Abstract
Automatic music genre classification commonly relies on a large amount of well-recorded data for model fitting. These conditions are frequently not met in ethnic music collections due to low media availability and ill recording environments. In this paper, we propose an automatic genre classification technique especially designed for small, noisy datasets. The proposed technique uses handcrafted features and a vote-based aggregation process. Its performance was evaluated over a Brazilian ethnic music dataset, showing that using the proposed technique produces higher F1 measures than using traditional data augmentation methods and state-of-the-art, Deep Learning-based methods. Therefore, our method can be used in automatic classification processes for small datasets, which can be helpful in the organization of ethnic music collections.
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Acknowledgements
The authors thank FAPESP for financial support. The datasets and source code for the proposed method can be downloaded from the author’s website (http://www.dca.fee.unicamp.br/~tavares).
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Tavares, T.F., Foleiss, J.H. (2018). Automatic Music Genre Classification in Small and Ethnic Datasets. In: Aramaki, M., Davies , M., Kronland-Martinet, R., Ystad, S. (eds) Music Technology with Swing. CMMR 2017. Lecture Notes in Computer Science(), vol 11265. Springer, Cham. https://doi.org/10.1007/978-3-030-01692-0_3
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DOI: https://doi.org/10.1007/978-3-030-01692-0_3
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