Rethinking Automatic Chord Recognition with Convolutional Neural Networks

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 classi...

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Bibliographic Details
Published in2012 Eleventh International Conference on Machine Learning and Applications Vol. 2; pp. 357 - 362
Main Authors Humphrey, E. J., Bello, J. P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
Accuracy
automatic music transcription
chord recognition
Computer architecture
convolutional neural nets
Kernel
Neural networks
Training
Training data
Vectors
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ISBN1467346519
9781467346511
DOI10.1109/ICMLA.2012.220

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Summary: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.
ISBN:1467346519
9781467346511
DOI:10.1109/ICMLA.2012.220