Douglas Eck
Co-Authors
I have also worked on music recommendation for Play Music, involving both both learning from audio and learning from how users consume music. In the audio domain, the main goal is to transform the ones and zeros in a digital audio file into something where musically-similar songs are also numerically similar, making it easier to do music recommendation. This is (a) user-dependent: my idea of similar is not the same as yours and (b) changes with context: my idea of similarity changes when I make a playlist for jogging versus making a playlist for a dinner party. I might choose the same song (say "Taxman" by the Beatles) but perhaps it would be the tempo for jogging that drove the selection of that specific song versus "I like the album Revolver and want to add it to the dinner party mix" for a dinner party playlist.
Before joining Google in 2010, I was an Associate Professor in Computer Science at University of Montreal. I helped found the BRAMS research center (Brain Music and Sound; www.brams.org) and was involved at the McGill CIRMMT center (Centre for Interdisciplinary Research in Music Media and Technology; www.cirmmt.org). Aside from audio signal processing and machine learning, I worked on music performance modeling. What exactly does a good music performer add to what is already in the score? I treated this as a machine learning question: Hypothetically, if we showed a piano-playing robot a huge collection of Chopin performances--- from the best in the world all the way down to that of a struggling teenage pianist---could it learn to play well by analyzing all of these examples? If so, what’s the right way to perform that analysis? In the end I learned a lot about the complexity and beauty of human music performance, and how performance relates to and extends composition.
Google Publications
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A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Adam Roberts, Jesse Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck
arXiv Preprint (2018)
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A Neural Representation of Sketch Drawings
ICLR 2018
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Learning Latent Representations of Music to Generate Interactive Musical Palettes
Adam Roberts, Jesse Engel, Sageev Oore, Douglas Eck
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A Neural Representation of Sketch Drawings
arXiv (2017)
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Cheng-Zhi Anna Huang, Tim Cooijmans, Adam Roberts, Aaron Courville, Douglas Eck
Proceedings of ISMIR 2017
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Deep Music: Towards Musical Dialogue
Mason Bretan, Sageev Oore, Jesse Engel, Douglas Eck, Larry Heck
AAAI, AAAI, AAAI (2017)
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Hierarchical Variational Autoencoders for Music
Adam Roberts, Jesse Engel, Douglas Eck
(2017)
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Improving image generative models with human interactions
Andrew Lampinen, David Richard So, Douglas Eck, Fred Bertsch
arXiv (2017)
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Learning to Create Piano Performances
Sageev Oore, Ian Simon, Sander Dieleman, Doug Eck
NIPS 2017 Workshop on Machine Learning and Creativity
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Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, Mohammad Norouzi
ICML (2017)
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Online and Linear-Time Attention by Enforcing Monotonic Alignments
Colin Raffel, Douglas Eck, Peter Liu, Ron J. Weiss, Thang Luong
Thirty-fourth International Conference on Machine Learning (2017)
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Onsets and Frames: Dual-Objective Piano Transcription
Curtis Hawthorne, Erich Elsen, Jialin Song, Adam Roberts, Ian Simon, Colin Raffel, Jesse Engel, Sageev Oore, Douglas Eck
arXiv Preprint (2017)
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Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck
ICML (2017)
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Tuning Recurrent Neural Networks With Reinforcement Learning
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, Jose Miguel Hernandez Lobato, Richard E. Turner, Doug Eck
ICLR Workshop (2017)
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Audio Deepdream: Optimizing raw audio with convolutional networks
Adam Roberts, Cinjon Resnick, Diego Ardila, Doug Eck
International Society for Music Information Retrieval Conference, Google Brain (2016)
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Generating Music by Fine-Tuning Recurrent Neural Networks with Reinforcement Learning
Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck
Deep Reinforcement Learning Workshop, NIPS (2016)
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Multi-Task Convolutional Music Models
Adam Roberts, Cinjon Resnick, Diego Ardila, Doug Eck
BayLearn (2016)
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Building Musically-relevant Audio Features through Multiple Timescale Representations
Philippe Hamel, Yoshua Bengio, Douglas Eck
Proceedings of the 13th International Society for Music Information Retrieval Conference, Porto, Portugal (2012)
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Temporal pooling and multiscale learning for automatic annotation and ranking of music audio
Philippe Hamel, Simon Lemieux, Yoshua Bengio, Douglas Eck
International Society for Music Information Retrieval (ISMIR 2011)
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The Need for Music Information Retrieval with User-Centered and Multimodal Strategies
Cynthia C.S. Liem, Meinard Müller, Douglas Eck, George Tzanetakis, Alan Hanjalic
MIRUM '11, ACM, Scottsdale, Arizona (2011), pp. 1-6
Previous Publications
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Acoustic Space Sampling and the Grand Piano in a Non-Anechoic Environment: a recordist-centric approach to musical acoustic study
B. Leonard, G. Sikora, M. De Francisco, Douglas Eck
