

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.
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.
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.
Eric J. Humphrey
Ph.D. Thesis, New York University, New York, NY, 2015
S. Durand, J.P. Bello, B. David, G. Richard
ICASSP, South Brisbane, QLD, April 2015
E.J. Humphrey, J.P. Bello
ICASSP, Florence, Italy, May 2014
E. Humphrey, J.P. Bello, Y. LeCun
Journal of Intelligent Information Systems 2013, 2013
E. Humphrey, J.P. Bello
Proceedings of the 11th International Conference on Machine Learning and Applications (ICMLA-12), Boca Raton, FL, USA. December. 2012
E. Humphrey, J.P. Bello, Y. LeCun
Music Informatics Proceedings of the International Conference on Music Information Retrieval (ISMIR-12), Porto, Portugal. October. 2012
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