Abstract
Preserving folk tradition is one of the most critical aspects of promoting the preservation of cultural heritage and is a symbol of pride and identification of societies. Numerous research projects have been developed in recent years focussing on preserving and disseminating cultural traditions. Many of them focused on the preservation of traditional music. This research uses artificial intelligence-based techniques to help preserve and categorise traditional Portuguese music. This work proposes a method for automating the tagging process in image or video content to enhance archival workflows, and improve access and exploration of traditional music archives. More specifically, the study focuses on generating a model for detecting traditional Portuguese musical instruments in images. This model is trained with a dataset of our own elabouration and using state-of-the-art detection algorithms. This task is not easy given the problems among the different traditional Portuguese instruments reflected in the article. Nevertheless, the results obtained in the research are outstanding in terms of the good performance obtained and establish a good starting point for the construction of future tools to help preserve Portuguese traditional music.
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Acknowledgments
We would like to express our gratitude for the assistance received by Tiago Pereira and Cristina Enes Garcia from MPAGDP in carrying out the case study of this research. Research partially funded by the project “EA-DIGIFOLK: AN EUROPEAN AND IBERO-AMERICAN APPROACH FOR THE DIGITAL COLLECTION, ANALYSIS AND DISSEMINATION OF FOLK MUSIC” (101086338) under the programme Marie Skłodowska-Curie Actions Staff Exchanges (HORIZON-MSCA-2021-SE-01-01). The Spanish Ministry of Universities has supported Héctor Sánchez San Blas’ research through an FPU pre-doctoral contract under grant FPU20/03014.
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Silva, L.A., Sánchez San Blas, H., Sales Mendes, A., Lozano Murciego, Á., Jiménez-Bravo, D.M. (2025). Automated Detection System for Portuguese Musical Instruments. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds) Progress in Artificial Intelligence. EPIA 2024. Lecture Notes in Computer Science(), vol 14967. Springer, Cham. https://doi.org/10.1007/978-3-031-73497-7_2
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