schmidhuber. A new approach to continuous speech recognition using LSTM recurrent neural networks. Technical Report IDSIA-14-03, IDSIA, www.idsia.ch/techrep.html, May 2003.
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[64] A. Graves, D. Eck, and J. Schmidhuber. Comparing LSTM recurrent networks and spiking recurrent networks on the recognition of spoken digits. Technical Report IDSIA-13-03, IDSIA, www.idsia.ch/techrep.html, May 2003.
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[65] J.A. Pérez-Ortiz, F. A. Gers, D. Eck, and J. Schmidhuber. Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Networks, 16(2):241-250, 2003.
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[66] D. Eck. Time-warped hierarchical structure in music and speech: A sequence prediction challenge. NIPS 2003 Workshop on Recurrent Neural Networks, Whistler, British Columbia, 2003.
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[67] D. Eck. LSTM hybrid recurrent networks for music and speech. Oregon Graduate Institute (OGI), Portland, Oregon, USA, March, 2003.
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[68] D. Eck. Timeseries analysis with Long Short-Term Memory (LSTM). Los Alamos National Laboratories (LANL), Los Alamos, New Mexico, USA, March, 2003.
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[69] D. Eck. Real-time musical beat induction with spiking neural networks. Technical Report IDSIA-22-02, IDSIA, www.idsia.ch/techrep.html, October 2002.
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[70] D. Eck and J. Schmidhuber. A first look at music composition using LSTM recurrent neural networks. Technical Report IDSIA-07-02, IDSIA, www.idsia.ch/techrep.html, March 2002.
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[71] D. Eck. Finding downbeats with a relaxation oscillator. Psychol. Research, 66(1):18-25, 2002.
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[72] J. Schmidhuber, F.A. Gers, and D. Eck. Learning nonregular languages: A comparison of simple recurrent networks and LSTM. Neural Computation, 14(9):2039-2041, 2002.
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[73] D. Eck and J. Schmidhuber. Finding temporal structure in music: Blues improvisation with LSTM recurrent networks. In H. Bourlard, editor, Neural Networks for Signal Processing XII, Proceedings of the 2002 IEEE Workshop, pages 747-756, New York, 2002. IEEE.
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[74] D. Eck. Real time beat induction with spiking neurons. Music, Motor Control and the Mind: Symposium at Monte Verita, May, 2002.
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[75] D. Eck and J. Schmidhuber. Learning the long-term structure of the blues. In J. Dorronsoro, editor, Artificial Neural Networks - ICANN 2002 (Proceedings), pages 284-289, Berlin, 2002. Springer.
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[76] F.A. Gers, J.A. Perez-Ortiz, D. Eck, and J. Schmidhuber. DEKF-LSTM. In Proceedings of the 10th European Symposium on Artificial Neural Networks, ESANN 2002, 2002.
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[77] F.A. Gers, J.A. Pérez-Ortiz, D. Eck, and J. Schmidhuber. Learning context sensitive languages with LSTM trained with Kalman filters. In J. Dorronsoro, editor, Artificial Neural Networks - ICANN 2002 (Proceedings), pages 655-660, Berlin, 2002. Springer.
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[78] J.A. Pérez-Ortiz, J. Schmidhuber, F.A. Gers, and D. Eck. Improving long-term online prediction with Decoupled Extended Kalman Filters. In J. Dorronsoro, editor, Artificial Neural Networks - ICANN 2002 (Proceedings), pages 1055-1060, Berlin, 2002. Springer.
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[79] D. Eck. Beat induction with spiking neurons. L'Institut de Recherche et Coordination Acoustique/Musique (IRCAM), Paris, France, August, 2002.
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[80] D. Eck. Beat induction with spiking neurons. Sony Computer Science Laboratory (CSL), Paris, France, August, 2002.
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[81] D. Eck. Time in neural networks. Universit√ɬ† della Svizzera italiana, Lugano, Switzerland, April, 2002.
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[82] D. Eck. A network of relaxation oscillators that finds downbeats in rhythms. Technical Report IDSIA-06-01, IDSIA, www.idsia.ch/techrep.html, February 2001.
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[83] D. Eck. A positive-evidence model for rhythmical beat induction. Journal of New Music Research, 30(2):187-200, 2001.
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[84] D. Eck. A network of relaxation oscillators that finds downbeats in rhythms. In Georg Dorffner, editor, Artificial Neural Networks - ICANN 2001 (Proceedings), pages 1239-1247, Berlin, 2001. Springer.
