Rethinking automatic chord recognition with convolutional neural networks

Eric J. Humphrey, Juan P. Bello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Despite early success in automatic chord recognition, recent efforts are yielding diminishing returns while basically iterating over the same fundamental approach. Here, we abandon typical conventions and adopt a different perspective of the problem, where several seconds of pitch spectra are classified directly by a convolutional neural network. Using labeled data to train the system in a supervised manner, we achieve state of the art performance through this initial effort in an otherwise unexplored area. Subsequent error analysis provides insight into potential areas of improvement, and this approach to chord recognition shows promise for future harmonic analysis systems.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages357-362
Number of pages6
DOIs
StatePublished - 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: Dec 12 2012Dec 15 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume2

Other

Other11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
CountryUnited States
CityBoca Raton, FL
Period12/12/1212/15/12

Keywords

  • automatic music transcription
  • chord recognition
  • convolutional neural nets

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

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  • Cite this

    Humphrey, E. J., & Bello, J. P. (2012). Rethinking automatic chord recognition with convolutional neural networks. In Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 (pp. 357-362). [6406762] (Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012; Vol. 2). https://doi.org/10.1109/ICMLA.2012.220