Paper year
2024
Paper dossier
Selective Annotation of Few Data for Beat Tracking of Latin American Music Using Rhythmic Features
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Citations
2
Authors
4
Topic labels
3
Source readout
Source and corpus status
Venue
Transactions of the International Society for Music Information Retrieval
Source slug
tismir
Corpus placement
Core corpus
Similarity rows
6
Ranking readout
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Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
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Top 50
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Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
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Abstract
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.
Authors
Neighborhood labels
Topics
3 labels
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Music and Audio ProcessingSpeech and Audio ProcessingMusic Technology and Sound Studies
Neighbor surface
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6 total neighborsEmbedding v1-title-abstract-1536-cleantext-r3
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