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[85] F. A. Gers, D. Eck, and J. Schmidhuber. Applying LSTM to time series predictable through time-window approaches. In Georg Dorffner, editor, Artificial Neural Networks - ICANN 2001 (Proceedings), pages 669-676, Berlin, 2001. Springer.
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[86] D. Eck. A nonlinear dynamical system for rhythmic pattern discovery. Nijmegen Institute for Cognition and Information (NICI), Nijmegen, The Netherlands, July, 2001.
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[87] D. Eck. Finding temporal structure in music using relaxation oscillators. Foundation for Neural Networks (FNN), Nijmegen, The Netherlands, July, 2001.
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[88] F. A. Gers, D. Eck, and J. Schmidhuber. Applying LSTM to time series predictable through time-window approaches. Technical Report IDSIA-22-00, IDSIA, www.idsia.ch/techrep.html, December 2000.
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[89] D. Eck. Tracking rhythms with a relaxation oscillator. Technical Report IDSIA-10-00, IDSIA, www.idsia.ch/techrep.html, October 2000.
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[90] D. Eck. A positive-evidence model for classifying rhythmical patterns. Technical Report IDSIA-09-00, IDSIA, www.idsia.ch/techrep.html, October 2000.
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[91] D. Eck, M. Gasser, and Robert Port. Dynamics and embodiment in beat induction. In P. Desain and L. Windsor, editors, Rhythm Perception and Production, pages 157-170. Swets and Zeitlinger, Lisse, The Netherlands, 2000.
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[92] D. Eck. Meter Through Synchrony: Processing Rhythmical Patterns with Relaxation Oscillators. PhD thesis, Indiana University, Bloomington, IN, www.idsia.ch/˜doug/publications.html, 2000.
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[93] M. Gasser, D. Eck, and R. Port. Meter as mechanism: A neural network model that learns metrical patterns. Connection Science, 11(2):187-216, 1999.
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[94] D. Eck. Learning simple metrical preferences in a network of Fitzhugh-Nagumo oscillators. In The Proceedings of the Twenty-First Annual Conference of the Cognitive Science Society, New Jersey, 1999. Lawrence Erlbaum Associates.
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[95] T. Chemero and D. Eck. An exploration of representational complexity via coupled oscillators. In Proceedings of the Tenth Midwest Artificial Intelligence and Cognitive Science Society, Cambridge, Mass., 1999. MIT Press.
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[96] D. Eck and M. Gasser. Perception of simple rhythmic patterns in a network of oscillators. In The Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society, New Jersey, 1996. Lawrence Erlbaum Associates.
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[97] M. Gasser and D. Eck. Representing rhythmic patterns in a network of oscillators. In The Proceedings of the International Conference on Music Perception and Cognition, number 4, pages 361-366, New Jersey, 1996. Lawrence Erlbaum Associates.
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[98] M. Gasser, D. Eck, and R. Port. Meter as mechanism a neural network that learns metrical patterns. Technical Report 180, Indiana University Cognitive Science Program, 1996.
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[99] Philippe Hamel. Adaptive music generation, to appear.
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[100] Nicolas Boulanger-Lewandowski. Timing and dynamics of expressive performance, to appear.
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[101] Michel Vernays. Computational model of piano timbre, to appear.
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[102] Arnaud Bergeron. Automatic expressive performance. Master's Thesis. University of Montreal Department of Computer Science and Operations Research, to appear.
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[103] Tariq Daouda. Rhythm generation using reservoir computing. Master's Thesis. University of Montreal Department of Computer Science and Operations Research (Co-supervised with Pascal Vincent), to appear.
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[104] Lysiane Bouchard. Music and machine learning. Master's Thesis. University of Montreal Department of Computer Science and Operations Research (Co-supervised with Pascal Vincent), to appear.
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[105] Simon Lemieux. Methods for measuring similarity in melodic sequences. Master's Thesis. University of Montreal Department of Computer Science and Operations Research, to appear.
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[106] Francis Maillet. Automatic mastering of multi-track music recordings using machine learning. Master's Thesis. University of Montreal Department of Computer Science and Operations Research, to appear.
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[107] Thierry Bertin-Mahieux. Predicting social tags from audio features for music recommendation. Master's Thesis. University of Montreal Department of Computer Science and Operations Research (Co-supervised with Balázs Kégl), to appear.
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[108] Sean Wood. Using matrix factorization for polyphonic pitch tracking. Master's Thesis. University of Montreal Department of Computer Science and Operations Research, to appear.
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[109] Stanislaus Lauly. A model of expressive performance timing for the piano. Master's Thesis. University of Montreal Department of Computer Science and Operations Research, to appear.
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