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Automatic Chord Recognition from Audio Using a HMM with Supervised Learning

In this paper, we propose a novel method for obtaining labeled training data to estimate the parameters in a supervised learning model for automatic chord recognition. To this end, we perform harmonic analysis on symbolic data to generate label files. In parallel, we generate audio data from the same symbolic data, which are then provided to a machine learning algorithm along with label files to estimate model parameters. Experimental results show higher performance in frame-level chord recognition than the previous approaches.