  In this work, we explore techniques to improve performance for rare classes
in the task of Automatic Chord Recognition (ACR). We first explored the use of
the focal loss in the context of ACR, which was originally proposed to improve
the classification of hard samples. In parallel, we adapted a self-learning
technique originally designed for image recognition to the musical domain. Our
experiments show that both approaches individually (and their combination)
improve the recognition of rare chords, but using only self-learning with noise
addition yields the best results.
