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Research Studio at Music and Audio Research Laboratory

Deep Learning in Music Informatics


Deep architectures and hierarchical feature learning are burgeoning research topics with a variety of computer science and engineering application areas. Noting the specific challenges and opportunities faced in working to make machines more musically intelligent, MARL is actively exploring these promising methods in music informatics research (MIR). Here we provide the slides of a recent jointly organized presentation by deep learning practitioners in MIR, a walk-through programming tutorial tailored to the interests of MIR researchers, and point to a selection of some of our published work to date.

Deep Learning in Music Informatics - Demystifying the Dark Art

Given a growing interest within the MIR community, Erik M. Schmidt (Pandora), Philippe Hamel (Google), and Eric J. Humphrey (MARL) joined forces to present a thorough review of deep learning to the attendees of ISMIR2013 in Curitiba, PR, Brazil.

  • Part I – Background, E. M. Schmidt
  • Part II – State of the Art, P. Hamel
  • Part III – Practicum, E. J. Humphrey

Python Tutorial

After covering the “why” and “what” of deep learning, it can also be helpful to see the “how” as well. To these ends, we’ve assembled some deep learning examples specific to music informatics, complete with source code (Python) and pre-processed data to get you up and running quickly.


    An Exploration of Deep Learning in Content-Based Music Informatics

    Eric J. Humphrey
    Ph.D. Thesis, New York University, New York, NY, 2015

    Downbeat tracking with multiple features and deep neural networks

    S. Durand, J.P. Bello, B. David, G. Richard
    ICASSP, South Brisbane, QLD, April 2015

    From music audio to chord tablature: Teaching deep convolutional networks to play guitar

    E.J. Humphrey, J.P. Bello
    ICASSP, Florence, Italy, May 2014

    Feature Learning and Deep Architectures: New Directions for Music Informatics

    E. Humphrey, J.P. Bello, Y. LeCun
    Journal of Intelligent Information Systems 2013, 2013

    Rethinking Automatic Chord Recognition with Convolutional Neural Networks

    E. Humphrey, J.P. Bello
    Proceedings of the 11th International Conference on Machine Learning and Applications (ICMLA-12), Boca Raton, FL, USA. December. 2012

    Moving Beyond Feature Design: Deep Architectures and Automatic Feature Learning

    E. Humphrey, J.P. Bello, Y. LeCun
    Music Informatics Proceedings of the International Conference on Music Information Retrieval (ISMIR-12), Porto, Portugal. October. 2012

    Learning a robust tonnetz-space transform for automatic chord recognition

    E.J. Humphrey, T. Cho, J.P. Bello
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-12) Kyoto, Japan, May 2012