Skip That Beat: Augmenting Meter Tracking Models for Underrepresented Time Signatures

Giovana V Morais (New York University)*, Brian McFee (New York University), Magdalena Fuentes (New York University)

This paper will be presented both in person and virtually at the 12:15 PM - 12:45 PM PST session

Abstract:

Beat and downbeat tracking models are predominantly developed using datasets with music in 4/4 meter, which decreases their generalization to repertories in other time signatures. In this work, we propose a simple augmentation technique to increase the representation of time signatures beyond 4/4, namely 2/4 and 3/4. Our augmentation procedure works by removing beat intervals from 4/4 annotated tracks. We show that the augmented data helps to improve downbeat tracking for underrepresented meters while preserving the overall performance of beat tracking in two different models. We also show that this technique helps improve downbeat tracking in an unseen Samba dataset.