Selective annotation of few data for beat tracking of Latin American music using rhythmic features

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 dat...

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Detalles Bibliográficos
Autores: Maia, Lucas S., Rocamora, Martín, Biscainho, Luiz W.P., Fuentes, Magdalena
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/72445
Acceso en línea:https://hdl.handle.net/10230/72445
http://dx.doi.org/10.5334/tismir.170
http://hdl.handle.net/10230/72445
Access Level:acceso abierto
Palabra clave:Beat tracking
Selective annotation
Rhythmic description
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repository_id_str
spelling Selective annotation of few data for beat tracking of Latin American music using rhythmic featuresMaia, Lucas S.Rocamora, MartínBiscainho, Luiz W.P.Fuentes, MagdalenaBeat trackingSelective annotationRhythmic descriptionTraining 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.Ubiquity Press2026202620242026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/72445http://dx.doi.org/10.5334/tismir.170http://hdl.handle.net/10230/72445reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésTransactions of the International Society for Music Information Retrieval. 2024;7(1):99-112© 2024 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/724452026-05-29T05:05:01Z
dc.title.none.fl_str_mv Selective annotation of few data for beat tracking of Latin American music using rhythmic features
title Selective annotation of few data for beat tracking of Latin American music using rhythmic features
spellingShingle Selective annotation of few data for beat tracking of Latin American music using rhythmic features
Maia, Lucas S.
Beat tracking
Selective annotation
Rhythmic description
title_short Selective annotation of few data for beat tracking of Latin American music using rhythmic features
title_full Selective annotation of few data for beat tracking of Latin American music using rhythmic features
title_fullStr Selective annotation of few data for beat tracking of Latin American music using rhythmic features
title_full_unstemmed Selective annotation of few data for beat tracking of Latin American music using rhythmic features
title_sort Selective annotation of few data for beat tracking of Latin American music using rhythmic features
dc.creator.none.fl_str_mv Maia, Lucas S.
Rocamora, Martín
Biscainho, Luiz W.P.
Fuentes, Magdalena
author Maia, Lucas S.
author_facet Maia, Lucas S.
Rocamora, Martín
Biscainho, Luiz W.P.
Fuentes, Magdalena
author_role author
author2 Rocamora, Martín
Biscainho, Luiz W.P.
Fuentes, Magdalena
author2_role author
author
author
dc.subject.none.fl_str_mv Beat tracking
Selective annotation
Rhythmic description
topic Beat tracking
Selective annotation
Rhythmic description
description 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.
publishDate 2024
dc.date.none.fl_str_mv 2024
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10230/72445
http://dx.doi.org/10.5334/tismir.170
http://hdl.handle.net/10230/72445
url https://hdl.handle.net/10230/72445
http://dx.doi.org/10.5334/tismir.170
http://hdl.handle.net/10230/72445
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Transactions of the International Society for Music Information Retrieval. 2024;7(1):99-112
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Ubiquity Press
publisher.none.fl_str_mv Ubiquity Press
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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