Douglas Eck

I'm a research scientist working at the intersection of music and machine learning. Before coming to Google in 2010 I was an associate professor of computer science at Université de Montréal where I worked closely with the LISA machine learning lab, the BRAMS center for Brain Music and Sound and the CIRMMT group at McGill. I also worked with Ubisoft on video game player modeling.

I'm interested in a number of areas. All of them have to do with music but they're not necessarily related other than that. One of them is in measuring similarity between two audio files. Imagine you have a database of 500 million MP3s. How could you ever find new music you like among that many candidate tracks? You could always listen to music you already know, but that would get boring. You could shuffle play but that would be unsatisfactory, given the variety of music you'd find in so many songs. My research goal is to use machine learning and audio signal processing to transform audio into new spaces where musically similar songs are also numerically similar, making it easier to do music recommendation. Similarity in the domain of music turns out to be a slippery concept because music similarity is (a) somewhat user dependant; my idea of similar is not the same as yours and (b) changes with context; my idea of similar 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.

Another focus area of mine is that of music performance. In short: what makes a musical performance differ from it's score? Why do we bother to pay professional musicians to play music in the first place? For me the question is essentially a machine learning question: If we showed the 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 that comptuer-robot learn to play well by simply listening to them all and analyzing them? If so, what kind of analysis algorithms would we build into the robot?

When I'm not working I'm perhaps playing guitar, playing ultimate or hanging out with my family.

Google Publications

  •   

    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)

  •   

    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

  •  

    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)

  •  

    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)

  •  

    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)

  •  

    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)

  •   

    Probabilistic Models for Melodic Prediction

    Jean-Francois Paiement, Samy Bengio, Douglas Eck

    Artificial Intelligence Journal, vol. 173 (2009), pp. 1266-1274

  •  

    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)

  •  

    Towards a musical beat emphasis function

    M. Davies, M. Plumbley, Douglas Eck

    Proceedings of IEEE WASPAA, New Paltz, NY (2009)

  •   

    A Distance Model for Rhythms

    Jean-Francois Paiement, Yves Grandvalet, Samy Bengio, Douglas Eck

    International Conference on Machine Learning (ICML) (2008)

  •   

    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)

  •  

    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)

  •  

    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)

  •  

    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

  •  

    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)

  •   

    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)

  •  

    A Supervised Classification Algorithm For Note Onset Detection

    A. Lacoste, Douglas Eck

    EURASIP Journal on Applied Signal Processing, vol. 2007 (2007), pp. 1-13

  •  

    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)

  •  

    Beat Tracking Using an Autocorrelation Phase Matrix

    Douglas Eck

    Proceedings of the 2007 International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE Signal Processing Society, pp. 1313-1316

  •  

    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)

  •  

    Using 3D Visualizations to Explore and Discover Music

    P. Lamere, Douglas Eck

    Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007)

  •  

    Aggregate Features and AdaBoost for Music Classification

    J. Bergstra, N. Casagrande, D. Erhan, Douglas Eck, B. K{\'e}gl

    Machine Learning, vol. 65 (2006), pp. 473-484

  •  

    Beat Induction Using an Autocorrelation Phase Matrix

    Douglas Eck

    The Proceedings of the 9th International Conference on Music Perception and Cognition (ICMPC9), Causal Productions (2006), pp. 931-932

  •  

    Finding Long-Timescale Musical Structure with an Autocorrelation Phase Matrix

    Douglas Eck

    Music Perception, vol. 24 (2006), pp. 167-176

  •  

    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

  •  

    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

  •  

    Probabilistic Melodic Harmonization

    {J.-F.} Paiement, Douglas Eck, S. Bengio

    Canadian Conference on AI, Springer (2006), pp. 218-229

  •  

    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)

  •  

    A Probabilistic Model for Chord Progressions

    J.-F. Paiement, D. Eck, S. Bengio

    International Conference on Music Information Retrieval, ISMIR (2005)

  •  

    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

  •  

    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

  •  

    Editorial: New Research in Rhythm Perception and Production

    Douglas Eck, S. K. Scott

    Music Perception, vol. 22 (2005), pp. 371-388

  •  

    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

  •  

    Frame-Level Audio Feature Extraction using AdaBoost

    N. Casagrande, Douglas Eck, B. K\'{e}gl

    Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), London: University of London, pp. 345-350

  •  

    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

  •  

    Music Perception, Guest Editor, Special Issue on Rhythm Perception and Production

    Douglas Eck, S. K. Scott

    Music Perception, vol. 22 (3) (2005)

  •  

    A Machine-Learning Approach to Musical Sequence Induction That Uses Autocorrelation to Bridge Long Timelags

    Douglas Eck

    The Proceedings of the Eighth International Conference on Music Perception and Cognition (ICMPC8), Causal Productions, Adelaide (2004), pp. 542-543

  •  

    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

  •  

    Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets

    J.A. P\'{e}rez-Ortiz, F. A. Gers, Douglas Eck, J. Schmidhuber

    Neural Networks, vol. 16 (2003), pp. 241-250

  •  

    Finding Downbeats with a Relaxation Oscillator

    Douglas Eck

    Psychological Research, vol. 66 (2002), pp. 18-25

  •  

    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

  •  

    Improving Long-Term Online Prediction with Decoupled Extended Kalman Filters

    J.A. P\'{e}rez-Ortiz, J. Schmidhuber, F.A. Gers, Douglas Eck

    Artificial Neural Networks - ICANN 2002 (Proceedings), Springer, Berlin, pp. 1055-1060

  •  

    Learning Context Sensitive Languages with LSTM Trained with Kalman Filters

    F.A. Gers, J.A. P\'{e}rez-Ortiz, Douglas Eck, J. Schmidhuber

    Artificial Neural Networks - ICANN 2002 (Proceedings), Springer, Berlin, pp. 655-660

  •  

    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

  •  

    Learning The Long-Term Structure of the Blues

    Douglas Eck, J. Schmidhuber

    Artificial Neural Networks - ICANN 2002 (Proceedings), Springer, Berlin, pp. 284-289

  •  

    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

  •  

    A Network of Relaxation Oscillators that Finds Downbeats in Rhythms

    Douglas Eck

    Artificial Neural Networks - ICANN 2001 (Proceedings), Springer, Berlin, pp. 1239-1247

  •  

    A Positive-Evidence Model for Rhythmical Beat Induction

    Douglas Eck

    Journal of New Music Research, vol. 30 (2001), pp. 187-200

  •  

    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

  •  

    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

  •  

    Meter Through Synchrony: Processing Rhythmical Patterns with Relaxation Oscillators

    Douglas Eck

    Ph.D. Thesis, Indiana University, Bloomington, IN (2000)

  •  

    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)

  •  

    Learning Simple Metrical Preferences in a Network of Fitzhugh-Nagumo Oscillators

    Douglas Eck

    The Proceedings of the Twenty-First Annual Conference of the Cognitive Science Society, Lawrence Erlbaum Associates, New Jersey (1999)

  •  

    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

  •  

    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)

  •  

    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