Training state-of-the-art beat tracking models usually requires large amounts of annotated data. It is widely known that data annotation is a time-consuming process and generally involves expert knowledge in the context of MIR. This can be particularly challenging if we consider culture-specific datasets. Previous research has shown that, under certain homogeneity conditions, it is possible to obtain good tracking results with these models using few training datapoints. However, this shifts the problem ...
Training state-of-the-art beat tracking models usually requires large amounts of annotated data. It is widely known that data annotation is a time-consuming process and generally involves expert knowledge in the context of MIR. This can be particularly challenging if we consider culture-specific datasets. Previous research has shown that, under certain homogeneity conditions, it is possible to obtain good tracking results with these models using few training datapoints. However, this shifts the problem to that of the selection of these data. In this paper, we propose a methodology for selectively annotating meaningful samples from a dataset with the objective of training a beat tracker. We extract a rhythmic feature from each track and apply selection methods in the feature space limited by a budget of samples to be annotated. We then train a TCN-based state-of-the-art model using the selected data. The trained model is shown to perform well on the remainder of the dataset when compared to random selection. We hope that our study will alleviate the annotation process of culture-specific datasets and ultimately help build a more culturally diverse perspective in the field of Music Information Retrieval.
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