129th Audio Engineering Society (AES) Convention, London (2010)
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Acoustic Space Sampling and the Grand Piano in a Non-Anechoic Environment: a recordist-centric approach to to musical acoustic study
B. Leonard, G. Sikora, M. De Francisco, Douglas Eck
129th Audio Engineering Society (AES) Convention, London (2010)
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An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism
A. Courville, Douglas Eck, Y. Bengio
Neural Information Processing Systems Conference 22 (NIPS'09) (2010)
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Automatic identification of instrument classes in polyphonic and poly-instrument audio
P. Hamel, S. Wood, Douglas Eck
Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR 2009)
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Probabilistic Models for Melodic Prediction
Jean-Francois Paiement, Samy Bengio, Douglas Eck
Artificial Intelligence Journal, vol. 173 (2009), pp. 1266-1274
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Steerable Playlist Generation by Learning Song Similarity from Radio Station Playlists
F. Maillet, Douglas Eck, G. Desjardins, P. Lamere
Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR 2009)
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Towards a musical beat emphasis function
M. Davies, M. Plumbley, Douglas Eck
Proceedings of IEEE WASPAA, New Paltz, NY (2009)
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A Distance Model for Rhythms
Jean-Francois Paiement, Yves Grandvalet, Samy Bengio, Douglas Eck
International Conference on Machine Learning (ICML) (2008)
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A Generative Model for Rhythms
Jean-Francois Paiement, Samy Bengio, Yves Grandvalet, Doug Eck
Neural Information Processing Systems, Workshop on Brain, Music and Cognition (2008)
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A generative model for rhythms
{J.-F.} Paiement, Y. Grandvalet, S. Bengio, Douglas Eck
ICML '08: Proceedings of the 25th International Conference on Machine Learning (2008)
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Automatic generation of social tags for music recommendation
Douglas Eck, P. Lamere, T. Bertin-Mahieux, S. Green
Neural Information Processing Systems Conference 20 (NIPS'07) (2008)
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Autotagger: A Model For Predicting Social Tags from Acoustic Features on Large Music Databases
T. Bertin-Mahieux, Douglas Eck, F. Maillet, P. Lamere
Journal of New Music Research, vol. 37 (2008), pp. 115-135
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On the use of Sparse Time Relative Auditory Codes for Music
P-A. Manzagol, T. Bertin-Mahieux, Douglas Eck
Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008)
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A Generative Model for Distance Patterns in Music
Jean-Francois Paiement, Yves Grandvalet, Samy Bengio, Douglas Eck
NIPS Workshop on Music, Brain and Cognition (2007)
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A Supervised Classification Algorithm For Note Onset Detection
A. Lacoste, Douglas Eck
EURASIP Journal on Applied Signal Processing, vol. 2007 (2007), pp. 1-13
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Autotagging music using supervised machine learning
Douglas Eck, T. Bertin-Mahieux, P. Lamere
Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007)
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Beat Tracking Using an Autocorrelation Phase Matrix
Proceedings of the 2007 International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE Signal Processing Society, pp. 1313-1316
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Can't get you out of my head: A connectionist model of cyclic rehearsal
H. Jaeger, Douglas Eck
Modeling Communications with Robots and Virtual Humans, Springer-Verlag (2007)
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Using 3D Visualizations to Explore and Discover Music
P. Lamere, Douglas Eck
Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007)
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Aggregate Features and AdaBoost for Music Classification
J. Bergstra, N. Casagrande, D. Erhan, Douglas Eck, B. Kégl
Machine Learning, vol. 65 (2006), pp. 473-484
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Beat Induction Using an Autocorrelation Phase Matrix
The Proceedings of the 9th International Conference on Music Perception and Cognition (ICMPC9), Causal Productions (2006), pp. 931-932
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Finding Long-Timescale Musical Structure with an Autocorrelation Phase Matrix
Music Perception, vol. 24 (2006), pp. 167-176
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Predicting genre labels for artists using FreeDB
J. Bergstra, A. Lacoste, Douglas Eck
Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), pp. 85-88
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Probabilistic Melodic Harmonization
J.-F. Paiement, D. Eck, S. Bengio
Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI, Lecture Notes in Computer Science, Springer-Verlag (2006), pp. 218-229
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Probabilistic Melodic Harmonization
{J.-F.} Paiement, Douglas Eck, S. Bengio
Canadian Conference on AI, Springer (2006), pp. 218-229
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A Graphical Model for Chord Progressions Embedded in a Psychoacoustic Space
J.-F. Paiement, D. Eck, S. Bengio, D. Barber
International Conference on Machine Learning, ICML (2005)
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A Probabilistic Model for Chord Progressions
J.-F. Paiement, D. Eck, S. Bengio
International Conference on Music Information Retrieval, ISMIR (2005)
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A Probabilistic Model for Chord Progressions
{J.-F.} Paiement, Douglas Eck, S. Bengio
Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London: University of London, pp. 312-319
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A graphical model for chord progressions embedded in a psychoacoustic space
