Rethinking Automatic Chord Recognition with Convolutional Neural Networks

• Published in: 2012 Eleventh International Conference on Machine Learning and Applications Vol. 2; pp. 357 - 362
• Main Authors: Humphrey, E. J., Bello, J. P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2012
• Subjects: Accuracy; automatic music transcription; chord recognition; Computer architecture; convolutional neural nets; Kernel; Neural networks; Training; Training data; Vectors
• ISBN: 1467346519; 9781467346511
• DOI: 10.1109/ICMLA.2012.220

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.