Automatic chord recognition from audio using a supervised HMM trained with audio-from-symbolic data

A novel approach for obtaining labeled training data is presented to directly estimate the model parameters in a supervised learning algorithm for automatic chord recognition from the raw audio. To this end, harmonic analysis is first performed on symbolic data to generate label files. In paral-lel, we synthesize 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.