{J.-F.} Paiement, Douglas Eck, S. Bengio, D. Barber
ICML '05: Proceedings of the 22nd international conference on Machine learning, ACM Press, New York, NY, USA (2005), pp. 641-648
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Editorial: New Research in Rhythm Perception and Production
Douglas Eck, S. K. Scott
Music Perception, vol. 22 (2005), pp. 371-388
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Finding Meter in Music Using an Autocorrelation Phase Matrix and Shannon Entropy
Douglas Eck, N. Casagrande
Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London: University of London, pp. 504-509
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Frame-Level Audio Feature Extraction using AdaBoost
N. Casagrande, Douglas Eck, B. Kégl
Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London: University of London, pp. 345-350
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Geometry in Sound: A Speech/Music Audio Classifier Inspired by an Image Classifier
N. Casagrande, Douglas Eck, B. Kegl
Proceedings of the International Computer Music Conference (ICMC) (2005), pp. 207-210
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Music Perception, Guest Editor, Special Issue on Rhythm Perception and Production
Douglas Eck, S. K. Scott
Music Perception, vol. 22 (3) (2005)
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A Machine-Learning Approach to Musical Sequence Induction That Uses Autocorrelation to Bridge Long Timelags
The Proceedings of the Eighth International Conference on Music Perception and Cognition (ICMPC8), Causal Productions, Adelaide (2004), pp. 542-543
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Biologically Plausible Speech Recognition with LSTM Neural Nets
A. Graves, Douglas Eck, N. Beringer, J. Schmidhuber
Proceedings of the First Int'l Workshop on Biologically Inspired Approaches to Advanced Information Technology (Bio-ADIT) (2004), pp. 127-136
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Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets
J.A. Pérez-Ortiz, F. A. Gers, Douglas Eck, J. Schmidhuber
Neural Networks, vol. 16 (2003), pp. 241-250
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DEKF-LSTM
F.A. Gers, J.A. Perez-Ortiz, Douglas Eck, J. Schmidhuber
Proceedings of the 10th European Symposium on Artificial Neural Networks, ESANN 2002
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Finding Downbeats with a Relaxation Oscillator
Psychological Research, vol. 66 (2002), pp. 18-25
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Finding Temporal Structure in Music: Blues Improvisation with LSTM Recurrent Networks
Douglas Eck, J. Schmidhuber
Neural Networks for Signal Processing XII, Proceedings of the 2002 IEEE Workshop, IEEE, New York, pp. 747-756
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Improving Long-Term Online Prediction with Decoupled Extended Kalman Filters
J.A. Pérez-Ortiz, J. Schmidhuber, F.A. Gers, Douglas Eck
Artificial Neural Networks -- ICANN 2002 (Proceedings), Springer, Berlin, pp. 1055-1060
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Learning Context Sensitive Languages with LSTM Trained with Kalman Filters
F.A. Gers, J.A. Pérez-Ortiz, Douglas Eck, J. Schmidhuber
Artificial Neural Networks -- ICANN 2002 (Proceedings), Springer, Berlin, pp. 655-660
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Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM
J. Schmidhuber, F.A. Gers, Douglas Eck
Neural Computation, vol. 14 (2002), pp. 2039-2041
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Learning The Long-Term Structure of the Blues
Douglas Eck, J. Schmidhuber
Artificial Neural Networks -- ICANN 2002 (Proceedings), Springer, Berlin, pp. 284-289
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A Network of Relaxation Oscillators that Finds Downbeats in Rhythms
Artificial Neural Networks -- ICANN 2001 (Proceedings), Springer, Berlin, pp. 1239-1247
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A Positive-Evidence Model for Rhythmical Beat Induction
Journal of New Music Research, vol. 30 (2001), pp. 187-200
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Applying LSTM to Time Series Predictable Through Time-Window Approaches
F. A. Gers, Douglas Eck, J. Schmidhuber
Artificial Neural Networks -- ICANN 2001 (Proceedings), Springer, Berlin, pp. 669-676
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Dynamics and Embodiment in Beat Induction
Douglas Eck, M. Gasser, Robert Port
Rhythm Perception and Production, Swets and Zeitlinger, Lisse, The Netherlands (2000), pp. 157-170
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Meter Through Synchrony: Processing Rhythmical Patterns with Relaxation Oscillators
Ph.D. Thesis, Indiana University, Bloomington, IN (2000)
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An Exploration of Representational Complexity via Coupled Oscillators
T. Chemero, Douglas Eck
Proceedings of the Tenth Midwest Artificial Intelligence and Cognitive Science Society, MIT Press, Cambridge, Mass. (1999)
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Learning Simple Metrical Preferences in a Network of Fitzhugh-Nagumo Oscillators
The Proceedings of the Twenty-First Annual Conference of the Cognitive Science Society, Lawrence Erlbaum Associates, New Jersey (1999)
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Meter as Mechanism: A Neural Network Model that Learns Metrical patterns
M. Gasser, Douglas Eck, R. Port
Connect. Sci., vol. 11, no. 2 (1999), pp. 187-216
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Perception of Simple Rhythmic Patterns in a Network of Oscillators
Douglas Eck, M. Gasser
The Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society, Lawrence Erlbaum Associates, New Jersey (1996)
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Representing Rhythmic Patterns in a Network of Oscillators
M. Gasser, Douglas Eck
The Proceedings of the International Conference on Music Perception and Cognition, Lawrence Erlbaum Associates, New Jersey (1996), pp. 361-366