  Beat and downbeat tracking models have improved significantly in recent years
with the introduction of deep learning methods. However, despite these
improvements, several challenges remain. Particularly, the adaptation of
available models to underrepresented music traditions in MIR is usually
synonymous with collecting and annotating large amounts of data, which is
impractical and time-consuming. Transfer learning, data augmentation, and
fine-tuning techniques have been used quite successfully in related tasks and
are known to alleviate this bottleneck. Furthermore, when studying these music
traditions, models are not required to generalize to multiple mainstream music
genres but to perform well in more constrained, homogeneous conditions. In this
work, we investigate simple yet effective strategies to adapt beat and downbeat
tracking models to two different Latin American music traditions and analyze
the feasibility of these adaptations in real-world applications concerning the
data and computational requirements. Contrary to common belief, our findings
show it is possible to achieve good performance by spending just a few minutes
annotating a portion of the data and training a model in a standard CPU
machine, with the precise amount of resources needed depending on the task and
the complexity of the dataset.
