| Paper ID | MLSP-23.4 |
| Paper Title |
IMPROVING THE CLASSIFICATION OF RARE CHORDS WITH UNLABELED DATA |
| Authors |
Marcelo Bortolozzo, Rodrigo Schramm, Claudio R. Jung, UFRGS, Brazil |
| Session | MLSP-23: Applications in Music and Audio Processing |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-MUSAP] Applications in music and audio processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
